source: doc/papers/concurrency/Paper.tex @ fe9cf9e

arm-ehjacob/cs343-translationnew-astnew-ast-unique-expr
Last change on this file since fe9cf9e was fe9cf9e, checked in by Peter A. Buhr <pabuhr@…>, 18 months ago

fixed a few small wording problems

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1\documentclass[AMA,STIX1COL]{WileyNJD-v2}
2
3\articletype{RESEARCH ARTICLE}%
4
5% Referees
6% Doug Lea, dl@cs.oswego.edu, SUNY Oswego
7% Herb Sutter, hsutter@microsoft.com, Microsoft Corp
8% Gor Nishanov, gorn@microsoft.com, Microsoft Corp
9% James Noble, kjx@ecs.vuw.ac.nz, Victoria University of Wellington, School of Engineering and Computer Science
10
11\received{XXXXX}
12\revised{XXXXX}
13\accepted{XXXXX}
14
15\raggedbottom
16
17%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
18
19% Latex packages used in the document.
20
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258\title{\texorpdfstring{Advanced Control-flow and Concurrency in \protect\CFA}{Advanced Control-flow in Cforall}}
259
260\author[1]{Thierry Delisle}
261\author[1]{Peter A. Buhr*}
262\authormark{DELISLE \textsc{et al.}}
263
264\address[1]{\orgdiv{Cheriton School of Computer Science}, \orgname{University of Waterloo}, \orgaddress{\state{Waterloo, ON}, \country{Canada}}}
265
266\corres{*Peter A. Buhr, Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada. \email{pabuhr{\char`\@}uwaterloo.ca}}
267
268% \fundingInfo{Natural Sciences and Engineering Research Council of Canada}
269
270\abstract[Summary]{
271\CFA is a polymorphic, non-object-oriented, concurrent, backwards compatible extension of the C programming language.
272This paper discusses the design philosophy and implementation of its advanced control-flow and concurrent/parallel features, along with the supporting runtime written in \CFA.
273These features are created from scratch as ISO C has only low-level and/or unimplemented concurrency, so C programmers continue to rely on library approaches like pthreads.
274\CFA introduces modern language-level control-flow mechanisms, like generators, coroutines, user-level threading, and monitors for mutual exclusion and synchronization.
275% Library extension for executors, futures, and actors are built on these basic mechanisms.
276The runtime provides significant programmer simplification and safety by eliminating spurious wakeup and monitor barging.
277The runtime also ensures multiple monitors can be safely acquired in a deadlock-free way, and this feature is fully integrated with all monitor synchronization mechanisms.
278All control-flow features integrate with the \CFA polymorphic type-system and exception handling, while respecting the expectations and style of C programmers.
279Experimental results show comparable performance of the new features with similar mechanisms in other concurrent programming languages.
280}%
281
282\keywords{generator, coroutine, concurrency, parallelism, thread, monitor, runtime, C, \CFA (Cforall)}
283
284
285\begin{document}
286\linenumbers                            % comment out to turn off line numbering
287
288\maketitle
289
290
291\section{Introduction}
292
293\CFA~\cite{Moss18,Cforall} is a modern, polymorphic, non-object-oriented\footnote{
294\CFA has object-oriented features, such as constructors, destructors, virtuals and simple trait/interface inheritance.
295% Go interfaces, Rust traits, Swift Protocols, Haskell Type Classes and Java Interfaces.
296% "Trait inheritance" works for me. "Interface inheritance" might also be a good choice, and distinguish clearly from implementation inheritance.
297% You'll want to be a little bit careful with terms like "structural" and "nominal" inheritance as well. CFA has structural inheritance (I think Go as well) -- it's inferred based on the structure of the code. Java, Rust, and Haskell (not sure about Swift) have nominal inheritance, where there needs to be a specific statement that "this type inherits from this type".
298However, functions \emph{cannot} be nested in structures, so there is no lexical binding between a structure and set of functions implemented by an implicit \lstinline@this@ (receiver) parameter.},
299backwards-compatible extension of the C programming language.
300In many ways, \CFA is to C as Scala~\cite{Scala} is to Java, providing a vehicle for new typing and control-flow capabilities on top of a highly popular programming language\footnote{
301The TIOBE index~\cite{TIOBE} for May 2020 ranks the top five \emph{popular} programming languages as C 17\%, Java 16\%, Python 9\%, \CC 6\%, and \Csharp 4\% = 52\%, and over the past 30 years, C has always ranked either first or second in popularity.}
302allowing immediate dissemination.
303This paper discusses the design philosophy and implementation of \CFA's advanced control-flow and concurrent/parallel features, along with the supporting runtime written in \CFA.
304
305% The call/return extensions retain state between callee and caller versus losing the callee's state on return;
306% the concurrency extensions allow high-level management of threads.
307
308The \CFA control-flow framework extends ISO \Celeven~\cite{C11} with new call/return and concurrent/parallel control-flow.
309Call/return control-flow with argument and parameter passing appeared in the first programming languages.
310Over the past 50 years, call/return has been augmented with features like static and dynamic call, exceptions (multi-level return) and generators/coroutines (see Section~\ref{s:StatefulFunction}).
311While \CFA has mechanisms for dynamic call (algebraic effects~\cite{Zhang19}) and exceptions\footnote{
312\CFA exception handling will be presented in a separate paper.
313The key feature that dovetails with this paper is nonlocal exceptions allowing exceptions to be raised across stacks, with synchronous exceptions raised among coroutines and asynchronous exceptions raised among threads, similar to that in \uC~\cite[\S~5]{uC++}}, this work only discusses retaining state between calls via generators and coroutines.
314\newterm{Coroutining} was introduced by Conway~\cite{Conway63} (1963), discussed by Knuth~\cite[\S~1.4.2]{Knuth73V1}, implemented in Simula67~\cite{Simula67}, formalized by Marlin~\cite{Marlin80}, and is now popular and appears in old and new programming languages: CLU~\cite{CLU}, \Csharp~\cite{Csharp}, Ruby~\cite{Ruby}, Python~\cite{Python}, JavaScript~\cite{JavaScript}, Lua~\cite{Lua}, \CCtwenty~\cite{C++20Coroutine19}.
315Coroutining is sequential execution requiring direct handoff among coroutines, \ie only the programmer is controlling execution order.
316If coroutines transfer to an internal event-engine for scheduling the next coroutines (as in async-await), the program transitions into the realm of concurrency~\cite[\S~3]{Buhr05a}.
317Coroutines are only a stepping stone towards concurrency where the commonality is that coroutines and threads retain state between calls.
318
319\Celeven/\CCeleven define concurrency~\cite[\S~7.26]{C11}, but it is largely wrappers for a subset of the pthreads library~\cite{Pthreads}.\footnote{Pthreads concurrency is based on simple thread fork and join in a function and mutex or condition locks, which is low-level and error-prone}
320Interestingly, almost a decade after the \Celeven standard, the most recent versions of gcc, clang, and msvc do not support the \Celeven include @threads.h@, indicating no interest in the C11 concurrency approach (possibly because of the recent effort to add concurrency to \CC).
321While the \Celeven standard does not state a threading model, the historical association with pthreads suggests implementations would adopt kernel-level threading (1:1)~\cite{ThreadModel}, as for \CC.
322In contrast, there has been a renewed interest during the past decade in user-level (M:N, green) threading in old and new programming languages.
323As multi-core hardware became available in the 1980/90s, both user and kernel threading were examined.
324Kernel threading was chosen, largely because of its simplicity and fit with the simpler operating systems and hardware architectures at the time, which gave it a performance advantage~\cite{Drepper03}.
325Libraries like pthreads were developed for C, and the Solaris operating-system switched from user (JDK 1.1~\cite{JDK1.1}) to kernel threads.
326As a result, many languages adopt the 1:1 kernel-threading model, like Java (Scala), Objective-C~\cite{obj-c-book}, \CCeleven~\cite{C11}, C\#~\cite{Csharp} and Rust~\cite{Rust}, with a variety of presentation mechanisms.
327From 2000 onwards, several language implementations have championed the M:N user-threading model, like Go~\cite{Go}, Erlang~\cite{Erlang}, Haskell~\cite{Haskell}, D~\cite{D}, and \uC~\cite{uC++,uC++book}, including putting green threads back into Java~\cite{Quasar}, and many user-threading libraries have appeared~\cite{Qthreads,MPC,Marcel}.
328The main argument for user-level threading is that it is lighter weight than kernel threading because locking and context switching do not cross the kernel boundary, so there is less restriction on programming styles that encourages large numbers of threads performing medium-sized work to facilitate load balancing by the runtime~\cite{Verch12}.
329As well, user-threading facilitates a simpler concurrency approach using thread objects that leverage sequential patterns versus events with call-backs~\cite{Adya02,vonBehren03}.
330Finally, performant user-threading implementations, both in time and space, meet or exceed direct kernel-threading implementations, while achieving the programming advantages of high concurrency levels and safety.
331
332A further effort over the past two decades is the development of language memory models to deal with the conflict between language features and compiler/hardware optimizations, \eg some language features are unsafe in the presence of aggressive sequential optimizations~\cite{Buhr95a,Boehm05}.
333The consequence is that a language must provide sufficient tools to program around safety issues, as inline and library code is compiled as sequential without any explicit concurrent directive.
334One solution is low-level qualifiers and functions, \eg @volatile@ and atomics, allowing \emph{programmers} to explicitly write safe, race-free~\cite{Boehm12} programs.
335A safer solution is high-level language constructs so the \emph{compiler} knows the concurrency boundaries, \ie where mutual exclusion and synchronization are acquired and released, and provide implicit safety at and across these boundaries.
336While the optimization problem is best known with respect to concurrency, it applies to other complex control-flow, like exceptions and coroutines.
337As well, language solutions allow matching the language paradigm with the approach, \eg matching the functional paradigm with data-flow programming or the imperative paradigm with thread programming.
338
339Finally, it is important for a language to provide safety over performance \emph{as the default}, allowing careful reduction of safety for performance when necessary.
340Two concurrency violations of this philosophy are \emph{spurious} or \emph{random wakeup}~\cite[\S~9]{Buhr05a}, and \emph{barging}\footnote{
341Barging is competitive succession instead of direct handoff, \ie after a lock is released both arriving and preexisting waiter threads compete to acquire the lock.
342Hence, an arriving thread can temporally \emph{barge} ahead of threads already waiting for an event, which can repeat indefinitely leading to starvation of waiter threads.
343} or signals-as-hints~\cite[\S~8]{Buhr05a}, where one is a consequence of the other, \ie once there is spurious wakeup, barging follows.
344(Author experience teaching concurrency is that students are confused by these semantics.)
345However, spurious wakeup is \emph{not} a foundational concurrency property~\cite[\S~9]{Buhr05a};
346it is a performance design choice.
347We argue removing spurious wakeup and signals-as-hints make concurrent programming simpler and safer as there is less local non-determinism to manage.
348If barging acquisition is allowed, its specialized performance advantage should be available as an option not the default.
349
350\CFA embraces language extensions for advanced control-flow, user-level threading, and safety as the default.
351We present comparative examples to support our argument that the \CFA control-flow extensions are as expressive and safe as those in other concurrent imperative programming languages, and perform experiments to show the \CFA runtime is competitive with other similar mechanisms.
352The main contributions of this work are:
353\begin{itemize}[topsep=3pt,itemsep=0pt]
354\item
355a set of fundamental execution properties that dictate which language-level control-flow features need to be supported,
356
357\item
358integration of these language-level control-flow features, while respecting the style and expectations of C programmers,
359
360\item
361monitor synchronization without barging, and the ability to safely acquiring multiple monitors in a deadlock-free way, while seamlessly integrating these capabilities with all monitor synchronization mechanisms,
362
363\item
364providing statically type-safe interfaces that integrate with the \CFA polymorphic type-system and other language features,
365
366% \item
367% library extensions for executors, futures, and actors built on the basic mechanisms.
368
369\item
370a runtime system without spurious wake-up and no performance loss,
371
372\item
373a dynamic partitioning mechanism to segregate groups of executing user and kernel threads performing specialized work, \eg web-server or compute engine, or requiring different scheduling, \eg NUMA or real-time.
374
375% \item
376% a non-blocking I/O library
377
378\item
379experimental results showing comparable performance of the \CFA features with similar mechanisms in other languages.
380\end{itemize}
381
382Section~\ref{s:FundamentalExecutionProperties} presents the compositional hierarchy of execution properties directing the design of control-flow features in \CFA.
383Section~\ref{s:StatefulFunction} begins advanced control by introducing sequential functions that retain data and execution state between calls producing constructs @generator@ and @coroutine@.
384Section~\ref{s:Concurrency} begins concurrency, or how to create (fork) and destroy (join) a thread producing the @thread@ construct.
385Section~\ref{s:MutualExclusionSynchronization} discusses the two mechanisms to restricted nondeterminism when controlling shared access to resources, called mutual exclusion, and timing relationships among threads, called synchronization.
386Section~\ref{s:Monitor} shows how both mutual exclusion and synchronization are safely embedded in the @monitor@ and @thread@ constructs.
387Section~\ref{s:CFARuntimeStructure} describes the large-scale mechanism to structure threads and virtual processors (kernel threads).
388Section~\ref{s:Performance} uses microbenchmarks to compare \CFA threading with pthreads, Java 11.0.6, Go 1.12.6, Rust 1.37.0, Python 3.7.6, Node.js 12.14.1, and \uC 7.0.0.
389
390
391\section{Fundamental Execution Properties}
392\label{s:FundamentalExecutionProperties}
393
394The features in a programming language should be composed from a set of fundamental properties rather than an ad hoc collection chosen by the designers.
395To this end, the control-flow features created for \CFA are based on the fundamental properties of any language with function-stack control-flow (see also \uC~\cite[pp.~140-142]{uC++}).
396The fundamental properties are execution state, thread, and mutual-exclusion/synchronization (MES).
397These independent properties can be used to compose different language features, forming a compositional hierarchy, where the combination of all three is the most advanced feature, called a thread.
398While it is possible for a language to only provide threads for composing programs~\cite{Hermes90}, this unnecessarily complicates and makes inefficient solutions to certain classes of problems.
399As is shown, each of the non-rejected composed language features solves a particular set of problems, and hence, has a defensible position in a programming language.
400If a compositional feature is missing, a programmer has too few fundamental properties resulting in a complex and/or is inefficient solution.
401
402In detail, the fundamental properties are:
403\begin{description}[leftmargin=\parindent,topsep=3pt,parsep=0pt]
404\item[\newterm{execution state}:]
405is the state information needed by a control-flow feature to initialize, manage compute data and execution location(s), and de-initialize, \eg calling a function initializes a stack frame including contained objects with constructors, manages local data in blocks and return locations during calls, and de-initializes the frame by running any object destructors and management operations.
406State is retained in fixed-sized aggregate structures (objects) and dynamic-sized stack(s), often allocated in the heap(s) managed by the runtime system.
407The lifetime of state varies with the control-flow feature, where longer life-time and dynamic size provide greater power but also increase usage complexity and cost.
408Control-flow transfers among execution states in multiple ways, such as function call, context switch, asynchronous await, etc.
409Because the programming language determines what constitutes an execution state, implicitly manages this state, and defines movement mechanisms among states, execution state is an elementary property of the semantics of a programming language.
410% An execution-state is related to the notion of a process continuation \cite{Hieb90}.
411
412\item[\newterm{threading}:]
413is execution of code that occurs independently of other execution, where an individual thread's execution is sequential.
414Multiple threads provide \emph{concurrent execution};
415concurrent execution becomes parallel when run on multiple processing units, \eg hyper-threading, cores, or sockets.
416There must be language mechanisms to create, block and unblock, and join with a thread, even if the mechanism is indirect.
417
418\item[\newterm{MES}:]
419is the concurrency mechanisms to perform an action without interruption and establish timing relationships among multiple threads.
420We contented these two properties are independent, \ie mutual exclusion cannot provide synchronization and vice versa without introducing additional threads~\cite[\S~4]{Buhr05a}.
421Limiting MES, \eg no access to shared data, results in contrived solutions and inefficiency on multi-core von Neumann computers where shared memory is a foundational aspect of its design.
422\end{description}
423These properties are fundamental because they cannot be built from existing language features, \eg a basic programming language like C99~\cite{C99} cannot create new control-flow features, concurrency, or provide MES without atomic hardware mechanisms.
424
425
426\subsection{Structuring Execution Properties}
427
428Programming languages seldom present the fundamental execution properties directly to programmers.
429Instead, the properties are packaged into higher-level constructs that encapsulate details and provide safety to these low-level mechanisms.
430Interestingly, language designers often pick and choose among these execution properties proving a varying subset of constructs.
431
432Table~\ref{t:ExecutionPropertyComposition} shows all combinations of the three fundamental execution properties available to language designers.
433(When doing combination case-analysis, not all combinations are meaningful.)
434The combinations of state, thread, and MES compose a hierarchy of control-flow features all of which have appeared in prior programming languages, where each of these languages have found the feature useful.
435To understand the table, it is important to review the basic von Neumann execution requirement of at least one thread and execution state providing some form of call stack.
436For table entries missing these minimal components, the property is borrowed from the invoker (caller).
437Each entry in the table, numbered \textbf{1}--\textbf{12}, is discussed with respect to how the execution properties combine to generate a high-level language construct.
438
439\begin{table}
440\caption{Execution property composition}
441\centering
442\label{t:ExecutionPropertyComposition}
443\renewcommand{\arraystretch}{1.25}
444%\setlength{\tabcolsep}{5pt}
445\begin{tabular}{c|c||l|l}
446\multicolumn{2}{c||}{execution properties} & \multicolumn{2}{c}{mutual exclusion / synchronization} \\
447\hline
448stateful                        & thread        & \multicolumn{1}{c|}{No} & \multicolumn{1}{c}{Yes} \\
449\hline   
450\hline   
451No                                      & No            & \textbf{1}\ \ \ @struct@                              & \textbf{2}\ \ \ @mutex@ @struct@              \\
452\hline   
453Yes (stackless)         & No            & \textbf{3}\ \ \ @generator@                   & \textbf{4}\ \ \ @mutex@ @generator@   \\
454\hline   
455Yes (stackful)          & No            & \textbf{5}\ \ \ @coroutine@                   & \textbf{6}\ \ \ @mutex@ @coroutine@   \\
456\hline   
457No                                      & Yes           & \textbf{7}\ \ \ {\color{red}rejected} & \textbf{8}\ \ \ {\color{red}rejected} \\
458\hline   
459Yes (stackless)         & Yes           & \textbf{9}\ \ \ {\color{red}rejected} & \textbf{10}\ \ \ {\color{red}rejected} \\
460\hline   
461Yes (stackful)          & Yes           & \textbf{11}\ \ \ @thread@                             & \textbf{12}\ \ @mutex@ @thread@               \\
462\end{tabular}
463\end{table}
464
465Case 1 is a structure where access functions borrow local state (stack frame/activation) and thread from the invoker and retain this state across \emph{callees}, \ie function local-variables are retained on the borrowed stack during calls.
466Structures are a foundational mechanism for data organization, and access functions provide interface abstraction and code sharing in all programming languages.
467Case 2 is case 1 with thread safety to a structure's state where access functions provide serialization (mutual exclusion) and scheduling among calling threads (synchronization).
468A @mutex@ structure, often called a \newterm{monitor}, provides a high-level interface for race-free access of shared data in concurrent programming-languages.
469Case 3 is case 1 where the structure can implicitly retain execution state and access functions use this execution state to resume/suspend across \emph{callers}, but resume/suspend does not retain a function's local state.
470A stackless structure, often called a \newterm{generator} or \emph{iterator}, is \newterm{stackless} because it still borrow the caller's stack and thread, but the stack is used only to preserve state across its callees not callers.
471Generators provide the first step toward directly solving problems like finite-state machines that retain data and execution state between calls, whereas normal functions restart on each call.
472Case 4 is cases 2 and 3 with thread safety during execution of the generator's access functions.
473A @mutex@ generator extends generators into the concurrent domain.
474Cases 5 and 6 are like cases 3 and 4 where the structure is extended with an implicit separate stack, so only the thread is borrowed by access functions.
475A stackful generator, often called a \newterm{coroutine}, is \newterm{stackful} because resume/suspend now context switch to/from the caller's and coroutine's stack.
476A coroutine extends the state retained between calls beyond the generator's structure to arbitrary call depth in the access functions.
477Cases 7 and 8 are rejected because a new thread must have its own stack, where the thread begins and stack frames are stored for calls, \ie it is unrealistic for a thread to borrow a stack.
478Cases 9 and 10 are rejected because a thread needs a growable stack to accept calls, make calls, block, or be preempted, all of which compound to require an unknown amount of execution state.
479If this kind of thread exists, it must execute to completion, \ie computation only, which severely restricts runtime management.
480Cases 11 and 12 are a stackful thread with and without safe access to shared state.
481A thread is the language mechanism to start another thread of control in a program with growable execution state for call/return execution.
482In general, language constructs with more execution properties increase the cost of creation and execution along with complexity of usage.
483
484Given the execution-properties taxonomy, programmers now ask three basic questions: is state necessary across callers and how much, is a separate thread necessary, is thread safety necessary.
485Table~\ref{t:ExecutionPropertyComposition} then suggests the optimal language feature needed for implementing a programming problem.
486The following sections describe how \CFA fills in \emph{all} the non-rejected table entries with language features, while other programming languages may only provide a subset of the table.
487
488
489\subsection{Design Requirements}
490
491The following design requirements largely stem from building \CFA on top of C.
492\begin{itemize}[topsep=3pt,parsep=0pt]
493\item
494All communication must be statically type checkable for early detection of errors and efficient code generation.
495This requirement is consistent with the fact that C is a statically-typed programming-language.
496
497\item
498Direct interaction among language features must be possible allowing any feature to be selected without restricting comm\-unication.
499For example, many concurrent languages do not provide direct communication calls among threads, \ie threads only communicate indirectly through monitors, channels, messages, and/or futures.
500Indirect communication increases the number of objects, consuming more resources, and requires additional synchronization and possibly data transfer.
501
502\item
503All communication is performed using function calls, \ie data is transmitted from argument to parameter and results are returned from function calls.
504Alternative forms of communication, such as call-backs, message passing, channels, or communication ports, step outside of C's normal form of communication.
505
506\item
507All stateful features must follow the same declaration scopes and lifetimes as other language data.
508For C that means at program startup, during block and function activation, and on demand using dynamic allocation.
509
510\item
511MES must be available implicitly in language constructs, \eg Java built-in monitors, as well as explicitly for specialized requirements, \eg @java.util.concurrent@, because requiring programmers to build MES using low-level locks often leads to incorrect programs.
512Furthermore, reducing synchronization scope by encapsulating it within language constructs further reduces errors in concurrent programs.
513
514\item
515Both synchronous and asynchronous communication are needed.
516However, we believe the best way to provide asynchrony, such as call-buffering/chaining and/or returning futures~\cite{multilisp}, is building it from expressive synchronous features.
517
518\item
519Synchronization must be able to control the service order of requests including prioritizing selection from different kinds of outstanding requests, and postponing a request for an unspecified time while continuing to accept new requests.
520Otherwise, certain concurrency problems are difficult, \eg web server, disk scheduling, and the amount of concurrency is inhibited~\cite{Gentleman81}.
521\end{itemize}
522We have satisfied these requirements in \CFA while maintaining backwards compatibility with the huge body of legacy C programs.
523% In contrast, other new programming languages must still access C programs (\eg operating-system service routines), but do so through fragile C interfaces.
524
525
526\subsection{Asynchronous Await / Call}
527
528Asynchronous await/call is a caller mechanism for structuring programs and/or increasing concurrency, where the caller (client) postpones an action into the future, which is subsequently executed by a callee (server).
529The caller detects the action's completion through a \newterm{future} or \newterm{promise}.
530The benefit is asynchronous caller execution with respect to the callee until future resolution.
531For single-threaded languages like JavaScript, an asynchronous call passes a callee action, which is queued in the event-engine, and continues execution with a promise.
532When the caller needs the promise to be fulfilled, it executes @await@.
533A promise-completion call-back can be part of the callee action or the caller is rescheduled;
534in either case, the call back is executed after the promise is fulfilled.
535While asynchronous calls generate new callee (server) events, we contend this mechanism is insufficient for advanced control-flow mechanisms like generators or coroutines, which are discussed next.
536Specifically, control between caller and callee occurs indirectly through the event-engine precluding direct handoff and cycling among events, and requires complex resolution of a control promise and data.
537Note, @async-await@ is just syntactic-sugar over the event engine so it does not solve these deficiencies.
538For multi-threaded languages like Java, the asynchronous call queues a callee action with an executor (server), which subsequently executes the work by a thread in the executor thread-pool.
539The problem is when concurrent work-units need to interact and/or block as this effects the executor by stopping threads.
540While it is possible to extend this approach to support the necessary mechanisms, \eg message passing in Actors, we show monitors and threads provide an equally competitive approach that does not deviate from normal call communication and can be used to build asynchronous call, as is done in Java.
541
542
543\section{Stateful Function}
544\label{s:StatefulFunction}
545
546A \emph{stateful function} has the ability to remember state between calls, where state can be either data or execution, \eg plugin, device driver, finite-state machine (FSM).
547A simple technique to retain data state between calls is @static@ declarations within a function, which is often implemented by hoisting the declarations to the global scope but hiding the names within the function using name mangling.
548However, each call starts the function at the top making it difficult to determine the last point of execution in an algorithm, and requiring multiple flag variables and testing to reestablish the continuation point.
549Hence, the next step of generalizing function state is implicitly remembering the return point between calls and reentering the function at this point rather than the top, called \emph{generators}\,/\,\emph{iterators} or \emph{stackless coroutines}.
550For example, a Fibonacci generator retains data and execution state allowing it to remember prior values needed to generate the next value and the location in the algorithm to compute that value.
551The next step of generalization is instantiating the function to allow multiple named instances, \eg multiple Fibonacci generators, where each instance has its own state, and hence, can generate an independent sequence of values.
552Note, a subset of generator state is a function \emph{closure}, \ie the technique of capturing lexical references when returning a nested function.
553A further generalization is adding a stack to a generator's state, called a \emph{coroutine}, so it can suspend outside of itself, \eg call helper functions to arbitrary depth before suspending back to its resumer without unwinding these calls.
554For example, a coroutine iterator for a binary tree can stop the traversal at the visit point (pre, infix, post traversal), return the node value to the caller, and then continue the recursive traversal from the current node on the next call.
555
556There are two styles of activating a stateful function, \emph{asymmetric} or \emph{symmetric}, identified by resume/suspend (no cycles) and resume/resume (cycles).
557These styles \emph{do not} cause incremental stack growth, \eg a million resume/suspend or resume/resume cycles do not remember each cycle just the last resumer for each cycle.
558Selecting between stackless/stackful semantics and asymmetric/symmetric style is a tradeoff between programming requirements, performance, and design, where stackless is faster and smaller using modified call/return between closures, stackful is more general but slower and larger using context switching between distinct stacks, and asymmetric is simpler control-flow than symmetric.
559Additionally, storage management for the closure/stack must be factored into design and performance, especially in unmanaged languages without garbage collection.
560Note, creation cost (closure/stack) is amortized across usage, so activation cost (resume/suspend) is usually the dominant factor.
561
562% The stateful function is an old idea~\cite{Conway63,Marlin80} that is new again~\cite{C++20Coroutine19}, where execution is temporarily suspended and later resumed, \eg plugin, device driver, finite-state machine.
563% Hence, a stateful function may not end when it returns to its caller, allowing it to be restarted with the data and execution location present at the point of suspension.
564% If the closure is fixed size, we call it a \emph{generator} (or \emph{stackless}), and its control flow is restricted, \eg suspending outside the generator is prohibited.
565% If the closure is variable size, we call it a \emph{coroutine} (or \emph{stackful}), and as the names implies, often implemented with a separate stack with no programming restrictions.
566% Hence, refactoring a stackless coroutine may require changing it to stackful.
567% A foundational property of all \emph{stateful functions} is that resume/suspend \emph{do not} cause incremental stack growth, \ie resume/suspend operations are remembered through the closure not the stack.
568% As well, activating a stateful function is \emph{asymmetric} or \emph{symmetric}, identified by resume/suspend (no cycles) and resume/resume (cycles).
569% A fixed closure activated by modified call/return is faster than a variable closure activated by context switching.
570% Additionally, any storage management for the closure (especially in unmanaged languages, \ie no garbage collection) must also be factored into design and performance.
571% Therefore, selecting between stackless and stackful semantics is a tradeoff between programming requirements and performance, where stackless is faster and stackful is more general.
572% nppNote, creation cost is amortized across usage, so activation cost is usually the dominant factor.
573
574For example, Python presents asymmetric generators as a function object, \uC presents symmetric coroutines as a \lstinline[language=C++]|class|-like object, and many languages present threading using function pointers, @pthreads@~\cite{Butenhof97}, \Csharp~\cite{Csharp}, Go~\cite{Go}, and Scala~\cite{Scala}.
575\begin{center}
576\begin{tabular}{@{}l|l|l@{}}
577\multicolumn{1}{@{}c|}{Python asymmetric generator} & \multicolumn{1}{c|}{\uC symmetric coroutine} & \multicolumn{1}{c@{}}{Pthreads thread} \\
578\hline
579\begin{python}
580`def Gen():` $\LstCommentStyle{\color{red}// function}$
581        ... yield val ...
582gen = Gen()
583for i in range( 10 ):
584        print( next( gen ) )
585\end{python}
586&
587\begin{uC++}
588`_Coroutine Cycle {` $\LstCommentStyle{\color{red}// class}$
589        Cycle * p;
590        void main() { p->cycle(); }
591        void cycle() { resume(); }  `};`
592Cycle c1, c2; c1.p=&c2; c2.p=&c1; c1.cycle();
593\end{uC++}
594&
595\begin{cfa}
596void * `rtn`( void * arg ) { ... }
597int i = 3, rc;
598pthread_t t; $\C{// thread id}$
599$\LstCommentStyle{\color{red}// function pointer}$
600rc=pthread_create(&t, `rtn`, (void *)i);
601\end{cfa}
602\end{tabular}
603\end{center}
604\CFA's preferred presentation model for generators/coroutines/threads is a hybrid of functions and classes, giving an object-oriented flavour.
605Essentially, the generator/coroutine/thread function is semantically coupled with a generator/coroutine/thread custom type via the type's name.
606The custom type solves several issues, while accessing the underlying mechanisms used by the custom types is still allowed for flexibility reasons.
607Each custom type is discussed in detail in the following sections.
608
609
610\subsection{Generator}
611
612Stackless generators (Table~\ref{t:ExecutionPropertyComposition} case 3) have the potential to be very small and fast, \ie as small and fast as function call/return for both creation and execution.
613The \CFA goal is to achieve this performance target, possibly at the cost of some semantic complexity.
614A series of different kinds of generators and their implementation demonstrate how this goal is accomplished.\footnote{
615The \CFA operator syntax uses \lstinline|?| to denote operands, which allows precise definitions for pre, post, and infix operators, \eg \lstinline|?++|, \lstinline|++?|, and \lstinline|?+?|, in addition \lstinline|?\{\}| denotes a constructor, as in \lstinline|foo `f` = `\{`...`\}`|, \lstinline|^?\{\}| denotes a destructor, and \lstinline|?()| is \CC function call \lstinline|operator()|.
616Operator \lstinline+|+ is overloaded for printing, like bit-shift \lstinline|<<| in \CC.
617The \CFA \lstinline|with| clause opens an aggregate scope making its fields directly accessible, like Pascal \lstinline|with|, but using parallel semantics;
618multiple aggregates may be opened.
619\CFA has rebindable references \lstinline|int i, & ip = i, j; `&ip = &j;`| and non-rebindable references \lstinline|int i, & `const` ip = i, j; `&ip = &j;` // disallowed|.
620}%
621
622\begin{figure}
623\centering
624\begin{lrbox}{\myboxA}
625\begin{cfa}[aboveskip=0pt,belowskip=0pt]
626typedef struct {
627        int fn1, fn;
628} Fib;
629#define FibCtor { 1, 0 }
630int fib( Fib * f ) {
631
632
633
634
635
636        int fn = f->fn; f->fn = f->fn1;
637                f->fn1 = f->fn + fn;
638        return fn;
639}
640int main() {
641        Fib f1 = FibCtor, f2 = FibCtor;
642        for ( int i = 0; i < 10; i += 1 )
643                printf( "%d %d\n",
644                           fib( &f1 ), fib( &f2 ) );
645}
646\end{cfa}
647\end{lrbox}
648
649\begin{lrbox}{\myboxB}
650\begin{cfa}[aboveskip=0pt,belowskip=0pt]
651`generator` Fib {
652        int fn1, fn;
653};
654
655void `main(Fib & fib)` with(fib) {
656
657
658        [fn1, fn] = [1, 0];
659        for () {
660                `suspend;`
661                [fn1, fn] = [fn, fn + fn1];
662
663        }
664}
665int main() {
666        Fib f1, f2;
667        for ( 10 )
668                sout | `resume( f1 )`.fn
669                         | `resume( f2 )`.fn;
670}
671\end{cfa}
672\end{lrbox}
673
674\begin{lrbox}{\myboxC}
675\begin{cfa}[aboveskip=0pt,belowskip=0pt]
676typedef struct {
677        int `restart`, fn1, fn;
678} Fib;
679#define FibCtor { `0`, 1, 0 }
680Fib * comain( Fib * f ) {
681        `static void * states[] = {&&s0, &&s1};`
682        `goto *states[f->restart];`
683  s0: f->`restart` = 1;
684        for ( ;; ) {
685                return f;
686          s1:; int fn = f->fn + f->fn1;
687                f->fn1 = f->fn; f->fn = fn;
688        }
689}
690int main() {
691        Fib f1 = FibCtor, f2 = FibCtor;
692        for ( int i = 0; i < 10; i += 1 )
693                printf("%d %d\n",comain(&f1)->fn,
694                                 comain(&f2)->fn);
695}
696\end{cfa}
697\end{lrbox}
698
699\subfloat[C]{\label{f:CFibonacci}\usebox\myboxA}
700\hspace{3pt}
701\vrule
702\hspace{3pt}
703\subfloat[\CFA]{\label{f:CFAFibonacciGen}\usebox\myboxB}
704\hspace{3pt}
705\vrule
706\hspace{3pt}
707\subfloat[C generated code for \CFA version]{\label{f:CFibonacciSim}\usebox\myboxC}
708\caption{Fibonacci output asymmetric generator}
709\label{f:FibonacciAsymmetricGenerator}
710
711\bigskip
712
713\begin{lrbox}{\myboxA}
714\begin{cfa}[aboveskip=0pt,belowskip=0pt]
715`generator Fmt` {
716        char ch;
717        int g, b;
718};
719void ?{}( Fmt & fmt ) { `resume(fmt);` } // constructor
720void ^?{}( Fmt & f ) with(f) { $\C[2.25in]{// destructor}$
721        if ( g != 0 || b != 0 ) sout | nl; }
722void `main( Fmt & f )` with(f) {
723        for () { $\C{// until destructor call}$
724                for ( ; g < 5; g += 1 ) { $\C{// groups}$
725                        for ( ; b < 4; b += 1 ) { $\C{// blocks}$
726                                do { `suspend;` $\C{// wait for character}$
727                                while ( ch == '\n' ); // ignore newline
728                                sout | ch;                      $\C{// print character}$
729                        } sout | " ";  $\C{// block separator}$
730                } sout | nl; $\C{// group separator}$
731        }
732}
733int main() {
734        Fmt fmt; $\C{// fmt constructor called}$
735        for () {
736                sin | fmt.ch; $\C{// read into generator}$
737          if ( eof( sin ) ) break;
738                `resume( fmt );`
739        }
740
741} $\C{// fmt destructor called}\CRT$
742\end{cfa}
743\end{lrbox}
744
745\begin{lrbox}{\myboxB}
746\begin{cfa}[aboveskip=0pt,belowskip=0pt]
747typedef struct {
748        int `restart`, g, b;
749        char ch;
750} Fmt;
751void comain( Fmt * f ) {
752        `static void * states[] = {&&s0, &&s1};`
753        `goto *states[f->restart];`
754  s0: f->`restart` = 1;
755        for ( ;; ) {
756                for ( f->g = 0; f->g < 5; f->g += 1 ) {
757                        for ( f->b = 0; f->b < 4; f->b += 1 ) {
758                                do { return;  s1: ;
759                                } while ( f->ch == '\n' );
760                                printf( "%c", f->ch );
761                        } printf( " " );
762                } printf( "\n" );
763        }
764}
765int main() {
766        Fmt fmt = { `0` };  comain( &fmt ); // prime
767        for ( ;; ) {
768                scanf( "%c", &fmt.ch );
769          if ( feof( stdin ) ) break;
770                comain( &fmt );
771        }
772        if ( fmt.g != 0 || fmt.b != 0 ) printf( "\n" );
773}
774\end{cfa}
775\end{lrbox}
776
777\subfloat[\CFA]{\label{f:CFAFormatGen}\usebox\myboxA}
778\hspace{35pt}
779\vrule
780\hspace{3pt}
781\subfloat[C generated code for \CFA version]{\label{f:CFormatGenImpl}\usebox\myboxB}
782\hspace{3pt}
783\caption{Formatter input asymmetric generator}
784\label{f:FormatterAsymmetricGenerator}
785\end{figure}
786
787Figure~\ref{f:FibonacciAsymmetricGenerator} shows an unbounded asymmetric generator for an infinite sequence of Fibonacci numbers written left to right in C, \CFA, and showing the underlying C implementation for the \CFA version.
788This generator is an \emph{output generator}, producing a new result on each resumption.
789To compute Fibonacci, the previous two values in the sequence are retained to generate the next value, \ie @fn1@ and @fn@, plus the execution location where control restarts when the generator is resumed, \ie top or middle.
790An additional requirement is the ability to create an arbitrary number of generators of any kind, \ie retaining one state in global variables is insufficient;
791hence, state is retained in a closure between calls.
792Figure~\ref{f:CFibonacci} shows the C approach of manually creating the closure in structure @Fib@, and multiple instances of this closure provide multiple Fibonacci generators.
793The C version only has the middle execution state because the top execution state is declaration initialization.
794Figure~\ref{f:CFAFibonacciGen} shows the \CFA approach, which also has a manual closure, but replaces the structure with a custom \CFA @generator@ type.
795Each generator type must have a function named \lstinline|main|,
796% \footnote{
797% The name \lstinline|main| has special meaning in C, specifically the function where a program starts execution.
798% Leveraging starting semantics to this name for generator/coroutine/thread is a logical extension.}
799called a \emph{generator main} (leveraging the starting semantics for program @main@ in C), which is connected to the generator type via its single reference parameter.
800The generator main contains @suspend@ statements that suspend execution without ending the generator versus @return@.
801For the Fibonacci generator-main,
802the top initialization state appears at the start and the middle execution state is denoted by statement @suspend@.
803Any local variables in @main@ \emph{are not retained} between calls;
804hence local variables are only for temporary computations \emph{between} suspends.
805All retained state \emph{must} appear in the generator's type.
806As well, generator code containing a @suspend@ cannot be refactored into a helper function called by the generator, because @suspend@ is implemented via @return@, so a return from the helper function goes back to the current generator not the resumer.
807The generator is started by calling function @resume@ with a generator instance, which begins execution at the top of the generator main, and subsequent @resume@ calls restart the generator at its point of last suspension.
808Resuming an ended (returned) generator is undefined.
809Function @resume@ returns its argument generator so it can be cascaded in an expression, in this case to print the next Fibonacci value @fn@ computed in the generator instance.
810Figure~\ref{f:CFibonacciSim} shows the C implementation of the \CFA asymmetric generator.
811Only one execution-state field, @restart@, is needed to subscript the suspension points in the generator.
812At the start of the generator main, the @static@ declaration, @states@, is initialized to the N suspend points in the generator, where operator @&&@ dereferences or references a label~\cite{gccValueLabels}.
813Next, the computed @goto@ selects the last suspend point and branches to it.
814The  cost of setting @restart@ and branching via the computed @goto@ adds very little cost to the suspend and resume calls.
815
816An advantage of the \CFA explicit generator type is the ability to allow multiple type-safe interface functions taking and returning arbitrary types.
817\begin{cfa}
818int ?()( Fib & fib ) { return `resume( fib )`.fn; } $\C[3.9in]{// function-call interface}$
819int ?()( Fib & fib, int N ) { for ( N - 1 ) `fib()`; return `fib()`; } $\C{// add parameter to skip N values}$
820double ?()( Fib & fib ) { return (int)`fib()` / 3.14159; } $\C{// different return type, cast prevents recursive call}$
821Fib f;  int i;  double d;
822i = f();  i = f( 2 );  d = f();                                         $\C{// alternative interfaces}\CRT$
823\end{cfa}
824Now, the generator can be a separately compiled opaque-type only accessed through its interface functions.
825For contrast, Figure~\ref{f:PythonFibonacci} shows the equivalent Python Fibonacci generator, which does not use a generator type, and hence only has a single interface, but an implicit closure.
826
827\begin{figure}
828%\centering
829\newbox\myboxA
830\begin{lrbox}{\myboxA}
831\begin{python}[aboveskip=0pt,belowskip=0pt]
832def Fib():
833        fn1, fn = 0, 1
834        while True:
835                `yield fn1`
836                fn1, fn = fn, fn1 + fn
837f1 = Fib()
838f2 = Fib()
839for i in range( 10 ):
840        print( next( f1 ), next( f2 ) )
841
842
843
844
845
846
847
848
849
850
851\end{python}
852\end{lrbox}
853
854\newbox\myboxB
855\begin{lrbox}{\myboxB}
856\begin{python}[aboveskip=0pt,belowskip=0pt]
857def Fmt():
858        try:
859                while True:                                             $\C[2.5in]{\# until destructor call}$
860                        for g in range( 5 ):            $\C{\# groups}$
861                                for b in range( 4 ):    $\C{\# blocks}$
862                                        while True:
863                                                ch = (yield)    $\C{\# receive from send}$
864                                                if '\n' not in ch: $\C{\# ignore newline}$
865                                                        break
866                                        print( ch, end='' )     $\C{\# print character}$
867                                print( '  ', end='' )   $\C{\# block separator}$
868                        print()                                         $\C{\# group separator}$
869        except GeneratorExit:                           $\C{\# destructor}$
870                if g != 0 | b != 0:                             $\C{\# special case}$
871                        print()
872fmt = Fmt()
873`next( fmt )`                                                   $\C{\# prime, next prewritten}$
874for i in range( 41 ):
875        `fmt.send( 'a' );`                                      $\C{\# send to yield}$
876\end{python}
877\end{lrbox}
878
879\hspace{30pt}
880\subfloat[Fibonacci]{\label{f:PythonFibonacci}\usebox\myboxA}
881\hspace{3pt}
882\vrule
883\hspace{3pt}
884\subfloat[Formatter]{\label{f:PythonFormatter}\usebox\myboxB}
885\caption{Python generator}
886\label{f:PythonGenerator}
887\end{figure}
888
889Having to manually create the generator closure by moving local-state variables into the generator type is an additional programmer burden (removed by the coroutine in Section~\ref{s:Coroutine}).
890This manual requirement follows from the generality of allowing variable-size local-state, \eg local state with a variable-length array requires dynamic allocation as the array size is unknown at compile time.
891However, dynamic allocation significantly increases the cost of generator creation/destruction and is a showstopper for embedded real-time programming.
892But more importantly, the size of the generator type is tied to the local state in the generator main, which precludes separate compilation of the generator main, \ie a generator must be inlined or local state must be dynamically allocated.
893With respect to safety, we believe static analysis can discriminate persistent generator state from temporary generator-main state and raise a compile-time error for temporary usage spanning suspend points.
894Our experience using generators is that the problems have simple data state, including local state, but complex execution state, so the burden of creating the generator type is small.
895As well, C programmers are not afraid of this kind of semantic programming requirement, if it results in very small, fast generators.
896
897Figure~\ref{f:CFAFormatGen} shows an asymmetric \newterm{input generator}, @Fmt@, for restructuring text into groups of characters of fixed-size blocks, \ie the input on the left is reformatted into the output on the right, where newlines are ignored.
898\begin{center}
899\tt
900\begin{tabular}{@{}l|l@{}}
901\multicolumn{1}{c|}{\textbf{\textrm{input}}} & \multicolumn{1}{c}{\textbf{\textrm{output}}} \\
902\begin{tabular}[t]{@{}ll@{}}
903abcdefghijklmnopqrstuvwxyz \\
904abcdefghijklmnopqrstuvwxyz
905\end{tabular}
906&
907\begin{tabular}[t]{@{}lllll@{}}
908abcd    & efgh  & ijkl  & mnop  & qrst  \\
909uvwx    & yzab  & cdef  & ghij  & klmn  \\
910opqr    & stuv  & wxyz  &               &
911\end{tabular}
912\end{tabular}
913\end{center}
914The example takes advantage of resuming a generator in the constructor to prime the loops so the first character sent for formatting appears inside the nested loops.
915The destructor provides a newline, if formatted text ends with a full line.
916Figure~\ref{f:CFormatGenImpl} shows the C implementation of the \CFA input generator with one additional field and the computed @goto@.
917For contrast, Figure~\ref{f:PythonFormatter} shows the equivalent Python format generator with the same properties as the format generator.
918
919% https://dl-acm-org.proxy.lib.uwaterloo.ca/
920
921Figure~\ref{f:DeviceDriverGen} shows an important application for an asymmetric generator, a device-driver, because device drivers are a significant source of operating-system errors: 85\% in Windows XP~\cite[p.~78]{Swift05} and 51.6\% in Linux~\cite[p.~1358,]{Xiao19}. %\cite{Palix11}
922Swift \etal~\cite[p.~86]{Swift05} restructure device drivers using the Extension Procedure Call (XPC) within the kernel via functions @nooks_driver_call@ and @nooks_kernel_call@, which have coroutine properties context switching to separate stacks with explicit hand-off calls;
923however, the calls do not retain execution state, and hence always start from the top.
924The alternative approach for implementing device drivers is using stack-ripping.
925However, Adya \etal~\cite{Adya02} argue against stack ripping in Section 3.2 and suggest a hybrid approach in Section 4 using cooperatively scheduled \emph{fibers}, which is coroutining.
926
927As an example, the following protocol:
928\begin{center}
929\ldots\, STX \ldots\, message \ldots\, ESC ETX \ldots\, message \ldots\, ETX 2-byte crc \ldots
930\end{center}
931is for a simple network message beginning with the control character STX, ending with an ETX, and followed by a 2-byte cyclic-redundancy check.
932Control characters may appear in a message if preceded by an ESC.
933When a message byte arrives, it triggers an interrupt, and the operating system services the interrupt by calling the device driver with the byte read from a hardware register.
934The device driver returns a status code of its current state, and when a complete message is obtained, the operating system read the message accumulated in the supplied buffer.
935Hence, the device driver is an input/output generator, where the cost of resuming the device-driver generator is the same as call and return, so performance in an operating-system kernel is excellent.
936The key benefits of using a generator are correctness, safety, and maintenance because the execution states are transcribed directly into the programming language rather than table lookup or stack ripping.
937The conclusion is that FSMs are complex and occur in important domains, so direct generator support is important in a system programming language.
938
939\begin{figure}
940\centering
941\begin{tabular}{@{}l|l@{}}
942\begin{cfa}[aboveskip=0pt,belowskip=0pt]
943enum Status { CONT, MSG, ESTX,
944                                ELNTH, ECRC };
945`generator` Driver {
946        Status status;
947        char byte, * msg; // communication
948        int lnth, sum;      // local state
949        short int crc;
950};
951void ?{}( Driver & d, char * m ) { d.msg = m; }
952Status next( Driver & d, char b ) with( d ) {
953        byte = b; `resume( d );` return status;
954}
955void main( Driver & d ) with( d ) {
956        enum { STX = '\002', ESC = '\033',
957                        ETX = '\003', MaxMsg = 64 };
958  msg: for () { // parse message
959                status = CONT;
960                lnth = 0; sum = 0;
961                while ( byte != STX ) `suspend;`
962          emsg: for () {
963                        `suspend;` // process byte
964\end{cfa}
965&
966\begin{cfa}[aboveskip=0pt,belowskip=0pt]
967                        choose ( byte ) { // switch with implicit break
968                          case STX:
969                                status = ESTX; `suspend;` continue msg;
970                          case ETX:
971                                break emsg;
972                          case ESC:
973                                `suspend;`
974                        }
975                        if ( lnth >= MaxMsg ) { // buffer full ?
976                                status = ELNTH; `suspend;` continue msg; }
977                        msg[lnth++] = byte;
978                        sum += byte;
979                }
980                msg[lnth] = '\0'; // terminate string
981                `suspend;`
982                crc = byte << 8;
983                `suspend;`
984                status = (crc | byte) == sum ? MSG : ECRC;
985                `suspend;`
986        }
987}
988\end{cfa}
989\end{tabular}
990\caption{Device-driver generator for communication protocol}
991\label{f:DeviceDriverGen}
992\end{figure}
993
994Figure~\ref{f:CFAPingPongGen} shows a symmetric generator, where the generator resumes another generator, forming a resume/resume cycle.
995(The trivial cycle is a generator resuming itself.)
996This control flow is similar to recursion for functions but without stack growth.
997Figure~\ref{f:PingPongFullCoroutineSteps} shows the steps for symmetric control-flow are creating, executing, and terminating the cycle.
998Constructing the cycle must deal with definition-before-use to close the cycle, \ie, the first generator must know about the last generator, which is not within scope.
999(This issue occurs for any cyclic data structure.)
1000The example creates the generators, @ping@ and @pong@, and then assigns the partners that form the cycle.
1001% (Alternatively, the constructor can assign the partners as they are declared, except the first, and the first-generator partner is set after the last generator declaration to close the cycle.)
1002Once the cycle is formed, the program main resumes one of the generators, @ping@, and the generators can then traverse an arbitrary cycle using @resume@ to activate partner generator(s).
1003Terminating the cycle is accomplished by @suspend@ or @return@, both of which go back to the stack frame that started the cycle (program main in the example).
1004Note, the creator and starter may be different, \eg if the creator calls another function that starts the cycle.
1005The starting stack-frame is below the last active generator because the resume/resume cycle does not grow the stack.
1006Also, since local variables are not retained in the generator function, there are no objects with destructors to be called, so the cost is the same as a function return.
1007Destructor cost occurs when the generator instance is deallocated by the creator.
1008
1009\begin{figure}
1010\centering
1011\begin{lrbox}{\myboxA}
1012\begin{cfa}[aboveskip=0pt,belowskip=0pt]
1013`generator PingPong` {
1014        int N, i;                               // local state
1015        const char * name;
1016        PingPong & partner; // rebindable reference
1017};
1018
1019void `main( PingPong & pp )` with(pp) {
1020
1021
1022        for ( ; i < N; i += 1 ) {
1023                sout | name | i;
1024                `resume( partner );`
1025        }
1026}
1027int main() {
1028        enum { N = 5 };
1029        PingPong ping = {"ping",N,0}, pong = {"pong",N,0};
1030        &ping.partner = &pong;  &pong.partner = &ping;
1031        `resume( ping );`
1032}
1033\end{cfa}
1034\end{lrbox}
1035
1036\begin{lrbox}{\myboxB}
1037\begin{cfa}[escapechar={},aboveskip=0pt,belowskip=0pt]
1038typedef struct PingPong {
1039        int restart, N, i;
1040        const char * name;
1041        struct PingPong * partner;
1042} PingPong;
1043#define PPCtor(name, N) {0, N, 0, name, NULL}
1044void comain( PingPong * pp ) {
1045        static void * states[] = {&&s0, &&s1};
1046        goto *states[pp->restart];
1047  s0: pp->restart = 1;
1048        for ( ; pp->i < pp->N; pp->i += 1 ) {
1049                printf( "%s %d\n", pp->name, pp->i );
1050                asm( "mov  %0,%%rdi" : "=m" (pp->partner) );
1051                asm( "mov  %rdi,%rax" );
1052                asm( "add  $16, %rsp" );
1053                asm( "popq %rbp" );
1054                asm( "jmp  comain" );
1055          s1: ;
1056        }
1057}
1058\end{cfa}
1059\end{lrbox}
1060
1061\subfloat[\CFA symmetric generator]{\label{f:CFAPingPongGen}\usebox\myboxA}
1062\hspace{3pt}
1063\vrule
1064\hspace{3pt}
1065\subfloat[C generator simulation]{\label{f:CPingPongSim}\usebox\myboxB}
1066\hspace{3pt}
1067\caption{Ping-Pong symmetric generator}
1068\label{f:PingPongSymmetricGenerator}
1069\end{figure}
1070
1071\begin{figure}
1072\centering
1073\input{FullCoroutinePhases.pstex_t}
1074\vspace*{-10pt}
1075\caption{Symmetric coroutine steps: Ping / Pong}
1076\label{f:PingPongFullCoroutineSteps}
1077\end{figure}
1078
1079Figure~\ref{f:CPingPongSim} shows the C implementation of the \CFA symmetric generator, where there is still only one additional field, @restart@, but @resume@ is more complex because it does a forward rather than backward jump.
1080Before the jump, the parameter for the next call @partner@ is placed into the register used for the first parameter, @rdi@, and the remaining registers are reset for a return.
1081The @jmp comain@ restarts the function but with a different parameter, so the new call's behaviour depends on the state of the coroutine type, i.e., branch to restart location with different data state.
1082While the semantics of call forward is a tail-call optimization, which compilers perform, the generator state is different on each call rather a common state for a tail-recursive function (i.e., the parameter to the function never changes during the forward calls.
1083However, this assembler code depends on what entry code is generated, specifically if there are local variables and the level of optimization.
1084Hence, internal compiler support is necessary for any forward call or backwards return, \eg LLVM has various coroutine support~\cite{CoroutineTS}, and \CFA can leverage this support should it eventually fork @clang@.
1085For this reason, \CFA does not support general symmetric generators at this time, but, it is possible to hand generate any symmetric generators, as in Figure~\ref{f:CPingPongSim}, for proof of concept and performance testing.
1086
1087Finally, part of this generator work was inspired by the recent \CCtwenty coroutine proposal~\cite{C++20Coroutine19}, which uses the general term coroutine to mean generator.
1088Our work provides the same high-performance asymmetric generators as \CCtwenty, and extends their work with symmetric generators.
1089An additional \CCtwenty generator feature allows @suspend@ and @resume@ to be followed by a restricted compound statement that is executed after the current generator has reset its stack but before calling the next generator, specified with \CFA syntax:
1090\begin{cfa}
1091... suspend`{ ... }`;
1092... resume( C )`{ ... }` ...
1093\end{cfa}
1094Since the current generator's stack is released before calling the compound statement, the compound statement can only reference variables in the generator's type.
1095This feature is useful when a generator is used in a concurrent context to ensure it is stopped before releasing a lock in the compound statement, which might immediately allow another thread to resume the generator.
1096Hence, this mechanism provides a general and safe handoff of the generator among competing threads.
1097
1098
1099\subsection{Coroutine}
1100\label{s:Coroutine}
1101
1102Stackful coroutines (Table~\ref{t:ExecutionPropertyComposition} case 5) extend generator semantics, \ie there is an implicit closure and @suspend@ may appear in a helper function called from the coroutine main.
1103A coroutine is specified by replacing @generator@ with @coroutine@ for the type.
1104Coroutine generality results in higher cost for creation, due to dynamic stack allocation, for execution, due to context switching among stacks, and for terminating, due to possible stack unwinding and dynamic stack deallocation.
1105A series of different kinds of coroutines and their implementations demonstrate how coroutines extend generators.
1106
1107First, the previous generator examples are converted to their coroutine counterparts, allowing local-state variables to be moved from the generator type into the coroutine main.
1108\begin{center}
1109\begin{tabular}{@{}l|l|l|l@{}}
1110\multicolumn{1}{c|}{Fibonacci} & \multicolumn{1}{c|}{Formatter} & \multicolumn{1}{c|}{Device Driver} & \multicolumn{1}{c}{PingPong} \\
1111\hline
1112\begin{cfa}[xleftmargin=0pt]
1113void main( Fib & fib ) ...
1114        `int fn1;`
1115
1116
1117\end{cfa}
1118&
1119\begin{cfa}[xleftmargin=0pt]
1120for ( `g`; 5 ) {
1121        for ( `b`; 4 ) {
1122
1123
1124\end{cfa}
1125&
1126\begin{cfa}[xleftmargin=0pt]
1127status = CONT;
1128`int lnth = 0, sum = 0;`
1129...
1130`short int crc = byte << 8;`
1131\end{cfa}
1132&
1133\begin{cfa}[xleftmargin=0pt]
1134void main( PingPong & pp ) ...
1135        for ( `i`; N ) {
1136
1137
1138\end{cfa}
1139\end{tabular}
1140\end{center}
1141It is also possible to refactor code containing local-state and @suspend@ statements into a helper function, like the computation of the CRC for the device driver.
1142\begin{cfa}
1143int Crc() {
1144        `suspend;`  short int crc = byte << 8;
1145        `suspend;`  status = (crc | byte) == sum ? MSG : ECRC;
1146        return crc;
1147}
1148\end{cfa}
1149A call to this function is placed at the end of the driver's coroutine-main.
1150For complex finite-state machines, refactoring is part of normal program abstraction, especially when code is used in multiple places.
1151Again, this complexity is usually associated with execution state rather than data state.
1152
1153\begin{comment}
1154Figure~\ref{f:Coroutine3States} creates a @coroutine@ type, @`coroutine` Fib { int fn; }@, which provides communication, @fn@, for the \newterm{coroutine main}, @main@, which runs on the coroutine stack, and possibly multiple interface functions, \eg @restart@.
1155Like the structure in Figure~\ref{f:ExternalState}, the coroutine type allows multiple instances, where instances of this type are passed to the overloaded coroutine main.
1156The coroutine main's stack holds the state for the next generation, @f1@ and @f2@, and the code represents the three states in the Fibonacci formula via the three suspend points, to context switch back to the caller's @resume@.
1157The interface function @restart@, takes a Fibonacci instance and context switches to it using @resume@;
1158on restart, the Fibonacci field, @fn@, contains the next value in the sequence, which is returned.
1159The first @resume@ is special because it allocates the coroutine stack and cocalls its coroutine main on that stack;
1160when the coroutine main returns, its stack is deallocated.
1161Hence, @Fib@ is an object at creation, transitions to a coroutine on its first resume, and transitions back to an object when the coroutine main finishes.
1162Figure~\ref{f:Coroutine1State} shows the coroutine version of the C version in Figure~\ref{f:ExternalState}.
1163Coroutine generators are called \newterm{output coroutines} because values are only returned.
1164
1165\begin{figure}
1166\centering
1167\newbox\myboxA
1168% \begin{lrbox}{\myboxA}
1169% \begin{cfa}[aboveskip=0pt,belowskip=0pt]
1170% `int fn1, fn2, state = 1;`   // single global variables
1171% int fib() {
1172%       int fn;
1173%       `switch ( state )` {  // explicit execution state
1174%         case 1: fn = 0;  fn1 = fn;  state = 2;  break;
1175%         case 2: fn = 1;  fn2 = fn1;  fn1 = fn;  state = 3;  break;
1176%         case 3: fn = fn1 + fn2;  fn2 = fn1;  fn1 = fn;  break;
1177%       }
1178%       return fn;
1179% }
1180% int main() {
1181%
1182%       for ( int i = 0; i < 10; i += 1 ) {
1183%               printf( "%d\n", fib() );
1184%       }
1185% }
1186% \end{cfa}
1187% \end{lrbox}
1188\begin{lrbox}{\myboxA}
1189\begin{cfa}[aboveskip=0pt,belowskip=0pt]
1190#define FibCtor { 0, 1 }
1191typedef struct { int fn1, fn; } Fib;
1192int fib( Fib * f ) {
1193
1194        int ret = f->fn1;
1195        f->fn1 = f->fn;
1196        f->fn = ret + f->fn;
1197        return ret;
1198}
1199
1200
1201
1202int main() {
1203        Fib f1 = FibCtor, f2 = FibCtor;
1204        for ( int i = 0; i < 10; i += 1 ) {
1205                printf( "%d %d\n",
1206                                fib( &f1 ), fib( &f2 ) );
1207        }
1208}
1209\end{cfa}
1210\end{lrbox}
1211
1212\newbox\myboxB
1213\begin{lrbox}{\myboxB}
1214\begin{cfa}[aboveskip=0pt,belowskip=0pt]
1215`coroutine` Fib { int fn1; };
1216void main( Fib & fib ) with( fib ) {
1217        int fn;
1218        [fn1, fn] = [0, 1];
1219        for () {
1220                `suspend;`
1221                [fn1, fn] = [fn, fn1 + fn];
1222        }
1223}
1224int ?()( Fib & fib ) with( fib ) {
1225        return `resume( fib )`.fn1;
1226}
1227int main() {
1228        Fib f1, f2;
1229        for ( 10 ) {
1230                sout | f1() | f2();
1231}
1232
1233
1234\end{cfa}
1235\end{lrbox}
1236
1237\newbox\myboxC
1238\begin{lrbox}{\myboxC}
1239\begin{python}[aboveskip=0pt,belowskip=0pt]
1240
1241def Fib():
1242
1243        fn1, fn = 0, 1
1244        while True:
1245                `yield fn1`
1246                fn1, fn = fn, fn1 + fn
1247
1248
1249// next prewritten
1250
1251
1252f1 = Fib()
1253f2 = Fib()
1254for i in range( 10 ):
1255        print( next( f1 ), next( f2 ) )
1256
1257
1258
1259\end{python}
1260\end{lrbox}
1261
1262\subfloat[C]{\label{f:GlobalVariables}\usebox\myboxA}
1263\hspace{3pt}
1264\vrule
1265\hspace{3pt}
1266\subfloat[\CFA]{\label{f:ExternalState}\usebox\myboxB}
1267\hspace{3pt}
1268\vrule
1269\hspace{3pt}
1270\subfloat[Python]{\label{f:ExternalState}\usebox\myboxC}
1271\caption{Fibonacci generator}
1272\label{f:C-fibonacci}
1273\end{figure}
1274
1275\bigskip
1276
1277\newbox\myboxA
1278\begin{lrbox}{\myboxA}
1279\begin{cfa}[aboveskip=0pt,belowskip=0pt]
1280`coroutine` Fib { int fn; };
1281void main( Fib & fib ) with( fib ) {
1282        fn = 0;  int fn1 = fn; `suspend`;
1283        fn = 1;  int fn2 = fn1;  fn1 = fn; `suspend`;
1284        for () {
1285                fn = fn1 + fn2; fn2 = fn1; fn1 = fn; `suspend`; }
1286}
1287int next( Fib & fib ) with( fib ) { `resume( fib );` return fn; }
1288int main() {
1289        Fib f1, f2;
1290        for ( 10 )
1291                sout | next( f1 ) | next( f2 );
1292}
1293\end{cfa}
1294\end{lrbox}
1295\newbox\myboxB
1296\begin{lrbox}{\myboxB}
1297\begin{python}[aboveskip=0pt,belowskip=0pt]
1298
1299def Fibonacci():
1300        fn = 0; fn1 = fn; `yield fn`  # suspend
1301        fn = 1; fn2 = fn1; fn1 = fn; `yield fn`
1302        while True:
1303                fn = fn1 + fn2; fn2 = fn1; fn1 = fn; `yield fn`
1304
1305
1306f1 = Fibonacci()
1307f2 = Fibonacci()
1308for i in range( 10 ):
1309        print( `next( f1 )`, `next( f2 )` ) # resume
1310
1311\end{python}
1312\end{lrbox}
1313\subfloat[\CFA]{\label{f:Coroutine3States}\usebox\myboxA}
1314\qquad
1315\subfloat[Python]{\label{f:Coroutine1State}\usebox\myboxB}
1316\caption{Fibonacci input coroutine, 3 states, internal variables}
1317\label{f:cfa-fibonacci}
1318\end{figure}
1319\end{comment}
1320
1321\begin{figure}
1322\centering
1323\begin{tabular}{@{}l@{\hspace{2\parindentlnth}}l@{}}
1324\begin{cfa}
1325`coroutine` Prod {
1326        Cons & c;                       $\C[1.5in]{// communication}$
1327        int N, money, receipt;
1328};
1329void main( Prod & prod ) with( prod ) {
1330        for ( i; N ) {          $\C{// 1st resume}\CRT$
1331                int p1 = random( 100 ), p2 = random( 100 );
1332                int status = delivery( c, p1, p2 );
1333                receipt += 1;
1334        }
1335        stop( c );
1336}
1337int payment( Prod & prod, int money ) {
1338        prod.money = money;
1339        `resume( prod );`
1340        return prod.receipt;
1341}
1342void start( Prod & prod, int N, Cons &c ) {
1343        &prod.c = &c;
1344        prod.[N, receipt] = [N, 0];
1345        `resume( prod );`
1346}
1347int main() {
1348        Prod prod;
1349        Cons cons = { prod };
1350        start( prod, 5, cons );
1351}
1352\end{cfa}
1353&
1354\begin{cfa}
1355`coroutine` Cons {
1356        Prod & p;                       $\C[1.5in]{// communication}$
1357        int p1, p2, status;
1358        bool done;
1359};
1360void ?{}( Cons & cons, Prod & p ) {
1361        &cons.p = &p;           $\C{// reassignable reference}$
1362        cons.[status, done ] = [0, false];
1363}
1364void main( Cons & cons ) with( cons ) {
1365        int money = 1, receipt; $\C{// 1st resume}\CRT$
1366        for ( ; ! done; ) {
1367                status += 1;
1368                receipt = payment( p, money );
1369                money += 1;
1370        }
1371}
1372int delivery( Cons & cons, int p1, int p2 ) {
1373        cons.[p1, p2] = [p1, p2];
1374        `resume( cons );`
1375        return cons.status;
1376}
1377void stop( Cons & cons ) {
1378        cons.done = true;
1379        `resume( cons );`
1380}
1381
1382\end{cfa}
1383\end{tabular}
1384\caption{Producer / consumer: resume-resume cycle, bidirectional communication}
1385\label{f:ProdCons}
1386\end{figure}
1387
1388Figure~\ref{f:ProdCons} shows the ping-pong example in Figure~\ref{f:CFAPingPongGen} extended into a producer/consumer symmetric-coroutine performing bidirectional communication.
1389This example is illustrative because both producer and consumer have two interface functions with @resume@s that suspend execution in these interface functions.
1390The program main creates the producer coroutine, passes it to the consumer coroutine in its initialization, and closes the cycle at the call to @start@ along with the number of items to be produced.
1391The call to @start@ is the first @resume@ of @prod@, which remembers the program main as the starter and creates @prod@'s stack with a frame for @prod@'s coroutine main at the top, and context switches to it.
1392@prod@'s coroutine main starts, creates local-state variables that are retained between coroutine activations, and executes $N$ iterations, each generating two random values, calling the consumer's @deliver@ function to transfer the values, and printing the status returned from the consumer.
1393The producer call to @delivery@ transfers values into the consumer's communication variables, resumes the consumer, and returns the consumer status.
1394Similarly on the first resume, @cons@'s stack is created and initialized, holding local-state variables retained between subsequent activations of the coroutine.
1395The symmetric coroutine cycle forms when the consumer calls the producer's @payment@ function, which resumes the producer in the consumer's delivery function.
1396When the producer calls @delivery@ again, it resumes the consumer in the @payment@ function.
1397Both interface function than return to the their corresponding coroutine-main functions for the next cycle.
1398Figure~\ref{f:ProdConsRuntimeStacks} shows the runtime stacks of the program main, and the coroutine mains for @prod@ and @cons@ during the cycling.
1399As a consequence of a coroutine retaining its last resumer for suspending back, these reverse pointers allow @suspend@ to cycle \emph{backwards} around a symmetric coroutine cycle.
1400
1401\begin{figure}
1402\begin{center}
1403\input{FullProdConsStack.pstex_t}
1404\end{center}
1405\vspace*{-10pt}
1406\caption{Producer / consumer runtime stacks}
1407\label{f:ProdConsRuntimeStacks}
1408\end{figure}
1409
1410Terminating a coroutine cycle is more complex than a generator cycle, because it requires context switching to the program main's \emph{stack} to shutdown the program, whereas generators started by the program main run on its stack.
1411Furthermore, each deallocated coroutine must execute all destructors for object allocated in the coroutine type \emph{and} allocated on the coroutine's stack at the point of suspension, which can be arbitrarily deep.
1412In the example, termination begins with the producer's loop stopping after N iterations and calling the consumer's @stop@ function, which sets the @done@ flag, resumes the consumer in function @payment@, terminating the call, and the consumer's loop in its coroutine main.
1413% (Not shown is having @prod@ raise a nonlocal @stop@ exception at @cons@ after it finishes generating values and suspend back to @cons@, which catches the @stop@ exception to terminate its loop.)
1414When the consumer's main ends, its stack is already unwound so any stack allocated objects with destructors are finalized.
1415The question now is where does control continue?
1416
1417The na\"{i}ve semantics for coroutine-cycle termination is to context switch to the last resumer, like executing a @suspend@ or @return@ in a generator.
1418However, for coroutines, the last resumer is \emph{not} implicitly below the current stack frame, as for generators, because each coroutine's stack is independent.
1419Unfortunately, it is impossible to determine statically if a coroutine is in a cycle and unrealistic to check dynamically (graph-cycle problem).
1420Hence, a compromise solution is necessary that works for asymmetric (acyclic) and symmetric (cyclic) coroutines.
1421Our solution is to retain a coroutine's starter (first resumer), and context switch back to the starter when the coroutine ends.
1422Hence, the consumer restarts its first resumer, @prod@, in @stop@, and when the producer ends, it restarts its first resumer, program main, in @start@ (see dashed lines from the end of the coroutine mains in Figure~\ref{f:ProdConsRuntimeStacks}).
1423This semantics works well for the most common asymmetric and symmetric coroutine usage patterns.
1424For asymmetric coroutines, it is common for the first resumer (starter) coroutine to be the only resumer;
1425for symmetric coroutines, it is common for the cycle creator to persist for the lifetime of the cycle.
1426For other scenarios, it is always possible to devise a solution with additional programming effort, such as forcing the cycle forward or backward to a safe point before starting termination.
1427
1428Note, the producer/consumer example does not illustrate the full power of the starter semantics because @cons@ always ends first.
1429Assume generator @PingPong@ in Figure~\ref{f:PingPongSymmetricGenerator} is converted to a coroutine.
1430Unlike generators, coroutines have a starter structure with multiple levels, where the program main starts @ping@ and @ping@ starts @pong@.
1431By adjusting $N$ for either @ping@ or @pong@, it is possible to have either finish first.
1432If @pong@ ends first, it resumes its starter @ping@ in its coroutine main, then @ping@ ends and resumes its starter the program main on return;
1433if @ping@ ends first, it resumes its starter the program main on return.
1434Regardless of the cycle complexity, the starter structure always leads back to the program main, but the path can be entered at an arbitrary point.
1435Once back at the program main (creator), coroutines @ping@ and @pong@ are deallocated, runnning any destructors for objects within the coroutine and possibly deallocating any coroutine stacks for non-terminated coroutines, where stack deallocation implies stack unwinding to find destructors for allocated objects on the stack.
1436Hence, the \CFA termination semantics for the generator and coroutine ensure correct deallocation semnatics, regardless of the coroutine's state (terminated or active), like any other aggregate object.
1437
1438
1439\subsection{Generator / Coroutine Implementation}
1440
1441A significant implementation challenge for generators and coroutines (and threads in Section~\ref{s:threads}) is adding extra fields to the custom types and related functions, \eg inserting code after/before the coroutine constructor/destructor and @main@ to create/initialize/de-initialize/destroy any extra fields, \eg the coroutine stack.
1442There are several solutions to these problem, which follow from the object-oriented flavour of adopting custom types.
1443
1444For object-oriented languages, inheritance is used to provide extra fields and code via explicit inheritance:
1445\begin{cfa}[morekeywords={class,inherits}]
1446class myCoroutine inherits baseCoroutine { ... }
1447\end{cfa}
1448% The problem is that the programming language and its tool chain, \eg debugger, @valgrind@, need to understand @baseCoroutine@ because it infers special property, so type @baseCoroutine@ becomes a de facto keyword and all types inheriting from it are implicitly custom types.
1449The problem is that some special properties are not handled by existing language semantics, \eg the execution of constructors and destructors is in the wrong order to implicitly start threads because the thread must start \emph{after} all constructors as it relies on a completely initialized object, but the inherited constructor runs \emph{before} the derived.
1450Alternatives, such as explicitly starting threads as in Java, are repetitive and forgetting to call start is a common source of errors.
1451An alternative is composition:
1452\begin{cfa}
1453struct myCoroutine {
1454        ... // declaration/communication variables
1455        baseCoroutine dummy; // composition, last declaration
1456}
1457\end{cfa}
1458which also requires an explicit declaration that must be last to ensure correct initialization order.
1459However, there is nothing preventing wrong placement or multiple declarations.
1460
1461\CFA custom types make any special properties explicit to the language and its tool chain, \eg the language code-generator knows where to inject code
1462% and when it is unsafe to perform certain optimizations,
1463and IDEs using simple parsing can find and manipulate types with special properties.
1464The downside of this approach is that it makes custom types a special case in the language.
1465Users wanting to extend custom types or build their own can only do so in ways offered by the language.
1466Furthermore, implementing custom types without language support may display the power of a programming language.
1467\CFA blends the two approaches, providing custom type for idiomatic \CFA code, while extending and building new custom types is still possible, similar to Java concurrency with builtin and library (@java.util.concurrent@) monitors.
1468
1469Part of the mechanism to generalize custom types is the \CFA trait~\cite[\S~2.3]{Moss18}, \eg the definition for custom-type @coroutine@ is anything satisfying the trait @is_coroutine@, and this trait both enforces and restricts the coroutine-interface functions.
1470\begin{cfa}
1471trait is_coroutine( `dtype` T ) {
1472        void main( T & );
1473        coroutine_desc * get_coroutine( T & );
1474};
1475forall( `dtype` T | is_coroutine(T) ) void $suspend$( T & ), resume( T & );
1476\end{cfa}
1477Note, copying generators, coroutines, and threads is undefined because muliple objects cannot execute on a shared stack and stack copying does not work in unmanaged languages (no garbage collection), like C, because the stack may contain pointers to objects within it that require updating for the copy.
1478The \CFA @dtype@ property provides no \emph{implicit} copying operations and the @is_coroutine@ trait provides no \emph{explicit} copying operations, so all coroutines must be passed by reference or pointer.
1479The function definitions ensure there is a statically typed @main@ function that is the starting point (first stack frame) of a coroutine, and a mechanism to read the coroutine descriptor from its handle.
1480The @main@ function has no return value or additional parameters because the coroutine type allows an arbitrary number of interface functions with corresponding arbitrary typed input and output values versus fixed ones.
1481The advantage of this approach is that users can easily create different types of coroutines, \eg changing the memory layout of a coroutine is trivial when implementing the @get_coroutine@ function, and possibly redefining \textsf{suspend} and @resume@.
1482
1483The \CFA custom-type @coroutine@ implicitly implements the getter and forward declarations for the coroutine main.
1484\begin{cquote}
1485\begin{tabular}{@{}ccc@{}}
1486\begin{cfa}
1487coroutine MyCor {
1488        int value;
1489
1490};
1491\end{cfa}
1492&
1493{\Large $\Rightarrow$}
1494&
1495\begin{tabular}{@{}ccc@{}}
1496\begin{cfa}
1497struct MyCor {
1498        int value;
1499        coroutine_desc cor;
1500};
1501\end{cfa}
1502&
1503\begin{cfa}
1504static inline coroutine_desc *
1505get_coroutine( MyCor & this ) {
1506        return &this.cor;
1507}
1508\end{cfa}
1509&
1510\begin{cfa}
1511void main( MyCor * this );
1512
1513
1514
1515\end{cfa}
1516\end{tabular}
1517\end{tabular}
1518\end{cquote}
1519The combination of custom types and fundamental @trait@ description of these types allows a concise specification for programmers and tools, while more advanced programmers can have tighter control over memory layout and initialization.
1520
1521Figure~\ref{f:CoroutineMemoryLayout} shows different memory-layout options for a coroutine (where a thread is similar).
1522The coroutine handle is the @coroutine@ instance containing programmer specified type global and communication variables across interface functions.
1523The coroutine descriptor contains all implicit declarations needed by the runtime, \eg @suspend@/@resume@, and can be part of the coroutine handle or separate.
1524The coroutine stack can appear in a number of locations and be fixed or variable sized.
1525Hence, the coroutine's stack could be a variable-length structure (VLS)\footnote{
1526We are examining VLSs, where fields can be variable-sized structures or arrays.
1527Once allocated, a VLS is fixed sized.}
1528on the allocating stack, provided the allocating stack is large enough.
1529For a VLS stack allocation and deallocation is an inexpensive adjustment of the stack pointer, modulo any stack constructor costs to initial frame setup.
1530For stack allocation in the heap, allocation and deallocation is an expensive allocation, where the heap can be a shared resource, modulo any stack constructor costs.
1531It is also possible to use a split or segmented stack calling convention, available with gcc and clang, allowing a variable-sized stack via a set of connected blocks in the heap.
1532Currently, \CFA supports stack and heap allocated descriptors but only fixed-sized heap allocated stacks.
1533In \CFA debug-mode, the fixed-sized stack is terminated with a write-only page, which catches most stack overflows.
1534Experience teaching concurrency with \uC~\cite{CS343} shows fixed-sized stacks are rarely an issue for students.
1535Split-stack allocation is under development but requires recompilation of legacy code, which is not always possible.
1536
1537\begin{figure}
1538\centering
1539\input{corlayout.pstex_t}
1540\caption{Coroutine memory layout}
1541\label{f:CoroutineMemoryLayout}
1542\end{figure}
1543
1544
1545\section{Concurrency}
1546\label{s:Concurrency}
1547
1548Concurrency is nondeterministic scheduling of independent sequential execution paths (threads), where each thread has its own stack.
1549A single thread with multiple stacks, \ie coroutining, does \emph{not} imply concurrency~\cite[\S~3]{Buhr05a}.
1550Coroutining self-schedule the thread across stacks so execution is deterministic.
1551(It is \emph{impossible} to generate a concurrency error when coroutining.)
1552
1553The transition to concurrency, even for a single thread with multiple stacks, occurs when coroutines context switch to a \newterm{scheduling coroutine}, introducing non-determinism from the coroutine perspective~\cite[\S~3]{Buhr05a}.
1554Therefore, a minimal concurrency system requires coroutines \emph{in conjunction with a nondeterministic scheduler}.
1555The resulting execution system now follows a cooperative threading-model~\cite{Adya02,libdill} because context-switching points to the scheduler are known, but the next unblocking point is unknown due to the scheduler.
1556Adding \newterm{preemption} introduces \newterm{non-cooperative} or \newterm{preemptive} scheduling, where context switching points to the scheduler are unknown as they can occur randomly between any two instructions often based on a timer interrupt.
1557Uncertainty gives the illusion of parallelism on a single processor and provides a mechanism to access and increase performance on multiple processors.
1558The reason is that the scheduler and runtime have complete knowledge about resources and how to best utilized them.
1559However, the introduction of unrestricted nondeterminism results in the need for \newterm{mutual exclusion} and \newterm{synchronization}~\cite[\S~4]{Buhr05a}, which restrict nondeterminism for correctness;
1560otherwise, it is impossible to write meaningful concurrent programs.
1561Optimal concurrent performance is often obtained by having as much nondeterminism as mutual exclusion and synchronization correctness allow.
1562
1563A scheduler can also be stackless or stackful.
1564For stackless, the scheduler performs scheduling on the stack of the current coroutine and switches directly to the next coroutine, so there is one context switch.
1565For stackful, the current coroutine switches to the scheduler, which performs scheduling, and it then switches to the next coroutine, so there are two context switches.
1566The \CFA runtime uses a stackful scheduler for uniformity and security.
1567
1568
1569\subsection{Thread}
1570\label{s:threads}
1571
1572Threading (Table~\ref{t:ExecutionPropertyComposition} case 11) needs the ability to start a thread and wait for its completion.
1573A common API for this ability is @fork@ and @join@.
1574\vspace{4pt}
1575\par\noindent
1576\begin{tabular}{@{}l|l|l@{}}
1577\multicolumn{1}{c|}{\textbf{Java}} & \multicolumn{1}{c|}{\textbf{\Celeven}} & \multicolumn{1}{c}{\textbf{pthreads}} \\
1578\hline
1579\begin{cfa}
1580class MyThread extends Thread {...}
1581mythread t = new MyThread(...);
1582`t.start();` // start
1583// concurrency
1584`t.join();` // wait
1585\end{cfa}
1586&
1587\begin{cfa}
1588class MyThread { ... } // functor
1589MyThread mythread;
1590`thread t( mythread, ... );` // start
1591// concurrency
1592`t.join();` // wait
1593\end{cfa}
1594&
1595\begin{cfa}
1596void * rtn( void * arg ) {...}
1597pthread_t t;  int i = 3;
1598`pthread_create( &t, rtn, (void *)i );` // start
1599// concurrency
1600`pthread_join( t, NULL );` // wait
1601\end{cfa}
1602\end{tabular}
1603\vspace{1pt}
1604\par\noindent
1605\CFA has a simpler approach using a custom @thread@ type and leveraging declaration semantics, allocation and deallocation, where threads implicitly @fork@ after construction and @join@ before destruction.
1606\begin{cfa}
1607thread MyThread {};
1608void main( MyThread & this ) { ... }
1609int main() {
1610        MyThread team`[10]`; $\C[2.5in]{// allocate stack-based threads, implicit start after construction}$
1611        // concurrency
1612} $\C{// deallocate stack-based threads, implicit joins before destruction}$
1613\end{cfa}
1614This semantic ensures a thread is started and stopped exactly once, eliminating some programming error, and scales to multiple threads for basic termination synchronization.
1615For block allocation to arbitrary depth, including recursion, threads are created and destroyed in a lattice structure (tree with top and bottom).
1616Arbitrary topologies are possible using dynamic allocation, allowing threads to outlive their declaration scope, identical to normal dynamic allocation.
1617\begin{cfa}
1618MyThread * factory( int N ) { ... return `anew( N )`; } $\C{// allocate heap-based threads, implicit start after construction}$
1619int main() {
1620        MyThread * team = factory( 10 );
1621        // concurrency
1622        `delete( team );` $\C{// deallocate heap-based threads, implicit joins before destruction}\CRT$
1623}
1624\end{cfa}
1625
1626Figure~\ref{s:ConcurrentMatrixSummation} shows concurrently adding the rows of a matrix and then totalling the subtotals sequentially, after all the row threads have terminated.
1627The program uses heap-based threads because each thread needs different constructor values.
1628(Python provides a simple iteration mechanism to initialize array elements to different values allowing stack allocation.)
1629The allocation/deallocation pattern appears unusual because allocated objects are immediately deallocated without any intervening code.
1630However, for threads, the deletion provides implicit synchronization, which is the intervening code.
1631% While the subtotals are added in linear order rather than completion order, which slightly inhibits concurrency, the computation is restricted by the critical-path thread (\ie the thread that takes the longest), and so any inhibited concurrency is very small as totalling the subtotals is trivial.
1632
1633\begin{figure}
1634\begin{cfa}
1635`thread` Adder { int * row, cols, & subtotal; } $\C{// communication variables}$
1636void ?{}( Adder & adder, int row[], int cols, int & subtotal ) {
1637        adder.[ row, cols, &subtotal ] = [ row, cols, &subtotal ];
1638}
1639void main( Adder & adder ) with( adder ) {
1640        subtotal = 0;
1641        for ( c; cols ) { subtotal += row[c]; }
1642}
1643int main() {
1644        const int rows = 10, cols = 1000;
1645        int matrix[rows][cols], subtotals[rows], total = 0;
1646        // read matrix
1647        Adder * adders[rows];
1648        for ( r; rows; ) { $\C{// start threads to sum rows}$
1649                adders[r] = `new( matrix[r], cols, &subtotals[r] );`
1650        }
1651        for ( r; rows ) { $\C{// wait for threads to finish}$
1652                `delete( adders[r] );` $\C{// termination join}$
1653                total += subtotals[r]; $\C{// total subtotal}$
1654        }
1655        sout | total;
1656}
1657\end{cfa}
1658\caption{Concurrent matrix summation}
1659\label{s:ConcurrentMatrixSummation}
1660\end{figure}
1661
1662
1663\subsection{Thread Implementation}
1664
1665Threads in \CFA are user level run by runtime kernel threads (see Section~\ref{s:CFARuntimeStructure}), where user threads provide concurrency and kernel threads provide parallelism.
1666Like coroutines, and for the same design reasons, \CFA provides a custom @thread@ type and a @trait@ to enforce and restrict the thread-interface functions.
1667\begin{cquote}
1668\begin{tabular}{@{}c@{\hspace{3\parindentlnth}}c@{}}
1669\begin{cfa}
1670thread myThread {
1671        ... // declaration/communication variables
1672};
1673
1674
1675\end{cfa}
1676&
1677\begin{cfa}
1678trait is_thread( `dtype` T ) {
1679        void main( T & );
1680        thread_desc * get_thread( T & );
1681        void ^?{}( T & `mutex` );
1682};
1683\end{cfa}
1684\end{tabular}
1685\end{cquote}
1686Like coroutines, the @dtype@ property prevents \emph{implicit} copy operations and the @is_thread@ trait provides no \emph{explicit} copy operations, so threads must be passed by reference or pointer.
1687Similarly, the function definitions ensure there is a statically typed @main@ function that is the thread starting point (first stack frame), a mechanism to read the thread descriptor from its handle, and a special destructor to prevent deallocation while the thread is executing.
1688(The qualifier @mutex@ for the destructor parameter is discussed in Section~\ref{s:Monitor}.)
1689The difference between the coroutine and thread is that a coroutine borrows a thread from its caller, so the first thread resuming a coroutine creates the coroutine's stack and starts running the coroutine main on the stack;
1690whereas, a thread is scheduling for execution in @main@ immediately after its constructor is run.
1691No return value or additional parameters are necessary for this function because the @thread@ type allows an arbitrary number of interface functions with corresponding arbitrary typed input and output values.
1692
1693
1694\section{Mutual Exclusion / Synchronization}
1695\label{s:MutualExclusionSynchronization}
1696
1697Unrestricted nondeterminism is meaningless as there is no way to know when a result is completed and safe to access.
1698To produce meaningful execution requires clawing back some determinism using mutual exclusion and synchronization, where mutual exclusion provides access control for threads using shared data, and synchronization is a timing relationship among threads~\cite[\S~4]{Buhr05a}.
1699The shared data protected by mutual exlusion is called a \newterm{critical section}~\cite{Dijkstra65}, and the protection can be simple, only 1 thread, or complex, only N kinds of threads, \eg group~\cite{Joung00} or readers/writer~\cite{Courtois71} problems.
1700Without synchronization control in a critical section, an arriving thread can barge ahead of preexisting waiter threads resulting in short/long-term starvation, staleness and freshness problems, and incorrect transfer of data.
1701Preventing or detecting barging is a challenge with low-level locks, but made easier through higher-level constructs.
1702This challenge is often split into two different approaches: barging \emph{avoidance} and \emph{prevention}.
1703Approaches that unconditionally releasing a lock for competing threads to acquire must use barging avoidance with flag/counter variable(s) to force barging threads to wait;
1704approaches that conditionally hold locks during synchronization, \eg baton-passing~\cite{Andrews89}, prevent barging completely.
1705
1706At the lowest level, concurrent control is provided by atomic operations, upon which different kinds of locking mechanisms are constructed, \eg spin locks, semaphores~\cite{Dijkstra68b}, barriers, and path expressions~\cite{Campbell74}.
1707However, for productivity it is always desirable to use the highest-level construct that provides the necessary efficiency~\cite{Hochstein05}.
1708A significant challenge with locks is composability because it takes careful organization for multiple locks to be used while preventing deadlock.
1709Easing composability is another feature higher-level mutual-exclusion mechanisms can offer.
1710Some concurrent systems eliminate mutable shared-state by switching to non-shared communication like message passing~\cite{Thoth,Harmony,V-Kernel,MPI} (Erlang, MPI), channels~\cite{CSP} (CSP,Go), actors~\cite{Akka} (Akka, Scala), or functional techniques (Haskell).
1711However, these approaches introduce a new communication mechanism for concurrency different from the standard communication using function call/return.
1712Hence, a programmer must learn and manipulate two sets of design and programming patterns.
1713While this distinction can be hidden away in library code, effective use of the library still has to take both paradigms into account.
1714In contrast, approaches based on shared-state models more closely resemble the standard call and return programming model, resulting in a single programming paradigm.
1715Finally, a newer approach for restricting non-determinism is transactional memory~\cite{Herlihy93}.
1716While this approach is pursued in hardware~\cite{Nakaike15} and system languages, like \CC~\cite{Cpp-Transactions}, the performance and feature set is still too restrictive~\cite{Cascaval08,Boehm09} to be the main concurrency paradigm for system languages.
1717
1718
1719\section{Monitor}
1720\label{s:Monitor}
1721
1722One of the most natural, elegant, efficient, high-level mechanisms for mutual exclusion and synchronization for shared-memory systems is the \emph{monitor} (Table~\ref{t:ExecutionPropertyComposition} case 2).
1723First proposed by Brinch Hansen~\cite{Hansen73} and later described and extended by C.A.R.~Hoare~\cite{Hoare74}, many concurrent programming languages provide monitors as an explicit language construct: \eg Concurrent Pascal~\cite{ConcurrentPascal}, Mesa~\cite{Mesa}, Modula~\cite{Modula-2}, Turing~\cite{Turing:old}, Modula-3~\cite{Modula-3}, NeWS~\cite{NeWS}, Emerald~\cite{Emerald}, \uC~\cite{Buhr92a} and Java~\cite{Java}.
1724In addition, operating-system kernels and device drivers have a monitor-like structure, although they often use lower-level primitives such as mutex locks or semaphores to manually implement a monitor.
1725For these reasons, \CFA selected monitors as the core high-level concurrency construct, upon which higher-level approaches can be easily constructed.
1726
1727Figure~\ref{f:AtomicCounter} compares a \CFA and Java monitor implementing an atomic counter.\footnote{
1728Like other concurrent programming languages, \CFA and Java have performant specializations for the basic types using atomic instructions.}
1729A \newterm{monitor} is a set of functions that ensure mutual exclusion when accessing shared state.
1730(Note, in \CFA, @monitor@ is short-hand for @mutex struct@.)
1731More precisely, a monitor is a programming technique that implicitly binds mutual exclusion to static function scope by call and return, as opposed to locks, where mutual exclusion is defined by acquire/release calls, independent of lexical context (analogous to block and heap storage allocation).
1732Restricting acquire and release points eases programming, comprehension, and maintenance, at a slight cost in flexibility and efficiency.
1733As for other special types, \CFA has a custom @monitor@ type.
1734
1735\begin{figure}
1736\centering
1737
1738\begin{lrbox}{\myboxA}
1739\begin{cfa}[aboveskip=0pt,belowskip=0pt]
1740`monitor` Aint { // atomic integer counter
1741        int cnt;
1742};
1743int ++?( Aint & `mutex` this ) with(this) { return ++cnt; }
1744int ?=?( Aint & `mutex` lhs, int rhs ) with(lhs) { cnt = rhs; }
1745int ?=?(int & lhs, Aint & rhs) with(rhs) { lhs = cnt; }
1746
1747int i = 0, j = 0, k = 5;
1748Aint x = { 0 }, y = { 0 }, z = { 5 }; // no mutex
1749++x; ++y; ++z;     // mutex
1750x = 2; y = i; z = k;  // mutex
1751i = x; j = y; k = z;  // no mutex
1752\end{cfa}
1753\end{lrbox}
1754
1755\begin{lrbox}{\myboxB}
1756\begin{java}[aboveskip=0pt,belowskip=0pt]
1757class Aint {
1758    private int cnt;
1759    public Aint( int init ) { cnt = init; }
1760    `synchronized` public int inc() { return ++cnt; }
1761    `synchronized` public void set( int rhs ) {cnt=rhs;}
1762    public int get() { return cnt; }
1763}
1764int i = 0, j = 0, k = 5;
1765Aint x=new Aint(0), y=new Aint(0), z=new Aint(5);
1766x.inc(); y.inc(); z.inc();
1767x.set( 2 ); y.set( i ); z.set( k );
1768i = x.get(); j = y.get(); k = z.get();
1769\end{java}
1770\end{lrbox}
1771
1772\subfloat[\CFA]{\label{f:AtomicCounterCFA}\usebox\myboxA}
1773\hspace{3pt}
1774\vrule
1775\hspace{3pt}
1776\subfloat[Java]{\label{f:AtomicCounterJava}\usebox\myboxB}
1777\caption{Atomic counter}
1778\label{f:AtomicCounter}
1779\end{figure}
1780
1781Like Java, \CFA monitors have \newterm{multi-acquire} semantics so the thread in the monitor may acquire it multiple times without deadlock, allowing recursion and calling other interface functions.
1782% \begin{cfa}
1783% monitor M { ... } m;
1784% void foo( M & mutex m ) { ... } $\C{// acquire mutual exclusion}$
1785% void bar( M & mutex m ) { $\C{// acquire mutual exclusion}$
1786%       ... `bar( m );` ... `foo( m );` ... $\C{// reacquire mutual exclusion}$
1787% }
1788% \end{cfa}
1789\CFA monitors also ensure the monitor lock is released regardless of how an acquiring function ends, normal or exceptional, and returning a shared variable is safe via copying before the lock is released.
1790Similar safety is offered by \emph{explicit} opt-in disciplines like \CC RAII versus the monitor \emph{implicit} language-enforced safety guarantee ensuring no programmer usage errors.
1791However, RAII mechanisms cannot handle complex synchronization within a monitor, where the monitor lock may not be released on function exit because it is passed to an unblocking thread;
1792RAII is purely a mutual-exclusion mechanism (see Section~\ref{s:Scheduling}).
1793
1794Both Java and \CFA use a keyword @mutex@/\lstinline[language=java]|synchronized| to designate functions that implicitly acquire/release the monitor lock on call/return providing mutual exclusion to the stared data.
1795Non-designated functions provide no mutual exclusion for read-only access or as an interface to a multi-step protocol requiring several steps of acquiring and releasing the monitor.
1796Monitor objects can be passed through multiple helper functions without acquiring mutual exclusion, until a designated function associated with the object is called.
1797\CFA designated functions are marked by an explicitly parameter-only pointer/reference qualifier @mutex@ (discussed further in Section\ref{s:MutexAcquisition}).
1798Whereas, Java designated members are marked with \lstinline[language=java]|synchronized| that applies to the implicit reference parameter @this@.
1799In the example, the increment and setter operations need mutual exclusion while the read-only getter operation can be non-mutex if reading the implementation is atomic.
1800
1801
1802\subsection{Monitor Implementation}
1803
1804For the same design reasons, \CFA provides a custom @monitor@ type and a @trait@ to enforce and restrict the monitor-interface functions.
1805\begin{cquote}
1806\begin{tabular}{@{}c@{\hspace{3\parindentlnth}}c@{}}
1807\begin{cfa}
1808monitor M {
1809        ... // shared data
1810};
1811
1812\end{cfa}
1813&
1814\begin{cfa}
1815trait is_monitor( `dtype` T ) {
1816        monitor_desc * get_monitor( T & );
1817        void ^?{}( T & mutex );
1818};
1819\end{cfa}
1820\end{tabular}
1821\end{cquote}
1822The @dtype@ property prevents \emph{implicit} copy operations and the @is_monitor@ trait provides no \emph{explicit} copy operations, so monitors must be passed by reference or pointer.
1823Similarly, the function definitions ensures there is a mechanism to read the monitor descriptor from its handle, and a special destructor to prevent deallocation if a thread is using the shared data.
1824The custom monitor type also inserts any locks needed to implement the mutual exclusion semantics.
1825\CFA relies heavily on traits as an abstraction mechanism, so the @mutex@ qualifier prevents coincidentally matching of a monitor trait with a type that is not a monitor, similar to coincidental inheritance where a shape and playing card can both be drawable.
1826
1827
1828\subsection{Mutex Acquisition}
1829\label{s:MutexAcquisition}
1830
1831For object-oriented programming languages, the mutex property applies to one object, the implicit pointer/reference to the monitor type.
1832Because \CFA uses a pointer qualifier, other possibilities exist, \eg:
1833\begin{cfa}
1834monitor M { ... };
1835int f1( M & mutex m ); $\C{// single parameter object}$
1836int f2( M * mutex m ); $\C{// single or multiple parameter object}$
1837int f3( M * mutex m[$\,$] ); $\C{// multiple parameter object}$
1838int f4( stack( M * ) & mutex m ); $\C{// multiple parameters object}$
1839\end{cfa}
1840Function @f1@ has a single object parameter, while functions @f2@ to @f4@ can be a single or multi-element parameter with statically unknown size.
1841Because of the statically unknown size, \CFA only supports a single reference @mutex@ parameter, @f1@.
1842
1843The \CFA @mutex@ qualifier does allow the ability to support multi-monitor functions,\footnote{
1844While object-oriented monitors can be extended with a mutex qualifier for multiple-monitor members, no prior example of this feature could be found.}
1845where the number of acquisitions is statically known, called \newterm{bulk acquire}.
1846\CFA guarantees bulk acquisition order is consistent across calls to @mutex@ functions using the same monitors as arguments, so acquiring multiple monitors in a bulk acquire is safe from deadlock.
1847Figure~\ref{f:BankTransfer} shows a trivial solution to the bank transfer problem~\cite{BankTransfer}, where two resources must be locked simultaneously, using \CFA monitors with implicit locking and \CC with explicit locking.
1848A \CFA programmer only has to manage when to acquire mutual exclusion;
1849a \CC programmer must select the correct lock and acquisition mechanism from a panoply of locking options.
1850Making good choices for common cases in \CFA simplifies the programming experience and enhances safety.
1851
1852\begin{figure}
1853\centering
1854\begin{lrbox}{\myboxA}
1855\begin{cfa}[aboveskip=0pt,belowskip=0pt]
1856monitor BankAccount {
1857
1858        int balance;
1859} b1 = { 0 }, b2 = { 0 };
1860void deposit( BankAccount & `mutex` b,
1861                        int deposit ) with(b) {
1862        balance += deposit;
1863}
1864void transfer( BankAccount & `mutex` my,
1865        BankAccount & `mutex` your, int me2you ) {
1866        // bulk acquire
1867        deposit( my, -me2you ); // debit
1868        deposit( your, me2you ); // credit
1869}
1870`thread` Person { BankAccount & b1, & b2; };
1871void main( Person & person ) with(person) {
1872        for ( 10_000_000 ) {
1873                if ( random() % 3 ) deposit( b1, 3 );
1874                if ( random() % 3 ) transfer( b1, b2, 7 );
1875        }
1876}
1877int main() {
1878        `Person p1 = { b1, b2 }, p2 = { b2, b1 };`
1879
1880} // wait for threads to complete
1881\end{cfa}
1882\end{lrbox}
1883
1884\begin{lrbox}{\myboxB}
1885\begin{cfa}[aboveskip=0pt,belowskip=0pt]
1886struct BankAccount {
1887        `recursive_mutex m;`
1888        int balance = 0;
1889} b1, b2;
1890void deposit( BankAccount & b, int deposit ) {
1891        `scoped_lock lock( b.m );`
1892        b.balance += deposit;
1893}
1894void transfer( BankAccount & my,
1895                        BankAccount & your, int me2you ) {
1896        `scoped_lock lock( my.m, your.m );` // bulk acquire
1897        deposit( my, -me2you ); // debit
1898        deposit( your, me2you ); // credit
1899}
1900
1901void person( BankAccount & b1, BankAccount & b2 ) {
1902        for ( int i = 0; i < 10$'$000$'$000; i += 1 ) {
1903                if ( random() % 3 ) deposit( b1, 3 );
1904                if ( random() % 3 ) transfer( b1, b2, 7 );
1905        }
1906}
1907int main() {
1908        `thread p1(person, ref(b1), ref(b2)), p2(person, ref(b2), ref(b1));`
1909        `p1.join(); p2.join();`
1910}
1911\end{cfa}
1912\end{lrbox}
1913
1914\subfloat[\CFA]{\label{f:CFABank}\usebox\myboxA}
1915\hspace{3pt}
1916\vrule
1917\hspace{3pt}
1918\subfloat[\CC]{\label{f:C++Bank}\usebox\myboxB}
1919\hspace{3pt}
1920\caption{Bank transfer problem}
1921\label{f:BankTransfer}
1922\end{figure}
1923
1924Users can still force the acquiring order by using or not using @mutex@.
1925\begin{cfa}
1926void foo( M & mutex m1, M & mutex m2 ); $\C{// acquire m1 and m2}$
1927void bar( M & mutex m1, M & m2 ) { $\C{// only acquire m1}$
1928        ... foo( m1, m2 ); ... $\C{// acquire m2}$
1929}
1930void baz( M & m1, M & mutex m2 ) { $\C{// only acquire m2}$
1931        ... foo( m1, m2 ); ... $\C{// acquire m1}$
1932}
1933\end{cfa}
1934The bulk-acquire semantics allow @bar@ or @baz@ to acquire a monitor lock and reacquire it in @foo@.
1935The calls to @bar@ and @baz@ acquired the monitors in opposite order, possibly resulting in deadlock.
1936However, this case is the simplest instance of the \emph{nested-monitor problem}~\cite{Lister77}, where monitors are acquired in sequence versus bulk.
1937Detecting the nested-monitor problem requires dynamic tracking of monitor calls, and dealing with it requires rollback semantics~\cite{Dice10}.
1938\CFA does not deal with this fundamental problem.
1939
1940Finally, like Java, \CFA offers an alternative @mutex@ statement to reduce refactoring and naming.
1941\begin{cquote}
1942\renewcommand{\arraystretch}{0.0}
1943\begin{tabular}{@{}l@{\hspace{3\parindentlnth}}l@{}}
1944\multicolumn{1}{c}{\textbf{\lstinline@mutex@ call}} & \multicolumn{1}{c}{\lstinline@mutex@ \textbf{statement}} \\
1945\begin{cfa}[aboveskip=0pt,belowskip=0pt]
1946monitor M { ... };
1947void foo( M & mutex m1, M & mutex m2 ) {
1948        // critical section
1949}
1950void bar( M & m1, M & m2 ) {
1951        foo( m1, m2 );
1952}
1953\end{cfa}
1954&
1955\begin{cfa}[aboveskip=0pt,belowskip=0pt]
1956
1957void bar( M & m1, M & m2 ) {
1958        mutex( m1, m2 ) {       // remove refactoring and naming
1959                // critical section
1960        }
1961}
1962
1963\end{cfa}
1964\end{tabular}
1965\end{cquote}
1966
1967
1968\subsection{Scheduling}
1969\label{s:Scheduling}
1970
1971% There are many aspects of scheduling in a concurrency system, all related to resource utilization by waiting threads, \ie which thread gets the resource next.
1972% Different forms of scheduling include access to processors by threads (see Section~\ref{s:RuntimeStructureCluster}), another is access to a shared resource by a lock or monitor.
1973This section discusses scheduling for waiting threads eligible for monitor entry~\cite{Buhr95b}, \ie which user thread gets the shared resource next. (See Section~\ref{s:RuntimeStructureCluster} for scheduling kernel threads on virtual processors.)
1974While monitor mutual-exclusion provides safe access to its shared data, the data may indicate a thread cannot proceed, \eg a bounded buffer may be full/\-empty so produce/consumer threads must block.
1975Leaving the monitor and retrying (busy waiting) is impractical for high-level programming.
1976
1977Monitors eliminate busy waiting by providing synchronization within the monitor critical-section to schedule threads needing access to the shared data, where threads block versus spin.
1978Synchronization is generally achieved with internal~\cite{Hoare74} or external~\cite[\S~2.9.2]{uC++} scheduling.
1979\newterm{Internal} largely schedules threads located \emph{inside} the monitor and is accomplished using condition variables with signal and wait.
1980\newterm{External} largely schedules threads located \emph{outside} the monitor and is accomplished with the @waitfor@ statement.
1981Note, internal scheduling has a small amount of external scheduling and vice versa, so the naming denotes where the majority of the block threads reside (inside or outside) for scheduling.
1982For complex scheduling, the approaches can be combined, so there are threads waiting inside and outside.
1983
1984\CFA monitors do not allow calling threads to barge ahead of signalled threads via barging prevention, which simplifies synchronization among threads in the monitor and increases correctness.
1985A direct consequence of this semantics is that unblocked waiting threads are not required to recheck the waiting condition, \ie waits are not in a starvation-prone busy-loop as required by the signals-as-hints style with barging.
1986Preventing barging comes directly from Hoare's semantics in the seminal paper on monitors~\cite[p.~550]{Hoare74}.
1987% \begin{cquote}
1988% However, we decree that a signal operation be followed immediately by resumption of a waiting program, without possibility of an intervening procedure call from yet a third program.
1989% It is only in this way that a waiting program has an absolute guarantee that it can acquire the resource just released by the signalling program without any danger that a third program will interpose a monitor entry and seize the resource instead.~\cite[p.~550]{Hoare74}
1990% \end{cquote}
1991Furthermore, \CFA concurrency has no spurious wakeup~\cite[\S~9]{Buhr05a}, which eliminates an implicit self barging.
1992
1993Monitor mutual-exclusion means signalling cannot have the signaller and signalled thread in the monitor simultaneously, so only the signaller or signallee can proceed and the other waits on an implicit urgent list~\cite[p.~551]{Hoare74}.
1994Figure~\ref{f:MonitorScheduling} shows internal and external scheduling for the bounded-buffer examples in Figure~\ref{f:GenericBoundedBuffer}.
1995For internal scheduling in Figure~\ref{f:BBInt}, the @signal@ moves the signallee, front thread of the specified condition queue, to the urgent list (see Figure~\ref{f:MonitorScheduling}) and the signaller continues (solid line).
1996Multiple signals move multiple signallees to urgent until the condition queue is empty.
1997When the signaller exits or waits, a thread is implicitly unblocked from urgent, if available, before unblocking a calling thread to prevent barging.
1998(Java conceptually moves the signalled thread to the calling queue, and hence, allows barging.)
1999Signal is used when the signaller is providing the cooperation needed by the signallee, \eg creating an empty slot in a buffer for a producer, and the signaller immediately exits the monitor to run concurrently consuming the buffer element, and passes control of the monitor to the signalled thread, which can immediately take advantage of the state change.
2000Specifically, the @wait@ function atomically blocks the calling thread and implicitly releases the monitor lock(s) for all monitors in the function's parameter list.
2001Signalling is unconditional because signalling an empty condition queue does nothing.
2002It is common to declare condition queues as monitor fields to prevent shared access, hence no locking is required for access as the queues are protected by the monitor lock.
2003In \CFA, a condition queue can be created and stored independently.
2004
2005\begin{figure}
2006\centering
2007% \subfloat[Scheduling Statements] {
2008% \label{fig:SchedulingStatements}
2009% {\resizebox{0.45\textwidth}{!}{\input{CondSigWait.pstex_t}}}
2010\input{CondSigWait.pstex_t}
2011% }% subfloat
2012% \quad
2013% \subfloat[Bulk acquire monitor] {
2014% \label{fig:BulkMonitor}
2015% {\resizebox{0.45\textwidth}{!}{\input{ext_monitor.pstex_t}}}
2016% }% subfloat
2017\caption{Monitor Scheduling}
2018\label{f:MonitorScheduling}
2019\end{figure}
2020
2021\begin{figure}
2022\centering
2023\newbox\myboxA
2024\begin{lrbox}{\myboxA}
2025\begin{cfa}[aboveskip=0pt,belowskip=0pt]
2026forall( otype T ) { // distribute forall
2027        monitor Buffer {
2028                `condition` full, empty;
2029                int front, back, count;
2030                T elements[10];
2031        };
2032        void ?{}( Buffer(T) & buf ) with(buf) {
2033                front = back = count = 0;
2034        }
2035
2036        void insert(Buffer(T) & mutex buf, T elm) with(buf){
2037                if ( count == 10 ) `wait( empty )`; // full ?
2038                // insert elm into buf
2039                `signal( full )`;
2040        }
2041        T remove( Buffer(T) & mutex buf ) with(buf) {
2042                if ( count == 0 ) `wait( full )`; // empty ?
2043                // remove elm from buf
2044                `signal( empty )`;
2045                return elm;
2046        }
2047}
2048\end{cfa}
2049\end{lrbox}
2050
2051\newbox\myboxB
2052\begin{lrbox}{\myboxB}
2053\begin{cfa}[aboveskip=0pt,belowskip=0pt]
2054forall( otype T ) { // distribute forall
2055        monitor Buffer {
2056
2057                int front, back, count;
2058                T elements[10];
2059        };
2060        void ?{}( Buffer(T) & buf ) with(buf) {
2061                front = back = count = 0;
2062        }
2063        T remove( Buffer(T) & mutex buf ); // forward
2064        void insert(Buffer(T) & mutex buf, T elm) with(buf){
2065                if ( count == 10 ) `waitfor( remove : buf )`;
2066                // insert elm into buf
2067
2068        }
2069        T remove( Buffer(T) & mutex buf ) with(buf) {
2070                if ( count == 0 ) `waitfor( insert : buf )`;
2071                // remove elm from buf
2072
2073                return elm;
2074        }
2075}
2076\end{cfa}
2077\end{lrbox}
2078
2079\subfloat[Internal scheduling]{\label{f:BBInt}\usebox\myboxA}
2080\hspace{1pt}
2081\vrule
2082\hspace{3pt}
2083\subfloat[External scheduling]{\label{f:BBExt}\usebox\myboxB}
2084
2085\caption{Generic bounded buffer}
2086\label{f:GenericBoundedBuffer}
2087\end{figure}
2088
2089The @signal_block@ provides the opposite unblocking order, where the signaller is moved to urgent and the signallee continues and a thread is implicitly unblocked from urgent when the signallee exits or waits (dashed line)~\cite[p.~551]{Hoare74}.
2090Signal block is used when the signallee is providing the cooperation needed by the signaller, \eg if the buffer is removed and a producer hands off an item to a consumer as in Figure~\ref{f:DatingSignalBlock}, so the signaller must wait until the signallee unblocks, provides the cooperation, exits the monitor to run concurrently, and passes control of the monitor to the signaller, which can immediately take advantage of the state change.
2091Using @signal@ or @signal_block@ can be a dynamic decision based on whether the thread providing the cooperation arrives before or after the thread needing the cooperation.
2092
2093For external scheduling in Figure~\ref{f:BBExt}, the internal scheduling is replaced, eliminating condition queues and @signal@/@wait@ (cases where it cannot are discussed shortly), and has existed in the programming language Ada for almost 40 years with variants in other languages~\cite{SR,ConcurrentC++,uC++}.
2094While prior languages use external scheduling solely for thread interaction, \CFA generalizes it to both monitors and threads.
2095External scheduling allows waiting for events from other threads while restricting unrelated events, that would otherwise have to wait on condition queues in the monitor.
2096Scheduling is controlled by the @waitfor@ statement, which atomically blocks the calling thread, releases the monitor lock, and restricts the function calls that can next acquire mutual exclusion.
2097Specifically, a thread calling the monitor is unblocked directly from the calling queue based on function names that can fulfill the cooperation required by the signaller.
2098(The linear search through the calling queue to locate a particular call can be reduced to $O(1)$.)
2099Hence, the @waitfor@ has the same semantics as @signal_block@, where the signallee thread from the calling queue executes before the signaller, which waits on urgent.
2100Now when a producer/consumer detects a full/empty buffer, the necessary cooperation for continuation is specified by indicating the next function call that can occur.
2101For example, a producer detecting a full buffer must have cooperation from a consumer to remove an item so function @remove@ is accepted, which prevents producers from entering the monitor, and after a consumer calls @remove@, the producer waiting on urgent is \emph{implicitly} unblocked because it can now continue its insert operation.
2102Hence, this mechanism is done in terms of control flow, next call, versus in terms of data, channels, as in Go and Rust @select@.
2103While both mechanisms have strengths and weaknesses, \CFA uses the control-flow mechanism to be consistent with other language features.
2104
2105Figure~\ref{f:ReadersWriterLock} shows internal and external scheduling for a readers/writer lock with no barging and threads are serviced in FIFO order to eliminate staleness and freshness among the reader/writer threads.
2106For internal scheduling in Figure~\ref{f:RWInt}, the readers and writers wait on the same condition queue in FIFO order, making it impossible to tell if a waiting thread is a reader or writer.
2107To clawback the kind of thread, a \CFA condition can store user data in the node for a blocking thread at the @wait@, \ie whether the thread is a @READER@ or @WRITER@.
2108An unblocked reader thread checks if the thread at the front of the queue is a reader and unblock it, \ie the readers daisy-chain signal the next group of readers demarcated by the next writer or end of the queue.
2109For external scheduling in Figure~\ref{f:RWExt}, a waiting reader checks if a writer is using the resource, and if so, restricts further calls until the writer exits by calling @EndWrite@.
2110The writer does a similar action for each reader or writer using the resource.
2111Note, no new calls to @StartRead@/@StartWrite@ may occur when waiting for the call to @EndRead@/@EndWrite@.
2112
2113\begin{figure}
2114\centering
2115\newbox\myboxA
2116\begin{lrbox}{\myboxA}
2117\begin{cfa}[aboveskip=0pt,belowskip=0pt]
2118enum RW { READER, WRITER };
2119monitor ReadersWriter {
2120        int rcnt, wcnt; // readers/writer using resource
2121        `condition RWers;`
2122};
2123void ?{}( ReadersWriter & rw ) with(rw) {
2124        rcnt = wcnt = 0;
2125}
2126void EndRead( ReadersWriter & mutex rw ) with(rw) {
2127        rcnt -= 1;
2128        if ( rcnt == 0 ) `signal( RWers )`;
2129}
2130void EndWrite( ReadersWriter & mutex rw ) with(rw) {
2131        wcnt = 0;
2132        `signal( RWers );`
2133}
2134void StartRead( ReadersWriter & mutex rw ) with(rw) {
2135        if ( wcnt !=0 || ! empty( RWers ) )
2136                `wait( RWers, READER )`;
2137        rcnt += 1;
2138        if ( ! empty(RWers) && `front(RWers) == READER` )
2139                `signal( RWers )`;  // daisy-chain signalling
2140}
2141void StartWrite( ReadersWriter & mutex rw ) with(rw) {
2142        if ( wcnt != 0 || rcnt != 0 ) `wait( RWers, WRITER )`;
2143
2144        wcnt = 1;
2145}
2146\end{cfa}
2147\end{lrbox}
2148
2149\newbox\myboxB
2150\begin{lrbox}{\myboxB}
2151\begin{cfa}[aboveskip=0pt,belowskip=0pt]
2152
2153monitor ReadersWriter {
2154        int rcnt, wcnt; // readers/writer using resource
2155
2156};
2157void ?{}( ReadersWriter & rw ) with(rw) {
2158        rcnt = wcnt = 0;
2159}
2160void EndRead( ReadersWriter & mutex rw ) with(rw) {
2161        rcnt -= 1;
2162
2163}
2164void EndWrite( ReadersWriter & mutex rw ) with(rw) {
2165        wcnt = 0;
2166
2167}
2168void StartRead( ReadersWriter & mutex rw ) with(rw) {
2169        if ( wcnt > 0 ) `waitfor( EndWrite : rw );`
2170
2171        rcnt += 1;
2172
2173
2174}
2175void StartWrite( ReadersWriter & mutex rw ) with(rw) {
2176        if ( wcnt > 0 ) `waitfor( EndWrite : rw );`
2177        else while ( rcnt > 0 ) `waitfor( EndRead : rw );`
2178        wcnt = 1;
2179}
2180\end{cfa}
2181\end{lrbox}
2182
2183\subfloat[Internal scheduling]{\label{f:RWInt}\usebox\myboxA}
2184\hspace{1pt}
2185\vrule
2186\hspace{3pt}
2187\subfloat[External scheduling]{\label{f:RWExt}\usebox\myboxB}
2188
2189\caption{Readers / writer lock}
2190\label{f:ReadersWriterLock}
2191\end{figure}
2192
2193Finally, external scheduling requires urgent to be a stack, because the signaller expects to execute immediately after the specified monitor call has exited or waited.
2194Internal schedulling performing multiple signalling results in unblocking from urgent in the reverse order from signalling.
2195It is rare for the unblocking order to be important as an unblocked thread can be time-sliced immediately after leaving the monitor.
2196If the unblocking order is important, multiple signalling can be restructured into daisy-chain signalling, where each thread signals the next thread.
2197Hence, \CFA uses a single urgent stack to correctly handle @waitfor@ and adequately support both forms of signalling.
2198(Advanced @waitfor@ features are discussed in Section~\ref{s:ExtendedWaitfor}.)
2199
2200\begin{figure}
2201\centering
2202\newbox\myboxA
2203\begin{lrbox}{\myboxA}
2204\begin{cfa}[aboveskip=0pt,belowskip=0pt]
2205enum { CCodes = 20 };
2206monitor DS {
2207        int GirlPhNo, BoyPhNo;
2208        condition Girls[CCodes], Boys[CCodes];
2209        `condition exchange;`
2210};
2211int girl( DS & mutex ds, int phNo, int ccode ) {
2212        if ( empty( Boys[ccode] ) ) {
2213                wait( Girls[ccode] );
2214                GirlPhNo = phNo;
2215                `signal( exchange );`
2216        } else {
2217                GirlPhNo = phNo;
2218                `signal( Boys[ccode] );`
2219                `wait( exchange );`
2220        }
2221        return BoyPhNo;
2222}
2223int boy( DS & mutex ds, int phNo, int ccode ) {
2224        // as above with boy/girl interchanged
2225}
2226\end{cfa}
2227\end{lrbox}
2228
2229\newbox\myboxB
2230\begin{lrbox}{\myboxB}
2231\begin{cfa}[aboveskip=0pt,belowskip=0pt]
2232
2233monitor DS {
2234        int GirlPhNo, BoyPhNo;
2235        condition Girls[CCodes], Boys[CCodes];
2236
2237};
2238int girl( DS & mutex ds, int phNo, int ccode ) {
2239        if ( empty( Boys[ccode] ) ) { // no compatible
2240                wait( Girls[ccode] ); // wait for boy
2241                GirlPhNo = phNo; // make phone number available
2242
2243        } else {
2244                GirlPhNo = phNo; // make phone number available
2245                `signal_block( Boys[ccode] );` // restart boy
2246
2247        } // if
2248        return BoyPhNo;
2249}
2250int boy( DS & mutex ds, int phNo, int ccode ) {
2251        // as above with boy/girl interchanged
2252}
2253\end{cfa}
2254\end{lrbox}
2255
2256\subfloat[\lstinline@signal@]{\label{f:DatingSignal}\usebox\myboxA}
2257\qquad
2258\subfloat[\lstinline@signal_block@]{\label{f:DatingSignalBlock}\usebox\myboxB}
2259\caption{Dating service Monitor}
2260\label{f:DatingServiceMonitor}
2261\end{figure}
2262
2263Figure~\ref{f:DatingServiceMonitor} shows a dating service demonstrating non-blocking and blocking signalling.
2264The dating service matches girl and boy threads with matching compatibility codes so they can exchange phone numbers.
2265A thread blocks until an appropriate partner arrives.
2266The complexity is exchanging phone numbers in the monitor because of the mutual-exclusion property.
2267For signal scheduling, the @exchange@ condition is necessary to block the thread finding the match, while the matcher unblocks to take the opposite number, post its phone number, and unblock the partner.
2268For signal-block scheduling, the implicit urgent-queue replaces the explicit @exchange@-condition and @signal_block@ puts the finding thread on the urgent stack and unblocks the matcher.
2269Note, barging corrupts the dating service during an exchange because a barger may also match and change the phone numbers, invalidating the previous exchange phone number.
2270This situation shows rechecking the waiting condition and waiting again (signals-as-hints) fails, requiring significant restructured to account for barging.
2271
2272Given external and internal scheduling, what guidelines can a programmer use to select between them?
2273In general, external scheduling is easier to understand and code because only the next logical action (mutex function(s)) is stated, and the monitor implicitly handles all the details.
2274Therefore, there are no condition variables, and hence, no wait and signal, which reduces coding complexity and synchronization errors.
2275If external scheduling is simpler than internal, why not use it all the time?
2276Unfortunately, external scheduling cannot be used if: scheduling depends on parameter value(s) or scheduling must block across an unknown series of calls on a condition variable, \ie internal scheduling.
2277For example, the dating service cannot be written using external scheduling.
2278First, scheduling requires knowledge of calling parameters to make matching decisions and parameters of calling threads are unavailable within the monitor.
2279Specifically, a thread within the monitor cannot examine the @ccode@ of threads waiting on the calling queue to determine if there is a matching partner.
2280(Similarly, if the bounded buffer or readers/writer are restructured with a single interface function with a parameter denoting producer/consumer or reader/write, they cannot be solved with external scheduling.)
2281Second, a scheduling decision may be delayed across an unknown number of calls when there is no immediate match so the thread in the monitor must block on a condition.
2282Specifically, if a thread determines there is no opposite calling thread with the same @ccode@, it must wait an unknown period until a matching thread arrives.
2283For complex synchronization, both external and internal scheduling can be used to take advantage of best of properties of each.
2284
2285Finally, both internal and external scheduling extend to multiple monitors in a natural way.
2286\begin{cquote}
2287\begin{tabular}{@{}l@{\hspace{2\parindentlnth}}l@{}}
2288\begin{cfa}
2289monitor M { `condition e`; ... };
2290void foo( M & mutex m1, M & mutex m2 ) {
2291        ... wait( `e` ); ...   // wait( e, m1, m2 )
2292        ... wait( `e, m1` ); ...
2293        ... wait( `e, m2` ); ...
2294}
2295\end{cfa}
2296&
2297\begin{cfa}
2298void rtn$\(_1\)$( M & mutex m1, M & mutex m2 ); // overload rtn
2299void rtn$\(_2\)$( M & mutex m1 );
2300void bar( M & mutex m1, M & mutex m2 ) {
2301        ... waitfor( `rtn`${\color{red}\(_1\)}$ ); ...       // $\LstCommentStyle{waitfor( rtn\(_1\) : m1, m2 )}$
2302        ... waitfor( `rtn${\color{red}\(_2\)}$ : m1` ); ...
2303}
2304\end{cfa}
2305\end{tabular}
2306\end{cquote}
2307For @wait( e )@, the default semantics is to atomically block the signaller and release all acquired mutex parameters, \ie @wait( e, m1, m2 )@.
2308To override the implicit multi-monitor wait, specific mutex parameter(s) can be specified, \eg @wait( e, m1 )@.
2309Wait cannot statically verify the released monitors are the acquired mutex-parameters without disallowing separately compiled helper functions calling @wait@.
2310While \CC supports bulk locking, @wait@ only accepts a single lock for a condition queue, so bulk locking with condition queues is asymmetric.
2311Finally, a signaller,
2312\begin{cfa}
2313void baz( M & mutex m1, M & mutex m2 ) {
2314        ... signal( e ); ...
2315}
2316\end{cfa}
2317must have acquired at least the same locks as the waiting thread signalled from a condition queue to allow the locks to be passed, and hence, prevent barging.
2318
2319Similarly, for @waitfor( rtn )@, the default semantics is to atomically block the acceptor and release all acquired mutex parameters, \ie @waitfor( rtn : m1, m2 )@.
2320To override the implicit multi-monitor wait, specific mutex parameter(s) can be specified, \eg @waitfor( rtn : m1 )@.
2321@waitfor@ does statically verify the monitor types passed are the same as the acquired mutex-parameters of the given function or function pointer, hence the prototype must be accessible.
2322% When an overloaded function appears in an @waitfor@ statement, calls to any function with that name are accepted.
2323% The rationale is that functions with the same name should perform a similar actions, and therefore, all should be eligible to accept a call.
2324Overloaded functions can be disambiguated using a cast
2325\begin{cfa}
2326void rtn( M & mutex m );
2327`int` rtn( M & mutex m );
2328waitfor( (`int` (*)( M & mutex ))rtn : m );
2329\end{cfa}
2330
2331The ability to release a subset of acquired monitors can result in a \newterm{nested monitor}~\cite{Lister77} deadlock (see Section~\ref{s:MutexAcquisition}).
2332\begin{cfa}
2333void foo( M & mutex m1, M & mutex m2 ) {
2334        ... wait( `e, m1` ); ...                                $\C{// release m1, keeping m2 acquired}$
2335void bar( M & mutex m1, M & mutex m2 ) {        $\C{// must acquire m1 and m2}$
2336        ... signal( `e` ); ...
2337\end{cfa}
2338The @wait@ only releases @m1@ so the signalling thread cannot acquire @m1@ and @m2@ to enter @bar@ and @signal@ the condition.
2339While deadlock can occur with multiple/nesting acquisition, this is a consequence of locks, and by extension monitor locking is not perfectly composable.
2340
2341
2342\subsection{\texorpdfstring{Extended \protect\lstinline@waitfor@}{Extended waitfor}}
2343\label{s:ExtendedWaitfor}
2344
2345Figure~\ref{f:ExtendedWaitfor} shows the extended form of the @waitfor@ statement to conditionally accept one of a group of mutex functions, with an optional statement to be performed \emph{after} the mutex function finishes.
2346For a @waitfor@ clause to be executed, its @when@ must be true and an outstanding call to its corresponding function(s) must exist.
2347The \emph{conditional-expression} of a @when@ may call a function, but the function must not block or context switch.
2348If there are multiple acceptable mutex calls, selection is prioritized top-to-bottom among the @waitfor@ clauses, whereas some programming languages with similar mechanisms accept nondeterministically for this case, \eg Go \lstinline[morekeywords=select]@select@.
2349If some accept guards are true and there are no outstanding calls to these functions, the acceptor is blocked until a call to one of these functions is made.
2350If there is a @timeout@ clause, it provides an upper bound on waiting.
2351If all the accept guards are false, the statement does nothing, unless there is a terminating @else@ clause with a true guard, which is executed instead.
2352Hence, the terminating @else@ clause allows a conditional attempt to accept a call without blocking.
2353If both @timeout@ and @else@ clause are present, the @else@ must be conditional, or the @timeout@ is never triggered.
2354% There is also a traditional future wait queue (not shown) (\eg Microsoft @WaitForMultipleObjects@), to wait for a specified number of future elements in the queue.
2355Finally, there is a shorthand for specifying multiple functions using the same set of monitors: @waitfor( f, g, h : m1, m2, m3 )@.
2356
2357\begin{figure}
2358\centering
2359\begin{cfa}
2360`when` ( $\emph{conditional-expression}$ )      $\C{// optional guard}$
2361        waitfor( $\emph{mutex-function-name}$ ) $\emph{statement}$ $\C{// action after call}$
2362`or` `when` ( $\emph{conditional-expression}$ ) $\C{// any number of functions}$
2363        waitfor( $\emph{mutex-function-name}$ ) $\emph{statement}$
2364`or`    ...
2365`when` ( $\emph{conditional-expression}$ ) $\C{// optional guard}$
2366        `timeout` $\emph{statement}$ $\C{// optional terminating timeout clause}$
2367`when` ( $\emph{conditional-expression}$ ) $\C{// optional guard}$
2368        `else`  $\emph{statement}$ $\C{// optional terminating clause}$
2369\end{cfa}
2370\caption{Extended \protect\lstinline@waitfor@}
2371\label{f:ExtendedWaitfor}
2372\end{figure}
2373
2374Note, a group of conditional @waitfor@ clauses is \emph{not} the same as a group of @if@ statements, \eg:
2375\begin{cfa}
2376if ( C1 ) waitfor( mem1 );                       when ( C1 ) waitfor( mem1 );
2377else if ( C2 ) waitfor( mem2 );         or when ( C2 ) waitfor( mem2 );
2378\end{cfa}
2379The left example only accepts @mem1@ if @C1@ is true or only @mem2@ if @C2@ is true.
2380The right example accepts either @mem1@ or @mem2@ if @C1@ and @C2@ are true.
2381Hence, the @waitfor@ has parallel semantics, accepting any true @when@ clause.
2382
2383An interesting use of @waitfor@ is accepting the @mutex@ destructor to know when an object is deallocated, \eg assume the bounded buffer is restructured from a monitor to a thread with the following @main@.
2384\begin{cfa}
2385void main( Buffer(T) & buffer ) with(buffer) {
2386        for () {
2387                `waitfor( ^?{} : buffer )` break;
2388                or when ( count != 20 ) waitfor( insert : buffer ) { ... }
2389                or when ( count != 0 ) waitfor( remove : buffer ) { ... }
2390        }
2391        // clean up
2392}
2393\end{cfa}
2394When the program main deallocates the buffer, it first calls the buffer's destructor, which is accepted, the destructor runs, and the buffer is deallocated.
2395However, the buffer thread cannot continue after the destructor call because the object is gone;
2396hence, clean up in @main@ cannot occur, which means destructors for local objects are not run.
2397To make this useful capability work, the semantics for accepting the destructor is the same as @signal@, \ie the destructor call is placed on urgent and the acceptor continues execution, which ends the loop, cleans up, and the thread terminates.
2398Then, the destructor caller unblocks from urgent to deallocate the object.
2399Accepting the destructor is the idiomatic way in \CFA to terminate a thread performing direct communication.
2400
2401
2402\subsection{Bulk Barging Prevention}
2403
2404Figure~\ref{f:BulkBargingPrevention} shows \CFA code where bulk acquire adds complexity to the internal-signalling semantics.
2405The complexity begins at the end of the inner @mutex@ statement, where the semantics of internal scheduling need to be extended for multiple monitors.
2406The problem is that bulk acquire is used in the inner @mutex@ statement where one of the monitors is already acquired.
2407When the signalling thread reaches the end of the inner @mutex@ statement, it should transfer ownership of @m1@ and @m2@ to the waiting threads to prevent barging into the outer @mutex@ statement by another thread.
2408However, both the signalling and waiting threads W1 and W2 need some subset of monitors @m1@ and @m2@.
2409\begin{cquote}
2410condition c: (order 1) W2(@m2@), W1(@m1@,@m2@)\ \ \ or\ \ \ (order 2) W1(@m1@,@m2@), W2(@m2@) \\
2411S: acq. @m1@ $\rightarrow$ acq. @m1,m2@ $\rightarrow$ @signal(c)@ $\rightarrow$ rel. @m2@ $\rightarrow$ pass @m2@ unblock W2 (order 2) $\rightarrow$ rel. @m1@ $\rightarrow$ pass @m1,m2@ unblock W1 \\
2412\hspace*{2.75in}$\rightarrow$ rel. @m1@ $\rightarrow$ pass @m1,m2@ unblock W1 (order 1)
2413\end{cquote}
2414
2415\begin{figure}
2416\newbox\myboxA
2417\begin{lrbox}{\myboxA}
2418\begin{cfa}[aboveskip=0pt,belowskip=0pt]
2419monitor M m1, m2;
2420condition c;
2421mutex( m1 ) { // $\LstCommentStyle{\color{red}outer}$
2422        ...
2423        mutex( m1, m2 ) { // $\LstCommentStyle{\color{red}inner}$
2424                ... `signal( c )`; ...
2425                // m1, m2 still acquired
2426        } // $\LstCommentStyle{\color{red}release m2}$
2427        // m1 acquired
2428} // release m1
2429\end{cfa}
2430\end{lrbox}
2431
2432\newbox\myboxB
2433\begin{lrbox}{\myboxB}
2434\begin{cfa}[aboveskip=0pt,belowskip=0pt]
2435
2436
2437mutex( m1 ) {
2438        ...
2439        mutex( m1, m2 ) {
2440                ... `wait( c )`; // release m1, m2
2441                // m1, m2 reacquired
2442        } // $\LstCommentStyle{\color{red}release m2}$
2443        // m1 acquired
2444} // release m1
2445\end{cfa}
2446\end{lrbox}
2447
2448\newbox\myboxC
2449\begin{lrbox}{\myboxC}
2450\begin{cfa}[aboveskip=0pt,belowskip=0pt]
2451
2452
2453mutex( m2 ) {
2454        ... `wait( c )`; // release m2
2455        // m2 reacquired
2456} // $\LstCommentStyle{\color{red}release m2}$
2457
2458
2459
2460
2461\end{cfa}
2462\end{lrbox}
2463
2464\begin{cquote}
2465\subfloat[Signalling Thread (S)]{\label{f:SignallingThread}\usebox\myboxA}
2466\hspace{3\parindentlnth}
2467\subfloat[Waiting Thread (W1)]{\label{f:WaitingThread}\usebox\myboxB}
2468\hspace{2\parindentlnth}
2469\subfloat[Waiting Thread (W2)]{\label{f:OtherWaitingThread}\usebox\myboxC}
2470\end{cquote}
2471\caption{Bulk Barging Prevention}
2472\label{f:BulkBargingPrevention}
2473\end{figure}
2474
2475One scheduling solution is for the signaller S to keep ownership of all locks until the last lock is ready to be transferred, because this semantics fits most closely to the behaviour of single-monitor scheduling.
2476However, this solution is inefficient if W2 waited first and can be immediate passed @m2@ when released, while S retains @m1@ until completion of the outer mutex statement.
2477If W1 waited first, the signaller must retain @m1@ amd @m2@ until completion of the outer mutex statement and then pass both to W1.
2478% Furthermore, there is an execution sequence where the signaller always finds waiter W2, and hence, waiter W1 starves.
2479To support this efficient semantics and prevent barging, the implementation maintains a list of monitors acquired for each blocked thread.
2480When a signaller exits or waits in a mutex function or statement, the front waiter on urgent is unblocked if all its monitors are released.
2481Implementing a fast subset check for the necessary released monitors is important and discussed in the following sections.
2482% The benefit is encapsulating complexity into only two actions: passing monitors to the next owner when they should be released and conditionally waking threads if all conditions are met.
2483
2484
2485\subsection{\texorpdfstring{\protect\lstinline@waitfor@ Implementation}{waitfor Implementation}}
2486\label{s:waitforImplementation}
2487
2488In a statically-typed object-oriented programming language, a class has an exhaustive list of members, even when members are added via static inheritance (see Figure~\ref{f:uCinheritance}).
2489Knowing all members at compilation, even separate compilation, allows uniquely numbered them so the accept-statement implementation can use a fast and compact bit mask with $O(1)$ compare.
2490
2491\begin{figure}
2492\centering
2493\begin{lrbox}{\myboxA}
2494\begin{uC++}[aboveskip=0pt,belowskip=0pt]
2495$\emph{translation unit 1}$
2496_Monitor B { // common type in .h file
2497        _Mutex virtual void `f`( ... );
2498        _Mutex virtual void `g`( ... );
2499        _Mutex virtual void w1( ... ) { ... _Accept(`f`, `g`); ... }
2500};
2501$\emph{translation unit 2}$
2502// include B
2503_Monitor D : public B { // inherit
2504        _Mutex void `h`( ... ); // add
2505        _Mutex void w2( ... ) { ... _Accept(`f`, `h`); ... }
2506};
2507\end{uC++}
2508\end{lrbox}
2509
2510\begin{lrbox}{\myboxB}
2511\begin{cfa}[aboveskip=0pt,belowskip=0pt]
2512$\emph{translation unit 1}$
2513monitor M { ... }; // common type in .h file
2514void `f`( M & mutex m, ... );
2515void `g`( M & mutex m, ... );
2516void w1( M & mutex m, ... ) { ... waitfor(`f`, `g` : m); ... }
2517
2518$\emph{translation unit 2}$
2519// include M
2520extern void `f`( M & mutex m, ... ); // import f but not g
2521void `h`( M & mutex m ); // add
2522void w2( M & mutex m, ... ) { ... waitfor(`f`, `h` : m); ... }
2523
2524\end{cfa}
2525\end{lrbox}
2526
2527\subfloat[\uC]{\label{f:uCinheritance}\usebox\myboxA}
2528\hspace{3pt}
2529\vrule
2530\hspace{3pt}
2531\subfloat[\CFA]{\label{f:CFinheritance}\usebox\myboxB}
2532\caption{Member / Function visibility}
2533\label{f:MemberFunctionVisibility}
2534\end{figure}
2535
2536However, the @waitfor@ statement in translation unit 2 (see Figure~\ref{f:CFinheritance}) cannot see function @g@ in translation unit 1 precluding a unique numbering for a bit-mask because the monitor type only carries the protected shared-data.
2537(A possible way to construct a dense mapping is at link or load-time.)
2538Hence, function pointers are used to identify the functions listed in the @waitfor@ statement, stored in a variable-sized array.
2539Then, the same implementation approach used for the urgent stack (see Section~\ref{s:Scheduling}) is used for the calling queue.
2540Each caller has a list of monitors acquired, and the @waitfor@ statement performs a short linear search matching functions in the @waitfor@ list with called functions, and then verifying the associated mutex locks can be transfers.
2541
2542
2543\subsection{Multi-Monitor Scheduling}
2544\label{s:Multi-MonitorScheduling}
2545
2546External scheduling, like internal scheduling, becomes significantly more complex for multi-monitor semantics.
2547Even in the simplest case, new semantics need to be established.
2548\begin{cfa}
2549monitor M { ... };
2550void f( M & mutex m1 );
2551void g( M & mutex m1, M & mutex m2 ) { `waitfor( f );` } $\C{// pass m1 or m2 to f?}$
2552\end{cfa}
2553The solution is for the programmer to disambiguate:
2554\begin{cfa}
2555waitfor( f : `m2` ); $\C{// wait for call to f with argument m2}$
2556\end{cfa}
2557Both locks are acquired by function @g@, so when function @f@ is called, the lock for monitor @m2@ is passed from @g@ to @f@, while @g@ still holds lock @m1@.
2558This behaviour can be extended to the multi-monitor @waitfor@ statement.
2559\begin{cfa}
2560monitor M { ... };
2561void f( M & mutex m1, M & mutex m2 );
2562void g( M & mutex m1, M & mutex m2 ) { waitfor( f : `m1, m2` ); $\C{// wait for call to f with arguments m1 and m2}$
2563\end{cfa}
2564Again, the set of monitors passed to the @waitfor@ statement must be entirely contained in the set of monitors already acquired by the accepting function.
2565% Also, the order of the monitors in a @waitfor@ statement must match the order of the mutex parameters.
2566
2567Figure~\ref{f:UnmatchedMutexSets} shows internal and external scheduling with multiple monitors that must match exactly with a signalling or accepting thread, \ie partial matching results in waiting.
2568In both cases, the set of monitors is disjoint so unblocking is impossible.
2569
2570\begin{figure}
2571\centering
2572\begin{lrbox}{\myboxA}
2573\begin{cfa}[aboveskip=0pt,belowskip=0pt]
2574monitor M1 {} m11, m12;
2575monitor M2 {} m2;
2576condition c;
2577void f( M1 & mutex m1, M2 & mutex m2 ) {
2578        signal( c );
2579}
2580void g( M1 & mutex m1, M2 & mutex m2 ) {
2581        wait( c );
2582}
2583g( `m11`, m2 ); // block on wait
2584f( `m12`, m2 ); // cannot fulfil
2585\end{cfa}
2586\end{lrbox}
2587
2588\begin{lrbox}{\myboxB}
2589\begin{cfa}[aboveskip=0pt,belowskip=0pt]
2590monitor M1 {} m11, m12;
2591monitor M2 {} m2;
2592
2593void f( M1 & mutex m1, M2 & mutex m2 ) {
2594
2595}
2596void g( M1 & mutex m1, M2 & mutex m2 ) {
2597        waitfor( f : m1, m2 );
2598}
2599g( `m11`, m2 ); // block on accept
2600f( `m12`, m2 ); // cannot fulfil
2601\end{cfa}
2602\end{lrbox}
2603\subfloat[Internal scheduling]{\label{f:InternalScheduling}\usebox\myboxA}
2604\hspace{3pt}
2605\vrule
2606\hspace{3pt}
2607\subfloat[External scheduling]{\label{f:ExternalScheduling}\usebox\myboxB}
2608\caption{Unmatched \protect\lstinline@mutex@ sets}
2609\label{f:UnmatchedMutexSets}
2610\end{figure}
2611
2612\begin{figure}
2613\centering
2614\begin{lrbox}{\myboxA}
2615\begin{cfa}[aboveskip=0pt,belowskip=0pt]
2616
2617struct Msg { int i, j; };
2618mutex thread GoRtn { int i;  float f;  Msg m; };
2619void mem1( GoRtn & mutex gortn, int i ) { gortn.i = i; }
2620void mem2( GoRtn & mutex gortn, float f ) { gortn.f = f; }
2621void mem3( GoRtn & mutex gortn, Msg m ) { gortn.m = m; }
2622void ^?{}( GoRtn & mutex ) {}
2623
2624void main( GoRtn & mutex gortn ) with(gortn) { // thread starts
2625
2626        for () {
2627
2628                `waitfor( mem1 : gortn )` sout | i;  // wait for calls
2629                or `waitfor( mem2 : gortn )` sout | f;
2630                or `waitfor( mem3 : gortn )` sout | m.i | m.j;
2631                or `waitfor( ^?{} : gortn )` break; // low priority
2632
2633        }
2634
2635}
2636int main() {
2637        GoRtn gortn; $\C[2.0in]{// start thread}$
2638        `mem1( gortn, 0 );` $\C{// different calls}\CRT$
2639        `mem2( gortn, 2.5 );`
2640        `mem3( gortn, (Msg){1, 2} );`
2641
2642
2643} // wait for completion
2644\end{cfa}
2645\end{lrbox}
2646
2647\begin{lrbox}{\myboxB}
2648\begin{Go}[aboveskip=0pt,belowskip=0pt]
2649func main() {
2650        type Msg struct{ i, j int }
2651
2652        ch1 := make( chan int )
2653        ch2 := make( chan float32 )
2654        ch3 := make( chan Msg )
2655        hand := make( chan string )
2656        shake := make( chan string )
2657        gortn := func() { $\C[1.5in]{// thread starts}$
2658                var i int;  var f float32;  var m Msg
2659                L: for {
2660                        select { $\C{// wait for messages}$
2661                          case `i = <- ch1`: fmt.Println( i )
2662                          case `f = <- ch2`: fmt.Println( f )
2663                          case `m = <- ch3`: fmt.Println( m )
2664                          case `<- hand`: break L $\C{// sentinel}$
2665                        }
2666                }
2667                `shake <- "SHAKE"` $\C{// completion}$
2668        }
2669
2670        go gortn() $\C{// start thread}$
2671        `ch1 <- 0` $\C{// different messages}$
2672        `ch2 <- 2.5`
2673        `ch3 <- Msg{1, 2}`
2674        `hand <- "HAND"` $\C{// sentinel value}$
2675        `<- shake` $\C{// wait for completion}\CRT$
2676}
2677\end{Go}
2678\end{lrbox}
2679
2680\subfloat[\CFA]{\label{f:CFAwaitfor}\usebox\myboxA}
2681\hspace{3pt}
2682\vrule
2683\hspace{3pt}
2684\subfloat[Go]{\label{f:Gochannel}\usebox\myboxB}
2685\caption{Direct versus indirect communication}
2686\label{f:DirectCommunicationComparison}
2687
2688\medskip
2689
2690\begin{cfa}
2691mutex thread DatingService {
2692        condition Girls[CompCodes], Boys[CompCodes];
2693        int girlPhoneNo, boyPhoneNo, ccode;
2694};
2695int girl( DatingService & mutex ds, int phoneno, int code ) with( ds ) {
2696        girlPhoneNo = phoneno;  ccode = code;
2697        `wait( Girls[ccode] );`                                                         $\C{// wait for boy}$
2698        girlPhoneNo = phoneno;  return boyPhoneNo;
2699}
2700int boy( DatingService & mutex ds, int phoneno, int code ) with( ds ) {
2701        boyPhoneNo = phoneno;  ccode = code;
2702        `wait( Boys[ccode] );`                                                          $\C{// wait for girl}$
2703        boyPhoneNo = phoneno;  return girlPhoneNo;
2704}
2705void main( DatingService & ds ) with( ds ) {                    $\C{// thread starts, ds defaults to mutex}$
2706        for () {
2707                waitfor( ^?{} ) break;                                                  $\C{// high priority}$
2708                or waitfor( girl )                                                              $\C{// girl called, compatible boy ? restart boy then girl}$
2709                        if ( ! is_empty( Boys[ccode] ) ) { `signal_block( Boys[ccode] );  signal_block( Girls[ccode] );` }
2710                or waitfor( boy ) {                                                             $\C{// boy called, compatible girl ? restart girl then boy}$
2711                        if ( ! is_empty( Girls[ccode] ) ) { `signal_block( Girls[ccode] );  signal_block( Boys[ccode] );` }
2712        }
2713}
2714\end{cfa}
2715\caption{Direct communication dating service}
2716\label{f:DirectCommunicationDatingService}
2717\end{figure}
2718
2719\begin{comment}
2720The following shows an example of two threads directly calling each other and accepting calls from each other in a cycle.
2721\begin{cfa}
2722\end{cfa}
2723\vspace{-0.8\baselineskip}
2724\begin{cquote}
2725\begin{tabular}{@{}l@{\hspace{3\parindentlnth}}l@{}}
2726\begin{cfa}
2727thread Ping {} pi;
2728void ping( Ping & mutex ) {}
2729void main( Ping & pi ) {
2730        for ( 10 ) {
2731                `waitfor( ping : pi );`
2732                `pong( po );`
2733        }
2734}
2735int main() {}
2736\end{cfa}
2737&
2738\begin{cfa}
2739thread Pong {} po;
2740void pong( Pong & mutex ) {}
2741void main( Pong & po ) {
2742        for ( 10 ) {
2743                `ping( pi );`
2744                `waitfor( pong : po );`
2745        }
2746}
2747
2748\end{cfa}
2749\end{tabular}
2750\end{cquote}
2751% \lstMakeShortInline@%
2752% \caption{Threads ping/pong using external scheduling}
2753% \label{f:pingpong}
2754% \end{figure}
2755Note, the ping/pong threads are globally declared, @pi@/@po@, and hence, start and possibly complete before the program main starts.
2756\end{comment}
2757
2758
2759\subsection{\texorpdfstring{\protect\lstinline@mutex@ Generators / Coroutines / Threads}{monitor Generators / Coroutines / Threads}}
2760
2761\CFA generators, coroutines, and threads can also be @mutex@ (Table~\ref{t:ExecutionPropertyComposition} cases 4, 6, 12) allowing safe \emph{direct communication} with threads, \ie the custom types can have mutex functions that are called by other threads.
2762All monitor features are available within these mutex functions.
2763For example, if the formatter generator or coroutine equivalent in Figure~\ref{f:CFAFormatGen} is extended with the monitor property and this interface function is used to communicate with the formatter:
2764\begin{cfa}
2765void fmt( Fmt & mutex fmt, char ch ) { fmt.ch = ch; resume( fmt ) }
2766\end{cfa}
2767multiple threads can safely pass characters for formatting.
2768
2769Figure~\ref{f:DirectCommunicationComparison} shows a comparison of direct call-communication in \CFA versus indirect channel-communication in Go.
2770(Ada has a similar mechanism to \CFA direct communication.)
2771% The thread main function is by default @mutex@, so the @mutex@ qualifier for the thread parameter is optional.
2772% The reason is that the thread logically starts instantaneously in the thread main acquiring its mutual exclusion, so it starts before any calls to prepare for synchronizing these calls.
2773The \CFA program @main@ uses the call/return paradigm to directly communicate with the @GoRtn main@, whereas Go switches to the unbuffered channel paradigm to indirectly communicate with the goroutine.
2774Communication by multiple threads is safe for the @gortn@ thread via mutex calls in \CFA or channel assignment in Go.
2775The different between call and channel send occurs for buffered channels making the send asynchronous.
2776In \CFA, asynchronous call and multiple buffers is provided using an administrator and worker threads~\cite{Gentleman81} and/or futures (not discussed).
2777
2778Figure~\ref{f:DirectCommunicationDatingService} shows the dating-service problem in Figure~\ref{f:DatingServiceMonitor} extended from indirect monitor communication to direct thread communication.
2779When converting a monitor to a thread (server), the coding pattern is to move as much code as possible from the accepted functions into the thread main so it does an much work as possible.
2780Notice, the dating server is postponing requests for an unspecified time while continuing to accept new requests.
2781For complex servers, \eg web-servers, there can be hundreds of lines of code in the thread main and safe interaction with clients can be complex.
2782
2783
2784\subsection{Low-level Locks}
2785
2786For completeness and efficiency, \CFA provides a standard set of low-level locks: recursive mutex, condition, semaphore, barrier, \etc, and atomic instructions: @fetchAssign@, @fetchAdd@, @testSet@, @compareSet@, \etc.
2787Some of these low-level mechanism are used to build the \CFA runtime, but we always advocate using high-level mechanisms whenever possible.
2788
2789
2790% \section{Parallelism}
2791% \label{s:Parallelism}
2792%
2793% Historically, computer performance was about processor speeds.
2794% However, with heat dissipation being a direct consequence of speed increase, parallelism is the new source for increased performance~\cite{Sutter05, Sutter05b}.
2795% Therefore, high-performance applications must care about parallelism, which requires concurrency.
2796% The lowest-level approach of parallelism is to use \newterm{kernel threads} in combination with semantics like @fork@, @join@, \etc.
2797% However, kernel threads are better as an implementation tool because of complexity and higher cost.
2798% Therefore, different abstractions are often layered onto kernel threads to simplify them, \eg pthreads.
2799%
2800%
2801% \subsection{User Threads}
2802%
2803% A direct improvement on kernel threads is user threads, \eg Erlang~\cite{Erlang} and \uC~\cite{uC++book}.
2804% This approach provides an interface that matches the language paradigms, gives more control over concurrency by the language runtime, and an abstract (and portable) interface to the underlying kernel threads across operating systems.
2805% In many cases, user threads can be used on a much larger scale (100,000 threads).
2806% Like kernel threads, user threads support preemption, which maximizes nondeterminism, but increases the potential for concurrency errors: race, livelock, starvation, and deadlock.
2807% \CFA adopts user-threads to provide more flexibility and a low-cost mechanism to build any other concurrency approach, \eg thread pools and actors~\cite{Actors}.
2808%
2809% A variant of user thread is \newterm{fibres}, which removes preemption, \eg Go~\cite{Go} @goroutine@s.
2810% Like functional programming, which removes mutation and its associated problems, removing preemption from concurrency reduces nondeterminism, making race and deadlock errors more difficult to generate.
2811% However, preemption is necessary for fairness and to reduce tail-latency.
2812% For concurrency that relies on spinning, if all cores spin the system is livelocked, whereas preemption breaks the livelock.
2813
2814
2815\begin{comment}
2816\subsection{Thread Pools}
2817
2818In contrast to direct threading is indirect \newterm{thread pools}, \eg Java @executor@, where small jobs (work units) are inserted into a work pool for execution.
2819If the jobs are dependent, \ie interact, there is an implicit dependency graph that ties them together.
2820While removing direct concurrency, and hence the amount of context switching, thread pools significantly limit the interaction that can occur among jobs.
2821Indeed, jobs should not block because that also blocks the underlying thread, which effectively means the CPU utilization, and therefore throughput, suffers.
2822While it is possible to tune the thread pool with sufficient threads, it becomes difficult to obtain high throughput and good core utilization as job interaction increases.
2823As well, concurrency errors return, which threads pools are suppose to mitigate.
2824
2825\begin{figure}
2826\centering
2827\begin{tabular}{@{}l|l@{}}
2828\begin{cfa}
2829struct Adder {
2830        int * row, cols;
2831};
2832int operator()() {
2833        subtotal = 0;
2834        for ( int c = 0; c < cols; c += 1 )
2835                subtotal += row[c];
2836        return subtotal;
2837}
2838void ?{}( Adder * adder, int row[$\,$], int cols, int & subtotal ) {
2839        adder.[rows, cols, subtotal] = [rows, cols, subtotal];
2840}
2841
2842
2843
2844
2845\end{cfa}
2846&
2847\begin{cfa}
2848int main() {
2849        const int rows = 10, cols = 10;
2850        int matrix[rows][cols], subtotals[rows], total = 0;
2851        // read matrix
2852        Executor executor( 4 ); // kernel threads
2853        Adder * adders[rows];
2854        for ( r; rows ) { // send off work for executor
2855                adders[r] = new( matrix[r], cols, &subtotal[r] );
2856                executor.send( *adders[r] );
2857        }
2858        for ( r; rows ) {       // wait for results
2859                delete( adders[r] );
2860                total += subtotals[r];
2861        }
2862        sout | total;
2863}
2864\end{cfa}
2865\end{tabular}
2866\caption{Executor}
2867\end{figure}
2868\end{comment}
2869
2870
2871\section{Runtime Structure}
2872\label{s:CFARuntimeStructure}
2873
2874Figure~\ref{f:RunTimeStructure} illustrates the runtime structure of a \CFA program.
2875In addition to the new kinds of objects introduced by \CFA, there are two more runtime entities used to control parallel execution: cluster and (virtual) processor.
2876An executing thread is illustrated by its containment in a processor.
2877
2878\begin{figure}
2879\centering
2880\input{RunTimeStructure}
2881\caption{\CFA Runtime structure}
2882\label{f:RunTimeStructure}
2883\end{figure}
2884
2885
2886\subsection{Cluster}
2887\label{s:RuntimeStructureCluster}
2888
2889A \newterm{cluster} is a collection of user and kernel threads, where the kernel threads run the user threads from the cluster's ready queue, and the operating system runs the kernel threads on the processors from its ready queue.
2890The term \newterm{virtual processor} is introduced as a synonym for kernel thread to disambiguate between user and kernel thread.
2891From the language perspective, a virtual processor is an actual processor (core).
2892
2893The purpose of a cluster is to control the amount of parallelism that is possible among threads, plus scheduling and other execution defaults.
2894The default cluster-scheduler is single-queue multi-server, which provides automatic load-balancing of threads on processors.
2895However, the design allows changing the scheduler, \eg multi-queue multi-server with work-stealing/sharing across the virtual processors.
2896If several clusters exist, both threads and virtual processors, can be explicitly migrated from one cluster to another.
2897No automatic load balancing among clusters is performed by \CFA.
2898
2899When a \CFA program begins execution, it creates a user cluster with a single processor and a special processor to handle preemption that does not execute user threads.
2900The user cluster is created to contain the application user-threads.
2901Having all threads execute on the one cluster often maximizes utilization of processors, which minimizes runtime.
2902However, because of limitations of scheduling requirements (real-time), NUMA architecture, heterogeneous hardware, or issues with the underlying operating system, multiple clusters are sometimes necessary.
2903
2904
2905\subsection{Virtual Processor}
2906\label{s:RuntimeStructureProcessor}
2907
2908A virtual processor is implemented by a kernel thread, \eg UNIX process, which are scheduled for execution on a hardware processor by the underlying operating system.
2909Programs may use more virtual processors than hardware processors.
2910On a multiprocessor, kernel threads are distributed across the hardware processors resulting in virtual processors executing in parallel.
2911(It is possible to use affinity to lock a virtual processor onto a particular hardware processor~\cite{affinityLinux,affinityWindows}, which is used when caching issues occur or for heterogeneous hardware processors.) %, affinityFreebsd, affinityNetbsd, affinityMacosx
2912The \CFA runtime attempts to block unused processors and unblock processors as the system load increases;
2913balancing the workload with processors is difficult because it requires future knowledge, \ie what will the application workload do next.
2914Preemption occurs on virtual processors rather than user threads, via operating-system interrupts.
2915Thus virtual processors execute user threads, where preemption frequency applies to a virtual processor, so preemption occurs randomly across the executed user threads.
2916Turning off preemption transforms user threads into fibres.
2917
2918
2919\begin{comment}
2920\section{Implementation}
2921\label{s:Implementation}
2922
2923A primary implementation challenge is avoiding contention from dynamically allocating memory because of bulk acquire, \eg the internal-scheduling design is almost free of allocations.
2924All blocking operations are made by parking threads onto queues, therefore all queues are designed with intrusive nodes, where each node has preallocated link fields for chaining.
2925Furthermore, several bulk-acquire operations need a variable amount of memory.
2926This storage is allocated at the base of a thread's stack before blocking, which means programmers must add a small amount of extra space for stacks.
2927
2928In \CFA, ordering of monitor acquisition relies on memory ordering to prevent deadlock~\cite{Havender68}, because all objects have distinct non-overlapping memory layouts, and mutual-exclusion for a monitor is only defined for its lifetime.
2929When a mutex call is made, pointers to the concerned monitors are aggregated into a variable-length array and sorted.
2930This array persists for the entire duration of the mutual exclusion and is used extensively for synchronization operations.
2931
2932To improve performance and simplicity, context switching occurs inside a function call, so only callee-saved registers are copied onto the stack and then the stack register is switched;
2933the corresponding registers are then restored for the other context.
2934Note, the instruction pointer is untouched since the context switch is always inside the same function.
2935Experimental results (not presented) for a stackless or stackful scheduler (1 versus 2 context switches) (see Section~\ref{s:Concurrency}) show the performance is virtually equivalent, because both approaches are dominated by locking to prevent a race condition.
2936
2937All kernel threads (@pthreads@) created a stack.
2938Each \CFA virtual processor is implemented as a coroutine and these coroutines run directly on the kernel-thread stack, effectively stealing this stack.
2939The exception to this rule is the program main, \ie the initial kernel thread that is given to any program.
2940In order to respect C expectations, the stack of the initial kernel thread is used by program main rather than the main processor, allowing it to grow dynamically as in a normal C program.
2941\end{comment}
2942
2943
2944\subsection{Preemption}
2945
2946Nondeterministic preemption provides fairness from long-running threads, and forces concurrent programmers to write more robust programs, rather than relying on code between cooperative scheduling to be atomic.
2947This atomic reliance can fail on multi-core machines, because execution across cores is nondeterministic.
2948A different reason for not supporting preemption is that it significantly complicates the runtime system, \eg Windows runtime does not support interrupts and on Linux systems, interrupts are complex (see below).
2949Preemption is normally handled by setting a countdown timer on each virtual processor.
2950When the timer expires, an interrupt is delivered, and its signal handler resets the countdown timer, and if the virtual processor is executing in user code, the signal handler performs a user-level context-switch, or if executing in the language runtime kernel, the preemption is ignored or rolled forward to the point where the runtime kernel context switches back to user code.
2951Multiple signal handlers may be pending.
2952When control eventually switches back to the signal handler, it returns normally, and execution continues in the interrupted user thread, even though the return from the signal handler may be on a different kernel thread than the one where the signal is delivered.
2953The only issue with this approach is that signal masks from one kernel thread may be restored on another as part of returning from the signal handler;
2954therefore, the same signal mask is required for all virtual processors in a cluster.
2955Because preemption interval is usually long (1 millisecond) performance cost is negligible.
2956
2957Linux switched a decade ago from specific to arbitrary virtual-processor signal-delivery for applications with multiple kernel threads.
2958In the new semantics, a virtual-processor directed signal may be delivered to any virtual processor created by the application that does not have the signal blocked.
2959Hence, the timer-expiry signal, which is generated \emph{externally} by the Linux kernel to an application, is delivered to any of its Linux subprocesses (kernel threads).
2960To ensure each virtual processor receives a preemption signal, a discrete-event simulation is run on a special virtual processor, and only it sets and receives timer events.
2961Virtual processors register an expiration time with the discrete-event simulator, which is inserted in sorted order.
2962The simulation sets the countdown timer to the value at the head of the event list, and when the timer expires, all events less than or equal to the current time are processed.
2963Processing a preemption event sends an \emph{internal} @SIGUSR1@ signal to the registered virtual processor, which is always delivered to that processor.
2964
2965
2966\subsection{Debug Kernel}
2967
2968There are two versions of the \CFA runtime kernel: debug and non-debug.
2969The debugging version has many runtime checks and internal assertions, \eg stack non-writable guard page, and checks for stack overflow whenever context switches occur among coroutines and threads, which catches most stack overflows.
2970After a program is debugged, the non-debugging version can be used to significantly decrease space and increase performance.
2971
2972
2973\section{Performance}
2974\label{s:Performance}
2975
2976To test the performance of the \CFA runtime, a series of microbenchmarks are used to compare \CFA with pthreads, Java 11.0.6, Go 1.12.6, Rust 1.37.0, Python 3.7.6, Node.js 12.14.1, and \uC 7.0.0.
2977For comparison, the package must be multi-processor (M:N), which excludes libdil and /libmil~\cite{libdill} (M:1)), and use a shared-memory programming model, \eg not message passing.
2978The benchmark computer is an AMD Opteron\texttrademark\ 6380 NUMA 64-core, 8 socket, 2.5 GHz processor, running Ubuntu 16.04.6 LTS, and pthreads/\CFA/\uC are compiled with gcc 9.2.1.
2979
2980All benchmarks are run using the following harness. (The Java harness is augmented to circumvent JIT issues.)
2981\begin{cfa}
2982#define BENCH( `run` ) uint64_t start = cputime_ns();  `run;`  double result = (double)(cputime_ns() - start) / N;
2983\end{cfa}
2984where CPU time in nanoseconds is from the appropriate language clock.
2985Each benchmark is performed @N@ times, where @N@ is selected so the benchmark runs in the range of 2--20 seconds for the specific programming language.
2986The total time is divided by @N@ to obtain the average time for a benchmark.
2987Each benchmark experiment is run 13 times and the average appears in the table.
2988All omitted tests for other languages are functionally identical to the \CFA tests and available online~\cite{CforallBenchMarks}.
2989% tar --exclude-ignore=exclude -cvhf benchmark.tar benchmark
2990
2991\paragraph{Creation}
2992
2993Creation is measured by creating and deleting a specific kind of control-flow object.
2994Figure~\ref{f:creation} shows the code for \CFA with results in Table~\ref{t:creation}.
2995Note, the call stacks of \CFA coroutines are lazily created on the first resume, therefore the cost of creation with and without a stack are presented.
2996
2997\begin{multicols}{2}
2998\lstset{language=CFA,moredelim=**[is][\color{red}]{@}{@},deletedelim=**[is][]{`}{`}}
2999\begin{cfa}
3000@coroutine@ MyCoroutine {};
3001void ?{}( MyCoroutine & this ) {
3002#ifdef EAGER
3003        resume( this );
3004#endif
3005}
3006void main( MyCoroutine & ) {}
3007int main() {
3008        BENCH( for ( N ) { @MyCoroutine c;@ } )
3009        sout | result;
3010}
3011\end{cfa}
3012\captionof{figure}{\CFA creation benchmark}
3013\label{f:creation}
3014
3015\columnbreak
3016
3017\vspace*{-16pt}
3018\captionof{table}{Creation comparison (nanoseconds)}
3019\label{t:creation}
3020
3021\begin{tabular}[t]{@{}r*{3}{D{.}{.}{5.2}}@{}}
3022\multicolumn{1}{@{}c}{} & \multicolumn{1}{c}{Median} & \multicolumn{1}{c}{Average} & \multicolumn{1}{c@{}}{Std Dev} \\
3023\CFA generator                  & 0.6           & 0.6           & 0.0           \\
3024\CFA coroutine lazy             & 13.4          & 13.1          & 0.5           \\
3025\CFA coroutine eager    & 144.7         & 143.9         & 1.5           \\
3026\CFA thread                             & 466.4         & 468.0         & 11.3          \\
3027\uC coroutine                   & 155.6         & 155.7         & 1.7           \\
3028\uC thread                              & 523.4         & 523.9         & 7.7           \\
3029Python generator                & 123.2         & 124.3         & 4.1           \\
3030Node.js generator               & 32.3          & 32.2          & 0.3           \\
3031Goroutine thread                & 751.0         & 750.5         & 3.1           \\
3032Rust tokio thread               & 1860.0        & 1881.1        & 37.6          \\
3033Rust thread                             & 53801.0       & 53896.8       & 274.9         \\
3034Java thread                             & 120274.0      & 120722.9      & 2356.7        \\
3035Pthreads thread                 & 31465.5       & 31419.5       & 140.4
3036\end{tabular}
3037\end{multicols}
3038
3039\vspace*{-10pt}
3040\paragraph{Internal Scheduling}
3041
3042Internal scheduling is measured using a cycle of two threads signalling and waiting.
3043Figure~\ref{f:schedint} shows the code for \CFA, with results in Table~\ref{t:schedint}.
3044Note, the incremental cost of bulk acquire for \CFA, which is largely a fixed cost for small numbers of mutex objects.
3045Java scheduling is significantly greater because the benchmark explicitly creates multiple thread in order to prevent the JIT from making the program sequential, \ie removing all locking.
3046
3047\begin{multicols}{2}
3048\lstset{language=CFA,moredelim=**[is][\color{red}]{@}{@},deletedelim=**[is][]{`}{`}}
3049\begin{cfa}
3050volatile int go = 0;
3051@condition c;@
3052@monitor@ M {} m1/*, m2, m3, m4*/;
3053void call( M & @mutex p1/*, p2, p3, p4*/@ ) {
3054        @signal( c );@
3055}
3056void wait( M & @mutex p1/*, p2, p3, p4*/@ ) {
3057        go = 1; // continue other thread
3058        for ( N ) { @wait( c );@ } );
3059}
3060thread T {};
3061void main( T & ) {
3062        while ( go == 0 ) { yield(); } // waiter must start first
3063        BENCH( for ( N ) { call( m1/*, m2, m3, m4*/ ); } )
3064        sout | result;
3065}
3066int main() {
3067        T t;
3068        wait( m1/*, m2, m3, m4*/ );
3069}
3070\end{cfa}
3071\vspace*{-8pt}
3072\captionof{figure}{\CFA Internal-scheduling benchmark}
3073\label{f:schedint}
3074
3075\columnbreak
3076
3077\vspace*{-16pt}
3078\captionof{table}{Internal-scheduling comparison (nanoseconds)}
3079\label{t:schedint}
3080\bigskip
3081
3082\begin{tabular}{@{}r*{3}{D{.}{.}{5.2}}@{}}
3083\multicolumn{1}{@{}c}{} & \multicolumn{1}{c}{Median} & \multicolumn{1}{c}{Average} & \multicolumn{1}{c@{}}{Std Dev} \\
3084\CFA @signal@, 1 monitor        & 364.4         & 364.2         & 4.4           \\
3085\CFA @signal@, 2 monitor        & 484.4         & 483.9         & 8.8           \\
3086\CFA @signal@, 4 monitor        & 709.1         & 707.7         & 15.0          \\
3087\uC @signal@ monitor            & 328.3         & 327.4         & 2.4           \\
3088Rust cond. variable                     & 7514.0        & 7437.4        & 397.2         \\
3089Java @notify@ monitor           & 9623.0        & 9654.6        & 236.2         \\
3090Pthreads cond. variable         & 5553.7        & 5576.1        & 345.6
3091\end{tabular}
3092\end{multicols}
3093
3094
3095\paragraph{External Scheduling}
3096
3097External scheduling is measured using a cycle of two threads calling and accepting the call using the @waitfor@ statement.
3098Figure~\ref{f:schedext} shows the code for \CFA with results in Table~\ref{t:schedext}.
3099Note, the incremental cost of bulk acquire for \CFA, which is largely a fixed cost for small numbers of mutex objects.
3100
3101\begin{multicols}{2}
3102\lstset{language=CFA,moredelim=**[is][\color{red}]{@}{@},deletedelim=**[is][]{`}{`}}
3103\vspace*{-16pt}
3104\begin{cfa}
3105@monitor@ M {} m1/*, m2, m3, m4*/;
3106void call( M & @mutex p1/*, p2, p3, p4*/@ ) {}
3107void wait( M & @mutex p1/*, p2, p3, p4*/@ ) {
3108        for ( N ) { @waitfor( call : p1/*, p2, p3, p4*/ );@ }
3109}
3110thread T {};
3111void main( T & ) {
3112        BENCH( for ( N ) { call( m1/*, m2, m3, m4*/ ); } )
3113        sout | result;
3114}
3115int main() {
3116        T t;
3117        wait( m1/*, m2, m3, m4*/ );
3118}
3119\end{cfa}
3120\captionof{figure}{\CFA external-scheduling benchmark}
3121\label{f:schedext}
3122
3123\columnbreak
3124
3125\vspace*{-16pt}
3126\captionof{table}{External-scheduling comparison (nanoseconds)}
3127\label{t:schedext}
3128\begin{tabular}{@{}r*{3}{D{.}{.}{3.2}}@{}}
3129\multicolumn{1}{@{}c}{} & \multicolumn{1}{c}{Median} &\multicolumn{1}{c}{Average} & \multicolumn{1}{c@{}}{Std Dev} \\
3130\CFA @waitfor@, 1 monitor       & 367.1 & 365.3 & 5.0   \\
3131\CFA @waitfor@, 2 monitor       & 463.0 & 464.6 & 7.1   \\
3132\CFA @waitfor@, 4 monitor       & 689.6 & 696.2 & 21.5  \\
3133\uC \lstinline[language=uC++]|_Accept| monitor  & 328.2 & 329.1 & 3.4   \\
3134Go \lstinline[language=Golang]|select| channel  & 365.0 & 365.5 & 1.2
3135\end{tabular}
3136\end{multicols}
3137
3138\paragraph{Mutual-Exclusion}
3139
3140Uncontented mutual exclusion, which frequently occurs, is measured by entering and leaving a critical section.
3141For monitors, entering and leaving a mutex function is measured, otherwise the language-appropriate mutex-lock is measured.
3142For comparison, a spinning (versus blocking) test-and-test-set lock is presented.
3143Figure~\ref{f:mutex} shows the code for \CFA with results in Table~\ref{t:mutex}.
3144Note the incremental cost of bulk acquire for \CFA, which is largely a fixed cost for small numbers of mutex objects.
3145
3146\begin{multicols}{2}
3147\lstset{language=CFA,moredelim=**[is][\color{red}]{@}{@},deletedelim=**[is][]{`}{`}}
3148\begin{cfa}
3149@monitor@ M {} m1/*, m2, m3, m4*/;
3150call( M & @mutex p1/*, p2, p3, p4*/@ ) {}
3151int main() {
3152        BENCH( for( N ) call( m1/*, m2, m3, m4*/ ); )
3153        sout | result;
3154}
3155\end{cfa}
3156\captionof{figure}{\CFA acquire/release mutex benchmark}
3157\label{f:mutex}
3158
3159\columnbreak
3160
3161\vspace*{-16pt}
3162\captionof{table}{Mutex comparison (nanoseconds)}
3163\label{t:mutex}
3164\begin{tabular}{@{}r*{3}{D{.}{.}{3.2}}@{}}
3165\multicolumn{1}{@{}c}{} & \multicolumn{1}{c}{Median} &\multicolumn{1}{c}{Average} & \multicolumn{1}{c@{}}{Std Dev} \\
3166test-and-test-set lock                  & 19.1  & 18.9  & 0.4   \\
3167\CFA @mutex@ function, 1 arg.   & 48.3  & 47.8  & 0.9   \\
3168\CFA @mutex@ function, 2 arg.   & 86.7  & 87.6  & 1.9   \\
3169\CFA @mutex@ function, 4 arg.   & 173.4 & 169.4 & 5.9   \\
3170\uC @monitor@ member rtn.               & 54.8  & 54.8  & 0.1   \\
3171Goroutine mutex lock                    & 34.0  & 34.0  & 0.0   \\
3172Rust mutex lock                                 & 33.0  & 33.2  & 0.8   \\
3173Java synchronized method                & 31.0  & 31.0  & 0.0   \\
3174Pthreads mutex Lock                             & 31.0  & 31.1  & 0.4
3175\end{tabular}
3176\end{multicols}
3177
3178\paragraph{Context Switching}
3179
3180In procedural programming, the cost of a function call is important as modularization (refactoring) increases.
3181(In many cases, a compiler inlines function calls to increase the size and number of basic blocks for optimizing.)
3182Similarly, when modularization extends to coroutines and threads, the time for a context switch becomes a relevant factor.
3183The coroutine test is from resumer to suspender and from suspender to resumer, which is two context switches.
3184%For async-await systems, the test is scheduling and fulfilling @N@ empty promises, where all promises are allocated before versus interleaved with fulfillment to avoid garbage collection.
3185For async-await systems, the test measures the cost of the @await@ expression entering the event engine by awaiting @N@ promises, where each created promise is resolved by an immediate event in the engine (using Node.js @setImmediate@).
3186The thread test is using yield to enter and return from the runtime kernel, which is two context switches.
3187The difference in performance between coroutine and thread context-switch is the cost of scheduling for threads, whereas coroutines are self-scheduling.
3188Figure~\ref{f:ctx-switch} shows the \CFA code for a coroutine and thread with results in Table~\ref{t:ctx-switch}.
3189
3190% From: Gregor Richards <gregor.richards@uwaterloo.ca>
3191% To: "Peter A. Buhr" <pabuhr@plg2.cs.uwaterloo.ca>
3192% Date: Fri, 24 Jan 2020 13:49:18 -0500
3193%
3194% I can also verify that the previous version, which just tied a bunch of promises together, *does not* go back to the
3195% event loop at all in the current version of Node. Presumably they're taking advantage of the fact that the ordering of
3196% events is intentionally undefined to just jump right to the next 'then' in the chain, bypassing event queueing
3197% entirely. That's perfectly correct behavior insofar as its difference from the specified behavior isn't observable, but
3198% it isn't typical or representative of much anything useful, because most programs wouldn't have whole chains of eager
3199% promises. Also, it's not representative of *anything* you can do with async/await, as there's no way to encode such an
3200% eager chain that way.
3201
3202\begin{multicols}{2}
3203\lstset{language=CFA,moredelim=**[is][\color{red}]{@}{@},deletedelim=**[is][]{`}{`}}
3204\begin{cfa}[aboveskip=0pt,belowskip=0pt]
3205@coroutine@ C {};
3206void main( C & ) { for () { @suspend;@ } }
3207int main() { // coroutine test
3208        C c;
3209        BENCH( for ( N ) { @resume( c );@ } )
3210        sout | result;
3211}
3212int main() { // thread test
3213        BENCH( for ( N ) { @yield();@ } )
3214        sout | result;
3215}
3216\end{cfa}
3217\captionof{figure}{\CFA context-switch benchmark}
3218\label{f:ctx-switch}
3219
3220\columnbreak
3221
3222\vspace*{-16pt}
3223\captionof{table}{Context switch comparison (nanoseconds)}
3224\label{t:ctx-switch}
3225\begin{tabular}{@{}r*{3}{D{.}{.}{3.2}}@{}}
3226\multicolumn{1}{@{}c}{} & \multicolumn{1}{c}{Median} &\multicolumn{1}{c}{Average} & \multicolumn{1}{c@{}}{Std Dev} \\
3227C function                      & 1.8           & 1.8           & 0.0   \\
3228\CFA generator          & 1.8           & 2.0           & 0.3   \\
3229\CFA coroutine          & 32.5          & 32.9          & 0.8   \\
3230\CFA thread                     & 93.8          & 93.6          & 2.2   \\
3231\uC coroutine           & 50.3          & 50.3          & 0.2   \\
3232\uC thread                      & 97.3          & 97.4          & 1.0   \\
3233Python generator        & 40.9          & 41.3          & 1.5   \\
3234Node.js await           & 1852.2        & 1854.7        & 16.4  \\
3235Node.js generator       & 32.6          & 32.2          & 1.0   \\
3236Goroutine thread        & 143.0         & 143.3         & 1.1   \\
3237Rust async await        & 32.0          & 32.0          & 0.0   \\
3238Rust tokio thread       & 143.0         & 143.0         & 1.7   \\
3239Rust thread                     & 332.0         & 331.4         & 2.4   \\
3240Java thread                     & 405.0         & 415.0         & 17.6  \\
3241Pthreads thread         & 334.3         & 335.2         & 3.9
3242\end{tabular}
3243\end{multicols}
3244
3245
3246\subsection{Discussion}
3247
3248Languages using 1:1 threading based on pthreads can at best meet or exceed, due to language overhead, the pthread results.
3249Note, pthreads has a fast zero-contention mutex lock checked in user space.
3250Languages with M:N threading have better performance than 1:1 because there is no operating-system interactions.
3251Languages with stackful coroutines have higher cost than stackless coroutines because of stack allocation and context switching;
3252however, stackful \uC and \CFA coroutines have approximately the same performance as stackless Python and Node.js generators.
3253The \CFA stackless generator is approximately 25 times faster for suspend/resume and 200 times faster for creation than stackless Python and Node.js generators.
3254
3255
3256\section{Conclusions and Future Work}
3257
3258Advanced control-flow will always be difficult, especially when there is temporal ordering and nondeterminism.
3259However, many systems exacerbate the difficulty through their presentation mechanisms.
3260This paper shows it is possible to understand high-level control-flow using three properties: statefulness, thread, mutual-exclusion/synchronization.
3261Combining these properties creates a number of high-level, efficient, and maintainable control-flow types: generator, coroutine, thread, each of which can be a monitor.
3262Eliminated from \CFA are barging and spurious wakeup, which are nonintuitive and lead to errors, and having to work with a bewildering set of low-level locks and acquisition techniques.
3263\CFA high-level race-free monitors and threads, when used with mutex access function, provide the core mechanisms for mutual exclusion and synchronization, without having to resort to magic qualifiers like @volatile@ or @atomic@.
3264Extending these mechanisms to handle high-level deadlock-free bulk acquire across both mutual exclusion and synchronization is a unique contribution.
3265The \CFA runtime provides concurrency based on a preemptive M:N user-level threading-system, executing in clusters, which encapsulate scheduling of work on multiple kernel threads providing parallelism.
3266The M:N model is judged to be efficient and provide greater flexibility than a 1:1 threading model.
3267These concepts and the \CFA runtime-system are written in the \CFA language, extensively leveraging the \CFA type-system, which demonstrates the expressiveness of the \CFA language.
3268Performance comparisons with other concurrent systems and languages show the \CFA approach is competitive across all basic operations, which translates directly into good performance in well-written applications with advanced control-flow.
3269C programmers should feel comfortable using these mechanisms for developing complex control-flow in applications, with the ability to obtain maximum available performance by selecting mechanisms at the appropriate level of need using only calling communication.
3270
3271While control flow in \CFA has a strong start, development is still underway to complete a number of missing features.
3272
3273\vspace{-5pt}
3274\paragraph{Flexible Scheduling}
3275\label{futur:sched}
3276
3277An important part of concurrency is scheduling.
3278Different scheduling algorithms can affect performance, both in terms of average and variation.
3279However, no single scheduler is optimal for all workloads and therefore there is value in being able to change the scheduler for given programs.
3280One solution is to offer various tuning options, allowing the scheduler to be adjusted to the requirements of the workload.
3281However, to be truly flexible, a pluggable scheduler is necessary.
3282Currently, the \CFA pluggable scheduler is too simple to handle complex scheduling, \eg quality of service and real-time, where the scheduler must interact with mutex objects to deal with issues like priority inversion~\cite{Buhr00b}.
3283
3284\vspace{-5pt}
3285\paragraph{Non-Blocking I/O}
3286\label{futur:nbio}
3287Many modern workloads are not bound by computation but IO operations, common cases being web servers and XaaS~\cite{XaaS} (anything as a service).
3288These types of workloads require significant engineering to amortizing costs of blocking IO-operations.
3289At its core, non-blocking I/O is an operating-system level feature queuing IO operations, \eg network operations, and registering for notifications instead of waiting for requests to complete.
3290Current trends use asynchronous programming like callbacks, futures, and/or promises, \eg Node.js~\cite{NodeJs} for JavaScript, Spring MVC~\cite{SpringMVC} for Java, and Django~\cite{Django} for Python.
3291However, these solutions lead to code that is hard to create, read and maintain.
3292A better approach is to tie non-blocking I/O into the concurrency system to provide ease of use with low overhead, \eg thread-per-connection web-services.
3293A non-blocking I/O library is currently under development for \CFA.
3294
3295\vspace{-5pt}
3296\paragraph{Other Concurrency Tools}
3297\label{futur:tools}
3298
3299While monitors offer flexible and powerful concurrency for \CFA, other concurrency tools are also necessary for a complete multi-paradigm concurrency package.
3300Examples of such tools can include futures and promises~\cite{promises}, executors and actors.
3301These additional features are useful for applications that can be constructed without shared data and direct blocking.
3302As well, new \CFA extensions should make it possible to create a uniform interface for virtually all mutual exclusion, including monitors and low-level locks.
3303
3304\vspace{-5pt}
3305\paragraph{Implicit Threading}
3306\label{futur:implcit}
3307
3308Basic \emph{embarrassingly parallel} applications can benefit greatly from implicit concurrency, where sequential programs are converted to concurrent, possibly with some help from pragmas to guide the conversion.
3309This type of concurrency can be achieved both at the language level and at the library level.
3310The canonical example of implicit concurrency is concurrent nested @for@ loops, which are amenable to divide and conquer algorithms~\cite{uC++book}.
3311The \CFA language features should make it possible to develop a reasonable number of implicit concurrency mechanism to solve basic HPC data-concurrency problems.
3312However, implicit concurrency is a restrictive solution with significant limitations, so it can never replace explicit concurrent programming.
3313
3314
3315\section{Acknowledgements}
3316
3317The authors recognize the design assistance of Aaron Moss, Rob Schluntz, Andrew Beach, and Michael Brooks; David Dice for commenting and helping with the Java benchmarks; and Gregor Richards for helping with the Node.js benchmarks.
3318This research is funded by a grant from Waterloo-Huawei (\url{http://www.huawei.com}) Joint Innovation Lab. %, and Peter Buhr is partially funded by the Natural Sciences and Engineering Research Council of Canada.
3319
3320{%
3321\fontsize{9bp}{11.5bp}\selectfont%
3322\bibliography{pl,local}
3323}%
3324
3325\end{document}
3326
3327% Local Variables: %
3328% tab-width: 4 %
3329% fill-column: 120 %
3330% compile-command: "make" %
3331% End: %
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