# source:doc/proposals/concurrency/concurrency.tex@ab84e8a

aaron-thesisarm-ehcleanup-dtorsdeferred_resndemanglerenumforall-pointer-decayjacob/cs343-translationjenkins-sandboxnew-astnew-ast-unique-exprnew-envno_listpersistent-indexerresolv-newwith_gc
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80%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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82\begin{document}
83% \linenumbers
84
85\title{Concurrency in \CFA}
86\author{Thierry Delisle \\
87Dept. of Computer Science, University of Waterloo, \\ Waterloo, Ontario, Canada
88}
89
90\maketitle
91\section{Introduction}
92This proposal provides a minimal core concurrency API that is both simple, efficient and can be reused to build higher-level features. The simplest possible core is a thread and a lock but this low-level approach is hard to master. An easier approach for users is to support higher-level construct as the basis of the concurrency in \CFA.
93Indeed, for highly productive parallel programming high-level approaches are much more popular\cite{HPP:Study}. Examples are task based parallelism, message passing, implicit threading.
94
95There are actually two problems that need to be solved in the design of the concurrency for a language. Which concurrency tools are available to the users and which parallelism tools are available. While these two concepts are often seen together, they are in fact distinct concepts that require different sorts of tools\cite{Buhr05a}. Concurrency tools need to handle mutual exclusion and synchronization while parallelism tools are more about performance, cost and resource utilization.
96
97\section{Concurrency}
98Several tool can be used to solve concurrency challenges. Since these challenges always appear with the use of mutable shared state, some languages and libraries simply disallow mutable shared-state (Erlang\cite{Erlang}, Haskell\cite{Haskell}, Akka (Scala)\cite{Akka}). In these paradigms, interaction among concurrent objects rely on message passing or other paradigms that often closely relate to networking concepts. However, in imperative or OO languages, these approaches entail a clear distinction between concurrent and non-concurrent paradigms (i.e. message passing versus routine call). Which in turns mean that programmers need to learn two sets of designs patterns in order to be effective. Approaches based on shared memory are more closely related to non-concurrent paradigms since they often rely on non-concurrent constructs like routine calls and objects. At a lower level these can be implemented as locks and atomic operations. However, for productivity reasons it is desireable to have a higher-level construct to be the core concurrency paradigm\cite{HPP:Study}. This project proposes Monitors\cite{Hoare74} as the core concurrency construct.
99\\
100
101Finally, an approach that is worth mentionning because it is gaining in popularity is transactionnal memory\cite{Dice10}. However, the performance and feature set is currently too restrictive to be possible to add such a paradigm to a language like C or \CC\cit, which is why it was rejected as the core paradigm for concurrency in \CFA.
102
103\subsection{Monitors}
104A monitor is a set of routines that ensure mutual exclusion when accessing shared state. This concept is generally associated with Object-Oriented Languages like Java\cite{Java} or \uC\cite{uC++book} but does not strictly require OOP semantics. The only requirements is the ability to declare a handle to a shared object and a set of routines that act on it :
105\begin{lstlisting}
106        typedef /*some monitor type*/ monitor;
107        int f(monitor & m);
108
109        int main() {
110                monitor m;
111                f(m);
112        }
113\end{lstlisting}
114
115\subsubsection{Call semantics} \label{call}
116The above example of monitors already displays some of their intrinsic caracteristics. Indeed, it is necessary to use pass-by-reference over pass-by-value for monitor routines. This semantics is important because at their core, monitors are implicit mutual exclusion objects (locks), and these objects cannot be copied. Therefore, monitors are implicitly non-copyable.
117\\
118
119Another aspect to consider is when a monitor acquires its mutual exclusion. Indeed, a monitor may need to be passed through multiple helper routines that do not acquire the monitor mutual exclusion on entry. Examples of this can be both generic helper routines (\code{swap}, \code{sort}, etc.) or specific helper routines like the following example :
120
121\begin{lstlisting}
122        mutex struct counter_t { /*...*/ };
123
124        void ?{}(counter_t & nomutex this);
125        int ++?(counter_t & mutex this);
126        void ?{}(Int * this, counter_t & mutex cnt);
127\end{lstlisting}
128*semantics of the declaration of \code{mutex struct counter_t} are discussed in details in section \ref{data}
129\\
130
131This example is of a monitor implementing an atomic counter. Here, the constructor uses the \code{nomutex} keyword to signify that it does not acquire the coroutine mutual exclusion when constructing. This is because object not yet constructed should never be shared and therefore do not require mutual exclusion. The prefix increment operator
132uses \code{mutex} to protect the incrementing process from race conditions. Finally, we have a conversion operator from \code{counter_t} to \code{Int}. This conversion may or may not require the \code{mutex} key word depending whether or not reading an \code{Int} is an atomic operation or not.
133\\
134
135Having both \code{mutex} and \code{nomutex} keywords could be argued to be redundant based on the meaning of a routine having neither of these keywords. If there were a meaning to routine \code{void foo(counter_t & this)} then one could argue that it should be to default to the safest option : \code{mutex}. On the other hand, the option of having routine \code{void foo(counter_t & this)} mean \code{nomutex} is unsafe by default and may easily cause subtle errors. It can be argued that this is the more "normal" behavior, \code{nomutex} effectively stating explicitly that "this routine has nothing special". An other alternative is to make one of these keywords mandatory, which would provide the same semantics but without the ambiguity of supporting routine \code{void foo(counter_t & this)}. Mandatory keywords would also have the added benefice of being more clearly self-documented but at the cost of extra typing. In the end, which solution should be picked is still up for debate. For the reminder of this proposal, the explicit approach will be used for the sake of clarity.
136\\
137
138Regardless of which keyword is kept, it is important to establish when mutex/nomutex may be used depending on type parameters.
139\begin{lstlisting}
140        int f1(monitor & mutex m);
141        int f2(const monitor & mutex m);
142        int f3(monitor ** mutex m);
143        int f4(monitor *[] mutex m);
144        int f5(graph(monitor*) & mutex m);
145\end{lstlisting}
146
147The problem is to indentify which object(s) should be acquired. Furthermore we also need to acquire each objects only once. In case of simple routines like \code{f1} and \code{f2} it is easy to identify an exhaustive list of objects to acquire on entering. Adding indirections (\code{f3}) still allows the compiler and programmer to indentify which object will be acquired. However, adding in arrays (\code{f4}) makes it much harder. Array lengths aren't necessarily known in C and even then making sure we only acquire objects once becomes also none trivial. This can be extended to absurd limits like \code{f5} which uses a custom graph of monitors. To keep everyone as sane as possible\cite{Chicken}, this projects imposes the requirement that a routine may only acquire one monitor per parameter and it must be the type of the parameter (ignoring potential qualifiers and indirections).
148
149\subsubsection{Data semantics} \label{data}
150Once the call semantics are established, the next step is to establish data semantics. Indeed, until now a monitor is used simply as a generic handle but in most cases monitors contian shared data. This data should be intrinsic to the monitor declaration to prevent any accidental use of data without its appripriate protection. For example here is a more fleshed-out version of the counter showed in \ref{call}:
151\begin{lstlisting}
152        mutex struct counter_t {
153                int value;
154        };
155
156        void ?{}(counter_t & nomutex this) {
157                this.cnt = 0;
158        }
159
160        int ++?(counter_t & mutex this) {
161                return ++this->value;
162        }
163
164        void ?{}(int * this, counter_t & mutex cnt) {
165                *this = (int)cnt;
166        }
167\end{lstlisting}
168\begin{tabular}{ c c }
170\begin{lstlisting}
171        void f(counter_t & mutex c) {
172                for(;;) {
173                        sout | (int)c | endl;
174                }
175        }
176\end{lstlisting} &\begin{lstlisting}
177        void g(counter_t & mutex c) {
178                for(;;) {
179                        ++c;
180                }
181        }
182
183\end{lstlisting}
184\end{tabular}
185\\
186
187
188This simple counter offers an example of monitor usage. Notice how the counter is used without any explicit synchronisation and yet supports thread-safe semantics for both reading and writting. \\
189
190These simple mutual exclusion semantics also naturally expand to multi-monitor calls.
191\begin{lstlisting}
192        int f(MonitorA & mutex a, MonitorB & mutex b);
193
194        MonitorA a;
195        MonitorB b;
196        f(a,b);
197\end{lstlisting}
198
199This code acquires both locks before entering the critical section. In practice, writing multi-locking routines that can not lead to deadlocks can be very tricky. Having language level support for such feature is therefore a significant asset for \CFA. However, this does have significant repercussions relating to scheduling (see \ref{insched} and \ref{extsched}). Furthermore, the ability to acquire multiple monitors at the same time does incur a significant pitfall even without looking into scheduling. For example :
200\begin{lstlisting}
201        void foo(A & mutex a, B & mutex a) {
202                //...
203        }
204
205        void bar(A & mutex a, B & nomutex a)
206                //...
207                foo(a, b);
208                //...
209        }
210
211        void baz(A & nomutex a, B & mutex a)
212                //...
213                foo(a, b);
214                //...
215        }
216\end{lstlisting}
217
218Recursive mutex routine calls are allowed in \CFA but if not done carefully it can lead to nested monitor call problems\cite{Lister77}. These problems which are a specific  implementation of the lock acquiring order problem. In the example above, the user uses implicit ordering in the case of function \code{bar} but explicit ordering in the case of \code{baz}. This subtle mistake can mean that calling these two functions concurrently will lead to deadlocks, depending on the implicit ordering matching the explicit ordering. As shown on several occasion\cit, there isn't really any solutions to this problem, users simply need to be carefull when acquiring multiple monitors at the same time.
219
220\subsubsection{Implementation Details: Interaction with polymorphism}
221At first glance, interaction between monitors and \CFA's concept of polymorphism seem complexe to support. However, it can be reasoned that entry-point locking can solve most of the issues that could be present with polymorphism.
222
223First of all, interaction between \code{otype} polymorphism and monitors is impossible since monitors do not support copying. Therefore the main question is how to support \code{dtype} polymorphism. We must remember that monitors' main purpose is to ensure mutual exclusion when accessing shared data. This implies that mutual exclusion is only required for routines that do in fact access shared data. However, since \code{dtype} polymorphism always handle incomplete types (by definition) no \code{dtype} polymorphic routine can access shared data since the data would require knowledge about the type. Therefore the only concern when combining \code{dtype} polymorphism and monitors is to protect access to routines. With callsite-locking, this would require significant amount of work since any \code{dtype} routine could have to obtain some lock before calling a routine. However, with entry-point-locking calling a monitor routine becomes exactly the same as calling it from anywhere else.
224
225\subsection{Internal scheduling} \label{insched}
226Monitors should also be able to schedule what threads access it as a mean of synchronization. Internal scheduling is one of the simple examples of such a feature. It allows users to declare condition variables and wait for them to be signaled. Here is a simple example of such a technique :
227
228\begin{lstlisting}
229        mutex struct A {
230                condition e;
231        }
232
233        void foo(A & mutex a) {
234                //...
235                wait(a.e);
236                //...
237        }
238
239        void bar(A & mutex a) {
240                signal(a.e);
241        }
242\end{lstlisting}
243
244Here routine \code{foo} waits on the \code{signal} from \code{bar} before making further progress, effectively ensuring a basic ordering. This semantic can easily be extended to multi-monitor calls by offering the same guarantee.
245
246\begin{center}
247\begin{tabular}{ c @{\hskip 0.65in} c }
249\begin{lstlisting}
250void foo(monitor & mutex a,
251         monitor & mutex b) {
252        //...
253        wait(a.e);
254        //...
255}
256
257foo(a, b);
258\end{lstlisting} &\begin{lstlisting}
259void bar(monitor & mutex a,
260         monitor & mutex b) {
261        signal(a.e);
262}
263
264
265
266bar(a, b);
267\end{lstlisting}
268\end{tabular}
269\end{center}
270
271A direct extension of the single monitor semantics would be to release all locks when waiting and transferring ownership of all locks when signalling. However, for the purpose of synchronization it may be usefull to only release some of the locks but keep others. On the technical side, partially releasing lock is feasible but from the user perspective a choice must be made for the syntax of this feature. It is possible to do without any extra syntax by relying on order of acquisition (Note that here the use of helper routines is irrelevant, only routines the acquire mutual exclusion have an impact on internal scheduling):
272
273\begin{center}
274\begin{tabular}{|c|c|c|}
275Context 1 & Context 2 & Context 3 \\
276\hline
277\begin{lstlisting}
278condition e;
279
280void foo(monitor & mutex a,
281         monitor & mutex b) {
282        wait(e);
283}
284
285
286
287
288
289
290foo(a,b);
291\end{lstlisting} &\begin{lstlisting}
292condition e;
293
294void bar(monitor & mutex a,
295         monitor & nomutex b) {
296        foo(a,b);
297}
298
299void foo(monitor & mutex a,
300         monitor & mutex b) {
301        wait(e);
302}
303
304bar(a, b);
305\end{lstlisting} &\begin{lstlisting}
306condition e;
307
308void bar(monitor & mutex a,
309         monitor & nomutex b) {
310        foo(a,b);
311}
312
313void baz(monitor & nomutex a,
314         monitor & mutex b) {
315        wait(e);
316}
317
318bar(a, b);
319\end{lstlisting}
320\end{tabular}
321\end{center}
322
323This can be interpreted in two different ways :
324\begin{flushleft}
325\begin{enumerate}
326        \item \code{wait} atomically releases the monitors acquired by the inner-most routine, \underline{ignoring} nested calls.
327        \item \code{wait} atomically releases the monitors acquired by the inner-most routine, \underline{considering} nested calls.
328\end{enumerate}
329\end{flushleft}
330While the difference between these two is subtle, it has a significant impact. In the first case it means that the calls to \code{foo} would behave the same in Context 1 and 2. This semantic would also mean that the call to \code{wait} in routine \code{baz} would only release \code{monitor b}. While this may seem intuitive with these examples, it does have one significant implication, it creates a strong distinction between acquiring multiple monitors in sequence and acquiring the same monitors simulatenously, i.e. :
331
332\begin{center}
333\begin{tabular}{c @{\hskip 0.35in} c @{\hskip 0.35in} c}
334\begin{lstlisting}
335enterMonitor(a);
336enterMonitor(b);
337// do stuff
338leaveMonitor(b);
339leaveMonitor(a);
340\end{lstlisting} & != &\begin{lstlisting}
341enterMonitor(a);
342enterMonitor(a, b);
343// do stuff
344leaveMonitor(a, b);
345leaveMonitor(a);
346\end{lstlisting}
347\end{tabular}
348\end{center}
349
350This is not intuitive because even if both methods display the same monitors state both inside and outside the critical section respectively, the behavior is different. Furthermore, the actual acquiring order will be exaclty the same since acquiring a monitor from inside its mutual exclusion is a no-op. This means that even if the data and the actual control flow are the same using both methods, the behavior of the \code{wait} will be different. The alternative is option 2, that is releasing acquired monitors, \underline{considering} nesting. This solves the issue of having the two acquiring method differ at the cost of making routine \code{foo} behave differently depending on from which context it is called (Context 1 or 2). Indeed in Context 2, routine \code{foo} actually behaves like routine \code{baz} rather than having the same behavior than in Context 1. The fact that both implicit approaches can be unintuitive depending on the perspective may be a sign that the explicit approach is superior. For this reason this \CFA does not support implicit monitor releasing and uses explicit semantics.
351\\
352
353The following examples shows three alternatives of explicit wait semantics :
354\\
355
356\begin{center}
357\begin{tabular}{|c|c|c|}
358Case 1 & Case 2 & Case 3 \\
359Branding on construction & Explicit release list & Explicit ignore list \\
360\hline
361\begin{lstlisting}
362void foo(monitor & mutex a,
363         monitor & mutex b,
364           condition & c)
365{
366        // Releases monitors
367        // branded in ctor
368        wait(c);
369}
370
371monitor a;
372monitor b;
373condition1 c1 = {a};
374condition2 c2 = {a, b};
375
376//Will release only a
377foo(a,b,c1);
378
379//Will release a and b
380foo(a,b,c2);
381\end{lstlisting} &\begin{lstlisting}
382void foo(monitor & mutex a,
383         monitor & mutex b,
384           condition & c)
385{
386        // Releases monitor a
387        // Holds monitor b
388        waitRelease(c, [a]);
389}
390
391monitor a;
392monitor b;
393condition c;
394
395
396
397foo(a,b,c);
398
399
400
401\end{lstlisting} &\begin{lstlisting}
402void foo(monitor & mutex a,
403         monitor & mutex b,
404           condition & c)
405{
406        // Releases monitor a
407        // Holds monitor b
408        waitHold(c, [b]);
409}
410
411monitor a;
412monitor b;
413condition c;
414
415
416
417foo(a,b,c);
418
419
420
421\end{lstlisting}
422\end{tabular}
423\end{center}
424(Note : Case 2 and 3 use tuple semantics to pass a variable length list of elements.)
425\\
426
427All these cases have their pros and cons. Case 1 is more distinct because it means programmers need to be carefull about where the condition is initialized as well as where it is used. On the other hand, it is very clear and explicitly states which monitor is released and which monitor stays acquired. This is similar to Case 2, which releases only the monitors explictly listed. However, in Case 2, calling the \code{wait} routine instead of the \code{waitRelease} routine releases all the acquired monitor. The Case 3 is an improvement on that since it releases all the monitors except those specified. The result is that the \code{wait} routine can be written as follows :
428\begin{lstlisting}
429void wait(condition & cond) {
430        waitHold(cond, []);
431}
432\end{lstlisting}
433This alternative offers nice and consistent behavior between \code{wait} and \code{waitHold}. However, one large pitfall is that mutual exclusion can now be violated by calls to library code. Indeed, even if the following example seems benign there is one significant problem :
434\begin{lstlisting}
435monitor global;
436
437extern void doStuff(); //uses global
438
439void foo(monitor & mutex m) {
440        //...
441        doStuff(); //warning can release monitor m
442        //...
443}
444
445foo(global);
446\end{lstlisting}
447
448Indeed, if Case 2 or 3 are chosen it any code can violate the mutual exclusion of the calling code by issuing calls to \code{wait} or \code{waitHold} in a nested monitor context. Case 2 can be salvaged by removing the \code{wait} routine from the API but Case 3 cannot prevent users from calling \code{waitHold(someCondition, [])}. For this reason the syntax proposed in Case 3 is rejected. Note that the syntax proposed in case 1 and 2 are not exclusive. Indeed, by supporting two types of condition both cases can be supported :
449\begin{lstlisting}
450struct condition { /*...*/ };
451
452// Second argument is a variable length tuple.
453void wait(condition & cond, [...] monitorsToRelease);
454void signal(condition & cond);
455
456struct conditionN { /*...*/ };
457
458void ?{}(conditionN* this, /*list of N monitors to release*/);
459void wait(conditionN & cond);
460void signal(conditionN & cond);
461\end{lstlisting}
462
463Regardless of the option chosen for wait semantics, signal must be symmetrical. In all cases, signal only needs a single parameter, the condition variable that needs to be signalled. But \code{signal} needs to be called from the same monitor(s) that call to \code{wait}. Otherwise, mutual exclusion cannot be properly transferred back to the waiting monitor.
464
465Finally, an additionnal semantic which can be very usefull is the \code{signalBlock} routine. This routine behaves like signal for all of the semantics discussed above, but with the subtelty that mutual exclusion is transferred to the waiting task immediately rather than wating for the end of the critical section.
466\\
467
468\subsection{External scheduling} \label{extsched}
469As one might expect, the alternative to Internal scheduling is to use External scheduling instead. This method is somewhat more robust to deadlocks since one of the threads keeps a relatively tight control on scheduling. Indeed, as the following examples will demonstrate, external scheduling allows users to wait for events from other threads without the concern of unrelated events occuring. External scheduling can generally be done either in terms of control flow (ex: \uC) or in terms of data (ex: Go). Of course, both of these paradigms have their own strenghts and weaknesses but for this project control flow semantics where chosen to stay consistent with the rest of the languages semantics. Two challenges specific to \CFA arise when trying to add external scheduling with loose object definitions and multi-monitor routines. The following example shows what a simple use \code{accept} versus \code{wait}/\code{signal} and its advantages.
470
471\begin{center}
472\begin{tabular}{|c|c|}
473Internal Scheduling & External Scheduling \\
474\hline
475\begin{lstlisting}
476        _Monitor blarg {
477                condition c;
478        public:
479                void f() { signal(c)}
480                void g() { wait(c); }
481        private:
482        }
483\end{lstlisting}&\begin{lstlisting}
484        _Monitor blarg {
485
486        public:
487                void f();
488                void g() { _Accept(f); }
489        private:
490        }
491\end{lstlisting}
492\end{tabular}
493\end{center}
494
495In the case of internal scheduling, the call to \code{wait} only guarantees that \code{g} was the last routine to access the monitor. This intails that the routine \code{f} may have acquired mutual exclusion several times while routine \code{h} was waiting. On the other hand, external scheduling guarantees that while routine \code{h} was waiting, no routine other than \code{g} could acquire the monitor.
496\\
497
498\subsubsection{Loose object definitions}
499In \uC, monitor declarations include an exhaustive list of monitor operations. Since \CFA is not object oriented it becomes both more difficult to implement but also less clear for the user :
500
501\begin{lstlisting}
502        mutex struct A {};
503
504        void f(A & mutex a);
505        void g(A & mutex a) { accept(f); }
506\end{lstlisting}
507
508However, external scheduling is an example where implementation constraints become visible from the interface. Indeed, ince there is no hard limit to the number of threads trying to acquire a monitor concurrently, performance is a significant concern. Here is the pseudo code for the entering phase of a monitor :
509
510\begin{center}
511\begin{tabular}{l}
512\begin{lstlisting}
513        ¶if¶ critical section is free :
514                enter
515        elif critical section accepts me :
516                enter
517        ¶else¶ :
518                block
519\end{lstlisting}
520\end{tabular}
521\end{center}
522
523For the \code{critical section is free} condition it is easy to implement a check that can evaluate the condition in a few instruction. However, a fast check for \code{critical section accepts me} is much harder to implement depending on the constraints put on the monitors. Indeed, monitors are often expressed as an entry queue and some acceptor queue as in the following figure :
524
525\begin{center}
526{\resizebox{0.5\textwidth}{!}{\input{monitor}}}
527\end{center}
528
529There are other alternatives to these pictures but in the case of this picture implementing a fast accept check is relatively easy. Indeed simply updating a bitmask when the acceptor queue changes is enough to have a check that executes in a single instruction, even with a fairly large number of acceptor. However, this requires all the acceptable routines to be declared with the monitor declaration. For OO languages this doesn't compromise much since monitors already have an exhaustive list of member routines. However, for \CFA this isn't the case, routines can be added to a type anywhere after its declaration. A more flexible
530
531
532At this point we must make a decision between flexibility and performance. Many design decisions in \CFA achieve both flexibility and performance, for example polymorphic routines add significant flexibility but inlining them means the optimizer can easily remove any runtime cost.
533
534This approach leads to the \uC example being translated to :
535\begin{lstlisting}
536        accept( void g(mutex struct A & mutex a) )
537        mutex struct A {};
538
539        void f(A & mutex a) { accept(g); }
540        void g(A & mutex a);
541\end{lstlisting}
542
543This syntax is the most consistent with the language since it somewhat mimics the \code{forall} declarations. However, the fact that it comes before the struct declaration does means the type needs to be forward declared (done inline in the example). Here are a few alternatives to this syntax : \\
544\begin{tabular}[t]{l l}
545Alternative 1 & Alternative 2 \\
546\begin{lstlisting}
547mutex struct A
548accept( void g(A & mutex a) )
549{};
550\end{lstlisting} &\begin{lstlisting}
551mutex struct A {}
552accept( void g(A & mutex a) );
553
554\end{lstlisting} \\
555Alternative 3 & Alternative 4 \\
556\begin{lstlisting}
557mutex struct A {
558        accept( void g(A & mutex a) )
559};
560
561\end{lstlisting} &\begin{lstlisting}
562mutex struct A {
563        accept :
564                void g(A & mutex a) );
565};
566\end{lstlisting}
567\end{tabular}
568
569
570An other aspect to consider is what happens if multiple overloads of the same routine are used. For the time being it is assumed that multiple overloads of the same routine should be scheduled regardless of the overload used. However, this could easily be extended in the future.
571
572\subsubsection{Multi-monitor scheduling}
573
574External scheduling, like internal scheduling, becomes orders of magnitude more complex when we start introducing multi-monitor syntax. Even in the simplest possible case some new semantics need to be established :
575\begin{lstlisting}
576        accept( void f(mutex struct A & mutex this))
577        mutex struct A {};
578
579        mutex struct B {};
580
581        void g(A & mutex a, B & mutex b) {
582                accept(f); //ambiguous, which monitor
583        }
584\end{lstlisting}
585
586The obvious solution is to specify the correct monitor as follows :
587
588\begin{lstlisting}
589        accept( void f(mutex struct A & mutex this))
590        mutex struct A {};
591
592        mutex struct B {};
593
594        void g(A & mutex a, B & mutex b) {
595                accept( f, b );
596        }
597\end{lstlisting}
598
599This is unambiguous. The both locks will be acquired and kept, when routine \code{f} is called the lock for monitor \code{a} will be temporarily transferred from \code{g} to \code{f} (while \code{g} still holds lock \code{b}). This behavior can be extended to multi-monitor accept statment as follows.
600
601\begin{lstlisting}
602        accept( void f(mutex struct A & mutex, mutex struct A & mutex))
603        mutex struct A {};
604
605        mutex struct B {};
606
607        void g(A & mutex a, B & mutex b) {
608                accept( f, b, a );
609        }
610\end{lstlisting}
611
612Note that the set of monitors passed to the \code{accept} statement must be entirely contained in the set of monitor already acquired in the routine. \code{accept} used in any other context is Undefined Behaviour.
613
614\subsubsection{Implementation Details: External scheduling queues}
615To support multi-monitor external scheduling means that some kind of entry-queues must be used that is aware of both monitors. However, acceptable routines must be aware of the entry queues which means they most be stored inside at least one of the monitors that will be acquired. This in turn adds the requirement a systematic algorithm of disambiguating which queue is relavant regardless of user ordering. The proposed algorithm is to fall back on monitors lock ordering and specify that the monitor that is acquired first is the lock with the relevant entry queue. This assumes that the lock acquiring order is static for the lifetime of all concerned objects gut that is a reasonnable contraint. This algorithm choice has two consequences, the ofthe highest priority monitor is no longer a true FIFO queue and the queue of the lowest priority monitor is both required and probably unused. The queue can no longer be a FIFO queue because instead of simply containing the waiting threads in order arrival, they also contain the second mutex. Therefore, another thread with the same highest priority monitor but a different lowest priority monitor may arrive first but enter the critical section after a thread with the correct pairing. Secondly, since it may not be known at compile time which monitor will be the lowest priority monitor, every monitor needs to have the correct queues even though it is probably that half the multi-monitor queues will go unused for the entire duration of the program.
616
617\subsection{Other concurrency tools}
618
619\section{Parallelism}
620Historically, computer performance was about processor speeds and instructions count. However, with heat dissipaction being an ever growing challenge, parallelism has become the new source of greatest performance \cite{Sutter05, Sutter05b}. In this decade, it is not longer reasonnable create high-performance application without caring about parallelism. Indeed, parallelism an important aspect of performance and more specifically throughput and hardware utilization. The lowest level approach parallelism is to use \glspl{kthread}. However since these have significant costs and limitations, \glspl{kthread} are now mostly used as an implementation tool rather than a user oriented one. There are several alternatives to solve these issues which all have strengths and weaknesses.
621
623A direct improvement on the \gls{kthread} approach is to use \glspl{uthread}. These threads offer most of the same features that the operating system already provide but can be used on a much larger scale. This is the most powerfull solution as it allows all the features of multi-threading while removing several of the more expensives costs of using kernel threads. The down side is that almost none of the low-level threading complexities are hidden, users still have to think about data races, deadlocks and synchronization issues. This can be somewhat alleviated by a concurrency toolkit with strong garantees but the parallelism toolkit offers very little to reduce complexity in itself.
624
627The opposite approach is to base parallelism on \glspl{job}. Indeed, \glspl{job} offer limited flexibility but at the benefit of a simpler user interface. In \gls{job} based systems users express parallelism as units of work and the dependency graph (either explicit or implicit) that tie them together. This means users need not to worry about concurrency but significantly limits the interaction that can occur between different jobs. Indeed, any \gls{job} that blocks also blocks the underlying \gls{kthread}, this effectively mean the CPU utilization, and therefore throughput, will suffer noticeably. The golden standard of this implementation is Intel's TBB library\cite{TBB}.
628
629\subsection{Fibers : user-level threads without preemption}
630Finally, in the middle of the flexibility versus complexity spectrum lay \glspl{fiber} which offer \glspl{uthread} without the complexity of preemption. This means users don't have to worry about other \glspl{fiber} suddenly executing between two instructions which signficantly reduces complexity. However, any call to IO or other concurrency primitives can lead to context switches. Furthermore, users can also block \glspl{fiber} in the middle of their execution without blocking a full processor core. This means users still have to worry about mutual exclusion, deadlocks and race conditions in their code, raising the complexity significantly.
631\cite{Go}
632
634While the choice between the three paradigms listed above may have significant performance implication, it is difficult to pin the performance implications of chosing a model at the language level. Indeed, in many situations own of these paradigms will show better performance but it all strongly depends on the usage. Having mostly indepent units of work to execute almost guarantess that the \gls{job} based system will have the best performance. However, add interactions between jobs and the processor utilisation might suffer. User-level threads may allow maximum ressource utilisation but context switches will be more expansive and it is also harder for users to get perfect tunning. As with every example, fibers sit somewhat in the middle of the spectrum. Furthermore, if the units of uninterrupted work are large enough the paradigm choice will be fully armoticised by the actual work done.
635
637As a system level language, \CFA should offer both performance and flexibilty as its primary goals, simplicity and user-friendliness being a secondary concern. Therefore, the core of parallelism in \CFA should prioritize power and efficiency. With this said, it is possible to deconstruct the three paradigms details aboved in order to get simple building blocks. Here is a table showing the core caracteristics of the mentionned paradigms :
638\begin{center}
639\begin{tabular}[t]{| r | c | c |}
640\cline{2-3}
641\multicolumn{1}{ c| }{} & Has a stack & Preemptive \\
642\hline
643\Glspl{job} & X & X \\
644\hline
645\Glspl{fiber} & \checkmark & X \\
646\hline
647\Glspl{uthread} & \checkmark & \checkmark \\
648\hline
649\end{tabular}
650\end{center}
651
652As shown in section \ref{cfaparadigms} these different blocks being available in \CFA it is trivial to reproduce any of these paradigm.
653
655The basic building blocks of \CFA are \glspl{cfathread}. By default these are implemented as \glspl{uthread} and as such offer a flexible and lightweight threading interface (lightweight comparatievely to \glspl{kthread}). A thread can be declared using a struct declaration prefix with the \code{thread} as follows :
656
657\begin{lstlisting}
659\end{lstlisting}
660
661Obviously, for this thread implementation to be usefull it must run some user code. Several other threading interfaces use some function pointer representation as the interface of threads (for example : \Csharp \cite{Csharp} and Scala \cite{Scala}). However, we consider that statically tying a \code{main} routine to a thread superseeds this approach. Since the \code{main} routine is definetely a special routine in \CFA, we can reuse the existing syntax for declaring routines with unordinary name, i.e. operator overloading. As such the \code{main} routine of a thread can be defined as such :
662\begin{lstlisting}
664
665        void ?main(thread foo* this) {
666                /*... Some useful code ...*/
667        }
668\end{lstlisting}
669
670With these semantics it is trivial to write a thread type that takes a function pointer as parameter and executes it on its stack asynchronously :
671\begin{lstlisting}
672        typedef void (*voidFunc)(void);
673
675                voidFunc func;
676        };
677
678        //ctor
679        void ?{}(thread FuncRunner* this, voidFunc inFunc) {
680                func = inFunc;
681        }
682
683        //main
684        void ?main(thread FuncRunner* this) {
685                this->func();
686        }
687\end{lstlisting}
688
689In this example \code{func} is a function pointer stored in \acrfull{tls}, which is \CFA is both easy to use and completly typesafe.
690
691Of course for threads to be useful, it must be possible to start and stop threads and wait for them to complete execution. While using \acrshort{api} such as \code{fork} and \code{join} is relatively common in the literature, such an interface is not needed. Indeed, the simplest approach is to use \acrshort{raii} principles and have threads \code{fork} once the constructor has completed and \code{join} before the destructor runs.
692\begin{lstlisting}
693thread struct FuncRunner; //FuncRunner declared above
694
695void world() {
696        sout | "World!" | endl;
697}
698
699void main() {
700        FuncRunner run = {world};
702
703        //Print to "Hello " and "World!" will be run concurrently
704        sout | "Hello " | endl;
705
706        //Implicit join at end of scope
707}
708\end{lstlisting}
709This semantic has several advantages over explicit semantics : typesafety is guaranteed, any thread will always be started and stopped exaclty once and users can't make any progamming errors. Furthermore it naturally follows the memory allocation semantics which means users don't need to learn multiple semantics.
710
711These semantics also naturally scale to multiple threads meaning basic synchronisation is very simple :
712\begin{lstlisting}
714                //...
715        };
716
717        //ctor
719
720        //main
722                //...
723        }
724
725        void foo() {
727                //Start 10 threads at the beginning of the scope
728
729                DoStuff();
730
731                //Wait for the 10 threads to finish
732        }
733\end{lstlisting}
734
735\subsection{The \CFA Kernel : Processors, Clusters and Threads}\label{kernel}
736
737
739Given these building blocks we can then reproduce the all three of the popular paradigms. Indeed, we get \glspl{uthread} as the default paradigm in \CFA. However, disabling \glspl{preemption} on the \gls{cfacluster} means \glspl{cfathread} effectively become \glspl{fiber}. Since several \glspl{cfacluster} with different scheduling policy can coexist in the same application, this allows \glspl{fiber} and \glspl{uthread} to coexist in the runtime of an application.
740
742%
744% constructors destructors
745%       initializer lists
746% monitors
747%
748% \subsubsection{Futures}
749%
751% Finally, simpler applications can benefit greatly from having implicit parallelism. That is, parallelism that does not rely on the user to write concurrency. This type of parallelism can be achieved both at the language level and at the system level.
752%
753% \begin{center}
754% \begin{tabular}[t]{|c|c|c|}
755% Sequential & System Parallel & Language Parallel \\
756% \begin{lstlisting}
757% void big_sum(int* a, int* b,
758%                int* out,
759%                size_t length)
760% {
761%       for(int i = 0; i < length; ++i ) {
762%               out[i] = a[i] + b[i];
763%       }
764% }
765%
766%
767%
768%
769%
770% int* a[10000];
771% int* b[10000];
772% int* c[10000];
773% //... fill in a and b ...
774% big_sum(a, b, c, 10000);
775% \end{lstlisting} &\begin{lstlisting}
776% void big_sum(int* a, int* b,
777%                int* out,
778%                size_t length)
779% {
780%       range ar(a, a + length);
781%       range br(b, b + length);
782%       range or(out, out + length);
783%       parfor( ai, bi, oi,
784%       [](int* ai, int* bi, int* oi) {
785%               oi = ai + bi;
786%       });
787% }
788%
789% int* a[10000];
790% int* b[10000];
791% int* c[10000];
792% //... fill in a and b ...
793% big_sum(a, b, c, 10000);
794% \end{lstlisting}&\begin{lstlisting}
795% void big_sum(int* a, int* b,
796%                int* out,
797%                size_t length)
798% {
799%       for (ai, bi, oi) in (a, b, out) {
800%               oi = ai + bi;
801%       }
802% }
803%
804%
805%
806%
807%
808% int* a[10000];
809% int* b[10000];
810% int* c[10000];
811% //... fill in a and b ...
812% big_sum(a, b, c, 10000);
813% \end{lstlisting}
814% \end{tabular}
815% \end{center}
816%
817% \subsection{Machine setup}\label{machine}
818% Threads are all good and well but wee still some OS support to fully utilize available hardware.
819%
820% \textbf{\large{Work in progress...}} Do wee need something beyond specifying the number of kernel threads?
821
822\section{Putting it all together}
823
824\section{Future work}
825Concurrency and parallelism is still a very active field that strongly benefits from hardware advances. As such certain features that aren't necessarily mature enough in their current state could become relevant in the lifetime of \CFA.
826\subsection{Transactions}
827
828\section*{Acknowledgements}
829
830\clearpage
831\printglossary[type=\acronymtype]
832\printglossary
833
834\clearpage
835\bibliographystyle{plain}
836\bibliography{pl,local}
837
838
839\end{document}
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