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Timestamp:
Nov 29, 2016, 3:30:59 PM (7 years ago)
Author:
Peter A. Buhr <pabuhr@…>
Branches:
ADT, aaron-thesis, arm-eh, ast-experimental, cleanup-dtors, deferred_resn, demangler, enum, forall-pointer-decay, jacob/cs343-translation, jenkins-sandbox, master, new-ast, new-ast-unique-expr, new-env, no_list, persistent-indexer, pthread-emulation, qualifiedEnum, resolv-new, with_gc
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3a2128f (diff), 9129a84 (diff)
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Merge branch 'master' of plg2:software/cfa/cfa-cc

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src/Parser/parser.cc

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  • doc/proposals/concurrency/concurrency.tex

    r3a2128f r1f44196  
    1414
    1515% Latex packages used in the document.
    16 \usepackage[T1]{fontenc}                                % allow Latin1 (extended ASCII) characters
     16\usepackage[T1]{fontenc}                                        % allow Latin1 (extended ASCII) characters
    1717\usepackage{textcomp}
    1818\usepackage[latin1]{inputenc}
    1919\usepackage{fullpage,times,comment}
    2020\usepackage{epic,eepic}
    21 \usepackage{upquote}                                                                    % switch curled `'" to straight
     21\usepackage{upquote}                                            % switch curled `'" to straight
    2222\usepackage{calc}
    2323\usepackage{xspace}
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    2525\usepackage{tabularx}
    2626\usepackage[acronym]{glossaries}
    27 \usepackage{varioref}                                                           % extended references
     27\usepackage{varioref}                                           % extended references
    2828\usepackage{inconsolata}
    29 \usepackage{listings}                                                                   % format program code
    30 \usepackage[flushmargin]{footmisc}                                              % support label/reference in footnote
    31 \usepackage{latexsym}                                   % \Box glyph
    32 \usepackage{mathptmx}                                   % better math font with "times"
     29\usepackage{listings}                                           % format program code
     30\usepackage[flushmargin]{footmisc}                              % support label/reference in footnote
     31\usepackage{latexsym}                                           % \Box glyph
     32\usepackage{mathptmx}                                           % better math font with "times"
    3333\usepackage[usenames]{color}
    3434\usepackage[pagewise]{lineno}
    3535\usepackage{fancyhdr}
    3636\renewcommand{\linenumberfont}{\scriptsize\sffamily}
    37 \input{common}                                          % bespoke macros used in the document
     37\input{style}                                                   % bespoke macros used in the document
    3838\usepackage[dvips,plainpages=false,pdfpagelabels,pdfpagemode=UseNone,colorlinks=true,pagebackref=true,linkcolor=blue,citecolor=blue,urlcolor=blue,pagebackref=true,breaklinks=true]{hyperref}
    3939\usepackage{breakurl}
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    4545
    46 \setlength{\topmargin}{-0.45in}                                                 % move running title into header
     46\setlength{\topmargin}{-0.45in}                         % move running title into header
    4747\setlength{\headsep}{0.25in}
    4848
    … …  
    8686\title{Concurrency in \CFA}
    8787\author{Thierry Delisle \\
    88 Dept. of Computer Science, University of Waterloo, \\ Waterloo, Ontario, Canada
     88School of Computer Science, University of Waterloo, \\ Waterloo, Ontario, Canada
    8989}
    9090
    … …  
    100100
    101101\section{Introduction}
    102 This 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.
    103 Indeed, for highly productive parallel programming high-level approaches are much more popular\cite{HPP:Study}. Examples are task based parallelism, message passing, implicit threading.
    104 
    105 There 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.
     102This proposal provides a minimal core concurrency API that is both simple, efficient and can be reused to build higher-level features. The simplest possible concurrency 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 constructs as the basis of the concurrency in \CFA. Indeed, for highly productive parallel programming, high-level approaches are much more popular~\cite{HPP:Study}. Examples are task based, message passing and implicit threading.
     103
     104There are actually two problems that need to be solved in the design of the concurrency for a programming 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.
    106105
    107106%  #####  ####### #     #  #####  #     # ######  ######  ####### #     #  #####  #     #
    … …  
    114113
    115114\section{Concurrency}
    116 % Several 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.
    117 % \\
    118 
    119 Several 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\cite{Thoth,Harmony,V-Kernel} 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. Many such mechanisms have been proposed, including semaphores~\cite{Dijkstra68b} and path expressions~\cite{Campbell74}. However, for productivity reasons it is desireable to have a higher-level construct to be the core concurrency paradigm\cite{HPP:Study}. One of the most natural, elegant, and efficient mechanisms for synchronization and communication, especially for shared memory systems, is the \emph{monitor}.
    120 
    121 Monitors were first proposed by Brinch Hansen~\cite{Hansen73} and later described and extended by C.A.R.~Hoare~\cite{Hoare74}.
    122 Many programming languages---e.g., 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}---provide monitors as explicit language constructs. In addition, operating-system kernels and device drivers have a monitor-like structure, although they often use lower-level primitives such as semaphores or locks to simulate monitors. For these reasons, this project proposes Monitors as the core concurrency construct.
    123 \\
    124 
    125 Finally, 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.
     115Several 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 relies on message passing~\cite{Thoth,Harmony,V-Kernel} or other paradigms that closely relate to networking concepts (channels\cit for example). However, in languages that use routine calls as their core abstraction mechanism, these approaches force a clear distinction between concurrent and non-concurrent paradigms (i.e., message passing versus routine call). Which in turn means that, in order to be effective, programmers need to learn two sets of designs patterns. This distinction can be hidden away in library code, but effective use of the librairy still has to take both paradigms into account. Approaches based on shared memory are more closely related to non-concurrent paradigms since they often rely on basic constructs like routine calls and objects. At a lower level these can be implemented as locks and atomic operations. Many such mechanisms have been proposed, including semaphores~\cite{Dijkstra68b} and path expressions~\cite{Campbell74}. However, for productivity reasons it is desireable to have a higher-level construct be the core concurrency paradigm~\cite{HPP:Study}. An approach that is worth mentionning because it is gaining in popularity is transactionnal memory~\cite{Dice10}[Check citation]. While this approach is even pursued by system languages like \CC\cit, the performance and feature set is currently too restrictive 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. One of the most natural, elegant, and efficient mechanisms for synchronization and communication, especially for shared memory systems, is the \emph{monitor}. Monitors were first proposed by Brinch Hansen~\cite{Hansen73} and later described and extended by C.A.R.~Hoare~\cite{Hoare74}. Many programming languages---e.g., 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}---provide monitors as explicit language constructs. In addition, operating-system kernels and device drivers have a monitor-like structure, although they often use lower-level primitives such as semaphores or locks to simulate monitors. For these reasons, this project proposes monitors as the core concurrency construct.
    126116
    127117% #     # ####### #     # ### ####### ####### ######   #####
    … …  
    134124
    135125\subsection{Monitors}
    136 A 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 :
     126A 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 :
    137127\begin{lstlisting}
    138128        typedef /*some monitor type*/ monitor;
    … …  
    154144
    155145\subsubsection{Call semantics} \label{call}
    156 The 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.
    157 \\
    158 
    159 Another 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 :
    160 
    161 \begin{lstlisting}
    162         mutex struct counter_t { /*...*/ };
    163 
    164         void ?{}(counter_t & nomutex this);
    165         int ++?(counter_t & mutex this);
    166         void ?{}(Int * this, counter_t & mutex cnt);
    167 \end{lstlisting}
    168 *semantics of the declaration of \code{mutex struct counter_t} are discussed in details in section \ref{data}
    169 \\
    170 
    171 This 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
    172 uses \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.
    173 \\
    174 
    175 Having 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.
    176 \\
    177 
    178 Regardless of which keyword is kept, it is important to establish when mutex/nomutex may be used depending on type parameters.
     146The above monitor example displays some of the intrinsic characteristics. 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.
     147
     148Another aspect to consider is when a monitor acquires its mutual exclusion. For example, a monitor may need to be passed through multiple helper routines that do not acquire the monitor mutual-exclusion on entry. Pass through can be both generic helper routines (\code{swap}, \code{sort}, etc.) or specific helper routines like the following to implement an atomic counter :
     149
     150\begin{lstlisting}
     151        mutex struct counter_t { /*...see section §\ref{data}§...*/ };
     152
     153        void ?{}(counter_t & nomutex this); //constructor
     154        size_t ++?(counter_t & mutex this); //increment
     155
     156        //need for mutex is platform dependent here
     157        void ?{}(size_t * this, counter_t & mutex cnt); //conversion
     158\end{lstlisting}
     159
     160Here, the constructor(\code{?\{\}}) uses the \code{nomutex} keyword to signify that it does not acquire the monitor mutual exclusion when constructing. This semantics is because an object not yet constructed should never be shared and therefore does not require mutual exclusion. The prefix increment operator uses \code{mutex} to protect the incrementing process from race conditions. Finally, there is a conversion operator from \code{counter_t} to \code{size_t}. This conversion may or may not require the \code{mutex} key word depending on whether or not reading an \code{size_t} is an atomic operation or not.
     161
     162Having 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. For example, given a routine without wualifiers \code{void foo(counter_t & this)} then one could argue that it should 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 \code{nomutex} is the more "normal" behaviour, the \code{nomutex} keyword effectively stating explicitly that "this routine has nothing special". Another alternative is to make having exactly 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 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 is used for clarity.
     163
     164The next semantic decision is to establish when mutex/nomutex may be used as a type qualifier. Consider the following declarations:
    179165\begin{lstlisting}
    180166        int f1(monitor & mutex m);
    … …  
    184170        int f5(graph(monitor*) & mutex m);
    185171\end{lstlisting}
    186 
    187 The 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).
     172The problem is to indentify which object(s) should be acquired. Furthermore, each object needs to be acquired only once. In the case of simple routines like \code{f1} and \code{f2} it is easy to identify an exhaustive list of objects to acquire on entry. Adding indirections (\code{f3}) still allows the compiler and programmer to indentify which object is acquired. However, adding in arrays (\code{f4}) makes it much harder. Array lengths are not 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 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). Also note that while routine \code{f3} can be supported, meaning that monitor \code{**m} is be acquired, passing an array to this routine would be type safe and yet result in undefined behavior because only the first element of the array is acquired. However, this ambiguity is part of the C type system with respects to arrays. For this reason, it would also be reasonnable to disallow mutex in the context where arrays may be passed.
    188173
    189174% ######     #    #######    #
    … …  
    196181
    197182\subsubsection{Data semantics} \label{data}
    198 Once 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}:
     183Once 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 appropriate protection. For example, here is a complete version of the counter showed in section \ref{call}:
    199184\begin{lstlisting}
    200185        mutex struct counter_t {
    … …  
    207192
    208193        int ++?(counter_t & mutex this) {
    209                 return ++this->value;
    210         }
    211 
     194                return ++this.value;
     195        }
     196
     197        //need for mutex is platform dependent here
    212198        void ?{}(int * this, counter_t & mutex cnt) {
    213199                *this = (int)cnt;
    214200        }
    215201\end{lstlisting}
    216 \begin{tabular}{ c c }
    217 Thread 1 & Thread 2 \\
    218 \begin{lstlisting}
    219         void f(counter_t & mutex c) {
    220                 for(;;) {
    221                         sout | (int)c | endl;
    222                 }
    223         }
    224 \end{lstlisting} &\begin{lstlisting}
    225         void g(counter_t & mutex c) {
    226                 for(;;) {
    227                         ++c;
    228                 }
    229         }
    230 
     202
     203This simple counter is used as follows:
     204\begin{center}
     205\begin{tabular}{c @{\hskip 0.35in} c @{\hskip 0.35in} c}
     206\begin{lstlisting}
     207        //shared counter
     208        counter_t cnt;
     209
     210        //multiple threads access counter
     211        thread 1 : cnt++;
     212        thread 2 : cnt++;
     213        thread 3 : cnt++;
     214          ...
     215        thread N : cnt++;
    231216\end{lstlisting}
    232217\end{tabular}
    233 \\
    234 
    235 
    236 This 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. \\
    237 
    238 These simple mutual exclusion semantics also naturally expand to multi-monitor calls.
     218\end{center}
     219
     220Notice how the counter is used without any explicit synchronisation and yet supports thread-safe semantics for both reading and writting. Unlike object-oriented monitors, where calling a mutex member \emph{implicitly} acquires mutual-exclusion, \CFA uses an explicit mechanism to acquire mutual-exclusion. A consequence of this approach is that it extends to multi-monitor calls.
    239221\begin{lstlisting}
    240222        int f(MonitorA & mutex a, MonitorB & mutex b);
    … …  
    244226        f(a,b);
    245227\end{lstlisting}
    246 
    247 This 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 :
    248 \begin{lstlisting}
    249         void foo(A & mutex a, B & mutex a) {
    250                 //...
    251         }
    252 
    253         void bar(A & mutex a, B & nomutex a)
    254                 //...
    255                 foo(a, b);
    256                 //...
    257         }
    258 
    259         void baz(A & nomutex a, B & mutex a)
    260                 //...
    261                 foo(a, b);
    262                 //...
    263         }
    264 \end{lstlisting}
    265 
    266 Recursive 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.
     228This code acquires both locks before entering the critical section, called \emph{\gls{group-acquire}}. In practice, writing multi-locking routines that do not lead to deadlocks is tricky. Having language support for such a feature is therefore a significant asset for \CFA. In the case presented above, \CFA guarantees that the order of aquisition is consistent across calls to routines using the same monitors as arguments. However, since \CFA monitors use multi-acquisition locks, users can effectively force the acquiring order. For example, notice which routines use \code{mutex}/\code{nomutex} and how this affects aquiring order :
     229\begin{lstlisting}
     230        void foo(A & mutex a, B & mutex b) { //acquire a & b
     231                //...
     232        }
     233
     234        void bar(A & mutex a, B & nomutex b) { //acquire a
     235                //...
     236                foo(a, b); //acquire b
     237                //...
     238        }
     239
     240        void baz(A & nomutex a, B & mutex b) { //acquire b
     241                //...
     242                foo(a, b); //acquire a
     243                //...
     244        }
     245\end{lstlisting}
     246
     247The multi-acquisition monitor lock allows a monitor lock to be acquired by both \code{bar} or \code{baz} and acquired again in \code{foo}. In the calls to \code{bar} and \code{baz} the monitors are acquired in opposite order. such use leads to nested monitor call problems~\cite{Lister77}, which is a specific implementation of the lock acquiring order problem. In the example above, the user uses implicit ordering in the case of function \code{foo} but explicit ordering in the case of \code{bar} and \code{baz}. This subtle mistake means that calling these routines concurrently may lead to deadlock and is therefore undefined behavior. As shown on several occasion\cit, solving this problem requires :
     248\begin{enumerate}
     249        \item Dynamically tracking of the monitor-call order.
     250        \item Implement rollback semantics.
     251\end{enumerate}
     252
     253While the first requirement is already a significant constraint on the system, implementing a general rollback semantics in a C-like language is prohibitively complex \cit. In \CFA, users simply need to be carefull when acquiring multiple monitors at the same time.
    267254
    268255% ######  ####### #######    #    ### #        #####
    … …  
    283270
    284271\subsubsection{Implementation Details: Interaction with polymorphism}
    285 At 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.
    286 
    287 First 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.
     272At first glance, interaction between monitors and \CFA's concept of polymorphism seems complex to support. However, it is shown that entry-point locking can solve most of the issues.
     273
     274Before looking into complex control flow, it is important to present the difference between the two acquiring options : \gls{callsite-locking} and \gls{entry-point-locking}, i.e. acquiring the monitors before making a mutex call or as the first instruction of the mutex call. For example:
     275
     276\begin{center}
     277\begin{tabular}{|c|c|c|}
     278Code & \gls{callsite-locking} & \gls{entry-point-locking} \\
     279\CFA & pseudo-code & pseudo-code \\
     280\hline
     281\begin{lstlisting}
     282void foo(monitor & mutex a) {
     283
     284
     285
     286        //Do Work
     287        //...
     288
     289}
     290
     291void main() {
     292        monitor a;
     293
     294
     295
     296        foo(a);
     297
     298}
     299\end{lstlisting} &\begin{lstlisting}
     300foo(& a) {
     301
     302
     303
     304        //Do Work
     305        //...
     306
     307}
     308
     309main() {
     310        monitor a;
     311        //calling routine
     312        //handles concurrency
     313        acquire(a);
     314        foo(a);
     315        release(a);
     316}
     317\end{lstlisting} &\begin{lstlisting}
     318foo(& a) {
     319        //called routine
     320        //handles concurrency
     321        acquire(a);
     322        //Do Work
     323        //...
     324        release(a);
     325}
     326
     327main() {
     328        monitor a;
     329
     330
     331
     332        foo(a);
     333
     334}
     335\end{lstlisting}
     336\end{tabular}
     337\end{center}
     338
     339First 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. Since a monitor's 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 handles incomplete types (by definition), no \code{dtype} polymorphic routine can access shared data since the data requires knowledge about the type. Therefore, the only concern when combining \code{dtype} polymorphism and monitors is to protect access to routines. \Gls{callsite-locking} would require a significant amount of work, since any \code{dtype} routine may have to obtain some lock before calling a routine, depending on whether or not the type passed is a monitor. However, with \gls{entry-point-locking} calling a monitor routine becomes exactly the same as calling it from anywhere else.
     340
     341
    288342
    289343% ### #     # #######         #####   #####  #     # ####### ######
    … …  
    296350
    297351\subsection{Internal scheduling} \label{insched}
    298 Monitors 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 :
     352Monitors also need to schedule waiting threads internally 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 have threads wait and signaled from them. Here is a simple example of such a technique :
    299353
    300354\begin{lstlisting}
    … …  
    314368\end{lstlisting}
    315369
    316 Here 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.
    317 
     370Note that in \CFA, \code{condition} have no particular need to be stored inside a monitor, beyond any software engineering reasons. Here routine \code{foo} waits for 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.
    318371\begin{center}
    319372\begin{tabular}{ c @{\hskip 0.65in} c }
    … …  
    321374\begin{lstlisting}
    322375void foo(monitor & mutex a,
    323          monitor & mutex b) {
     376           monitor & mutex b) {
    324377        //...
    325378        wait(a.e);
    … …  
    330383\end{lstlisting} &\begin{lstlisting}
    331384void bar(monitor & mutex a,
    332          monitor & mutex b) {
     385           monitor & mutex b) {
    333386        signal(a.e);
    334387}
    … …  
    340393\end{tabular}
    341394\end{center}
    342 
    343 A 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):
     395A direct extension of the single monitor semantics is 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. It is possible to support internal scheduling and \gls{group-acquire} without any extra syntax by relying on order of acquisition. Here is an example of the different contexts in which internal scheduling can be used. (Note that here the use of helper routines is irrelevant, only routines acquire mutual exclusion have an impact on internal scheduling):
    344396
    345397\begin{center}
    … …  
    350402condition e;
    351403
     404//acquire a & b
    352405void foo(monitor & mutex a,
    353          monitor & mutex b) {
    354         wait(e);
     406           monitor & mutex b) {
     407
     408        wait(e); //release a & b
    355409}
    356410
    … …  
    364418condition e;
    365419
     420//acquire a
    366421void bar(monitor & mutex a,
    367          monitor & nomutex b) {
     422           monitor & nomutex b) {
    368423        foo(a,b);
    369424}
    370425
     426//acquire a & b
    371427void foo(monitor & mutex a,
    372          monitor & mutex b) {
    373         wait(e);
     428           monitor & mutex b) {
     429        wait(e);  //release a & b
    374430}
    375431
    … …  
    378434condition e;
    379435
     436//acquire a
    380437void bar(monitor & mutex a,
    381          monitor & nomutex b) {
    382         foo(a,b);
    383 }
    384 
     438           monitor & nomutex b) {
     439        baz(a,b);
     440}
     441
     442//acquire b
    385443void baz(monitor & nomutex a,
    386          monitor & mutex b) {
    387         wait(e);
     444           monitor & mutex b) {
     445        wait(e);  //release b
    388446}
    389447
    … …  
    393451\end{center}
    394452
    395 This can be interpreted in two different ways :
    396 \begin{flushleft}
    397 \begin{enumerate}
    398         \item \code{wait} atomically releases the monitors acquired by the inner-most routine, \underline{ignoring} nested calls.
    399         \item \code{wait} atomically releases the monitors acquired by the inner-most routine, \underline{considering} nested calls.
    400 \end{enumerate}
    401 \end{flushleft}
    402 While 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. :
     453Context 1 is the simplest way of acquiring more than one monitor (\gls{group-acquire}), using a routine with multiple parameters having the \code{mutex} keyword. Context 2 also uses \gls{group-acquire} as well in routine \code{foo}. However, the routine is called by routine \code{bar}, which only acquires monitor \code{a}. Since monitors can be acquired multiple times this does not cause a deadlock by itself but it does force the acquiring order to \code{a} then \code{b}. Context 3 also forces the acquiring order to be \code{a} then \code{b} but does not use \gls{group-acquire}. The previous example tries to illustrate the semantics that must be established to support releasing monitors in a \code{wait} statement. In all cases, the behavior of the wait statment is to release all the locks that were acquired my the inner-most monitor call. That is \code{a & b} in context 1 and 2 and \code{b} only in context 3. Here are a few other examples of this behavior.
     454
    403455
    404456\begin{center}
    405 \begin{tabular}{c @{\hskip 0.35in} c @{\hskip 0.35in} c}
    406 \begin{lstlisting}
    407 enterMonitor(a);
    408 enterMonitor(b);
    409 // do stuff
    410 leaveMonitor(b);
    411 leaveMonitor(a);
    412 \end{lstlisting} & != &\begin{lstlisting}
    413 enterMonitor(a);
    414 enterMonitor(a, b);
    415 // do stuff
    416 leaveMonitor(a, b);
    417 leaveMonitor(a);
     457\begin{tabular}{|c|c|c|}
     458\begin{lstlisting}
     459condition e;
     460
     461//acquire b
     462void foo(monitor & nomutex a,
     463           monitor & mutex b) {
     464        bar(a,b);
     465}
     466
     467//acquire a
     468void bar(monitor & mutex a,
     469           monitor & nomutex b) {
     470
     471        wait(e); //release a
     472                  //keep b
     473}
     474
     475foo(a, b);
     476\end{lstlisting} &\begin{lstlisting}
     477condition e;
     478
     479//acquire a & b
     480void foo(monitor & mutex a,
     481           monitor & mutex b) {
     482        bar(a,b);
     483}
     484
     485//acquire b
     486void bar(monitor & mutex a,
     487           monitor & nomutex b) {
     488
     489        wait(e); //release b
     490                  //keep a
     491}
     492
     493foo(a, b);
     494\end{lstlisting} &\begin{lstlisting}
     495condition e;
     496
     497//acquire a & b
     498void foo(monitor & mutex a,
     499           monitor & mutex b) {
     500        bar(a,b);
     501}
     502
     503//acquire none
     504void bar(monitor & nomutex a,
     505           monitor & nomutex b) {
     506
     507        wait(e); //release a & b
     508                  //keep none
     509}
     510
     511foo(a, b);
    418512\end{lstlisting}
    419513\end{tabular}
    420514\end{center}
    421 
    422 This 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.
    423 \\
    424 
    425 The following examples shows three alternatives of explicit wait semantics :
    426 \\
    427 
     515Note the right-most example is actually a trick pulled on the reader. Monitor state information is stored in thread local storage rather then in the routine context, which means that helper routines and other \code{nomutex} routines are invisible to the runtime system in regards to concurrency. This means that in the right-most example, the routine parameters are completly unnecessary. However, calling this routine from outside a valid monitor context is undefined.
     516
     517These semantics imply that in order to release of subset of the monitors currently held, users must write (and name) a routine that only acquires the desired subset and simply calls wait. While users can use this method, \CFA offers the \code{wait_release}\footnote{Not sure if an overload of \code{wait} would work...} which will release only the specified monitors. In the center previous examples, the code in the center uses the \code{bar} routine to only release monitor \code{b}. Using the \code{wait_release} helper, this can be rewritten without having the name two routines :
    428518\begin{center}
    429 \begin{tabular}{|c|c|c|}
    430 Case 1 & Case 2 & Case 3 \\
    431 Branding on construction & Explicit release list & Explicit ignore list \\
    432 \hline
    433 \begin{lstlisting}
    434 void foo(monitor & mutex a,
    435          monitor & mutex b,
    436            condition & c)
    437 {
    438         // Releases monitors
    439         // branded in ctor
    440         wait(c);
    441 }
    442 
    443 monitor a;
    444 monitor b;
    445 condition1 c1 = {a};
    446 condition2 c2 = {a, b};
    447 
    448 //Will release only a
    449 foo(a,b,c1);
    450 
    451 //Will release a and b
    452 foo(a,b,c2);
     519\begin{tabular}{ c c c }
     520\begin{lstlisting}
     521        condition e;
     522
     523        //acquire a & b
     524        void foo(monitor & mutex a,
     525                   monitor & mutex b) {
     526                bar(a,b);
     527        }
     528
     529        //acquire b
     530        void bar(monitor & mutex a,
     531                   monitor & nomutex b) {
     532
     533                wait(e); //release b
     534                          //keep a
     535        }
     536
     537        foo(a, b);
    453538\end{lstlisting} &\begin{lstlisting}
    454 void foo(monitor & mutex a,
    455          monitor & mutex b,
    456            condition & c)
    457 {
    458         // Releases monitor a
    459         // Holds monitor b
    460         waitRelease(c, [a]);
    461 }
    462 
    463 monitor a;
    464 monitor b;
    465 condition c;
    466 
    467 
    468 
    469 foo(a,b,c);
    470 
    471 
    472 
     539        =>
    473540\end{lstlisting} &\begin{lstlisting}
    474 void foo(monitor & mutex a,
    475          monitor & mutex b,
    476            condition & c)
    477 {
    478         // Releases monitor a
    479         // Holds monitor b
    480         waitHold(c, [b]);
    481 }
    482 
    483 monitor a;
    484 monitor b;
    485 condition c;
    486 
    487 
    488 
    489 foo(a,b,c);
    490 
    491 
    492 
     541        condition e;
     542
     543        //acquire a & b
     544        void foo(monitor & mutex a,
     545                   monitor & mutex b) {
     546                wait_release(e,b); //release b
     547                                         //keep a
     548        }
     549
     550        foo(a, b);
    493551\end{lstlisting}
    494552\end{tabular}
    495553\end{center}
    496 (Note : Case 2 and 3 use tuple semantics to pass a variable length list of elements.)
    497 \\
    498 
    499 All 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 :
    500 \begin{lstlisting}
    501 void wait(condition & cond) {
    502         waitHold(cond, []);
    503 }
    504 \end{lstlisting}
    505 This 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 :
    506 \begin{lstlisting}
    507 monitor global;
    508 
    509 extern void doStuff(); //uses global
    510 
    511 void foo(monitor & mutex m) {
    512         //...
    513         doStuff(); //warning can release monitor m
    514         //...
    515 }
    516 
    517 foo(global);
    518 \end{lstlisting}
    519 
    520 Indeed, 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 :
    521 \begin{lstlisting}
    522 struct condition { /*...*/ };
    523 
    524 // Second argument is a variable length tuple.
    525 void wait(condition & cond, [...] monitorsToRelease);
    526 void signal(condition & cond);
    527 
    528 struct conditionN { /*...*/ };
    529 
    530 void ?{}(conditionN* this, /*list of N monitors to release*/);
    531 void wait(conditionN & cond);
    532 void signal(conditionN & cond);
    533 \end{lstlisting}
    534 
    535 Regardless 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.
    536 
    537 Finally, 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.
     554
     555Regardless of the context in which the \code{wait} statement is used, \code{signal} must be called holding the same set of monitors. 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.
     556
     557Finally, an additional semantic which can be very usefull is the \code{signal_block} 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.
    538558\\
    539559
    … …  
    545565% #        #   #     #    ###    #     # #     # #     # #       #     #
    546566% ####### #     #    #    ###     #####   #####  #     # ####### ######
    547 
     567\newpage
    548568\subsection{External scheduling} \label{extsched}
    549 As 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.
     569An alternative to internal scheduling is to use external scheduling instead. This method is more constrained and explicit which may help users tone down the undeterministic nature of concurrency. Indeed, as the following examples demonstrates, 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 a simple use \code{accept} versus \code{wait}/\code{signal} and its advantages.
    550570
    551571\begin{center}
    … …  
    565585
    566586        public:
    567                 void f();
     587                void f() { /*...*/ }
    568588                void g() { _Accept(f); }
    569589        private:
    … …  
    573593\end{center}
    574594
    575 In 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.
     595In the case of internal scheduling, the call to \code{wait} only guarantees that \code{g} is 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.
    576596\\
    577597
    … …  
    756776% #       #     # #     # #     # ####### ####### ####### ####### ###  #####  #     #
    757777\section{Parallelism}
    758 Historically, computer performance was about processor speeds and instructions count. However, with heat dissipation 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 to create high-performance application without caring about parallelism. Indeed, parallelism is an important aspect of performance and more specifically throughput and hardware utilization. The lowest level approach of 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.
     778Historically, computer performance was about processor speeds and instructions count. However, with heat dissipation being a direct consequence of speed increase, parallelism has become the new source for increased performance~\cite{Sutter05, Sutter05b}. In this decade, it is not longer reasonnable to create a high-performance application without caring about parallelism. Indeed, parallelism is an important aspect of performance and more specifically throughput and hardware utilization. The lowest-level approach of parallelism is to use \glspl{kthread} in combination with semantics like \code{fork}, \code{join}, etc. 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 that all have strengths and weaknesses. While there are many variations of the presented paradigms, most of these variations do not actually change the guarantees or the semantics, they simply move costs in order to achieve better performance for certain workloads.
    759779
    760780\subsection{User-level threads}
    761 A 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.
    762 
    763 Examples of languages that support are Java\cite{Java}, Haskell\cite{Haskell} and \uC\cite{uC++book}.
     781A 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 approach 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 problems are hidden, users still have to think about data races, deadlocks and synchronization issues. These issues can be somewhat alleviated by a concurrency toolkit with strong garantees but the parallelism toolkit offers very little to reduce complexity in itself.
     782
     783Examples of languages that support \glspl{uthread} are Erlang~\cite{Erlang} and \uC~\cite{uC++book}.
     784
     785\subsubsection{Fibers : user-level threads without preemption}
     786A popular varient of \glspl{uthread} is what is often reffered to as \glspl{fiber}. However, \glspl{fiber} do not present meaningful semantical differences with \glspl{uthread}. Advocates of \glspl{fiber} list their high performance and ease of implementation as majors strenghts of \glspl{fiber} but the performance difference between \glspl{uthread} and \glspl{fiber} is controversial and the ease of implementation, while true, is a weak argument in the context of language design. Therefore this proposal largely ignore fibers.
     787
     788An example of a language that uses fibers is Go~\cite{Go}
    764789
    765790\subsection{Jobs and thread pools}
    766 The approach on the opposite end of the spectrum 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.
    767 The golden standard of this implementation is Intel's TBB library\cite{TBB}.
    768 
    769 \subsection{Fibers : user-level threads without preemption}
    770 Finally, 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.
    771 An example of a language that uses fibers is Go\cite{Go}
     791The approach on the opposite end of the spectrum is to base parallelism on \glspl{pool}. Indeed, \glspl{pool} offer limited flexibility but at the benefit of a simpler user interface. In \gls{pool} based systems, users express parallelism as units of work and a dependency graph (either explicit or implicit) that tie them together. This approach means users need not worry about concurrency but significantly limits the interaction that can occur among jobs. Indeed, any \gls{job} that blocks also blocks the underlying worker, which effectively means the CPU utilization, and therefore throughput, suffers noticeably. It can be argued that a solution to this problem is to use more workers than available cores. However, unless the number of jobs and the number of workers are comparable, having a significant amount of blocked jobs always results in idles cores.
     792
     793The gold standard of this implementation is Intel's TBB library~\cite{TBB}.
    772794
    773795\subsection{Paradigm performance}
    774 While 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 one 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 largely amorticised by the actual work done.
     796While the choice between the three paradigms listed above may have significant performance implication, it is difficult to pindown the performance implications of chosing a model at the language level. Indeed, in many situations one of these paradigms may show better performance but it all strongly depends on the workload. Having a large amount of mostly independent units of work to execute almost guarantess that the \gls{pool} based system has the best performance thanks to the lower memory overhead. However, interactions between jobs can easily exacerbate contention. User-level threads allow fine-grain context switching, which results in better resource utilisation, but context switches will be more expansive and the extra control means users need to tweak more variables to get the desired performance. Furthermore, if the units of uninterrupted work are large enough the paradigm choice is largely amorticised by the actual work done.
    775797
    776798%  #####  #######    #          ####### ######  ######
    … …  
    783805
    784806\section{\CFA 's Thread Building Blocks}
    785 As 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 :
    786 \begin{center}
    787 \begin{tabular}[t]{| r | c | c |}
    788 \cline{2-3}
    789 \multicolumn{1}{ c| }{} & Has a stack & Preemptive \\
    790 \hline
    791 \Glspl{job} & X & X \\
    792 \hline
    793 \Glspl{fiber} & \checkmark & X \\
    794 \hline
    795 \Glspl{uthread} & \checkmark & \checkmark \\
    796 \hline
    797 \end{tabular}
    798 \end{center}
    799 
    800 As shown in section \ref{cfaparadigms} these different blocks being available in \CFA it is trivial to reproduce any of these paradigm.
     807As 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, deconstructing popular paradigms in order to get simple building blocks yields \glspl{uthread} as the core parallelism block. \Glspl{pool} and other parallelism paradigms can then be built on top of the underlying threading model.
    801808
    802809% ####### #     # ######  #######    #    ######   #####
    … …  
    809816
    810817\subsection{Thread Interface}
    811 The 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 :
     818The 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 compared to \glspl{kthread}). A thread can be declared using a struct declaration with prefix \code{thread} as follows :
    812819
    813820\begin{lstlisting}
    … …  
    815822\end{lstlisting}
    816823
    817 Obviously, 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 :
     824Obviously, for this thread implementation to be usefull it must run some user code. Several other threading interfaces use a function-pointer representation as the interface of threads (for example : \Csharp~\cite{Csharp} and Scala~\cite{Scala}). However, this proposal considers that statically tying a \code{main} routine to a thread superseeds this approach. Since the \code{main} routine is already a special routine in \CFA (where the program begins), the existing syntax for declaring routines names with special semantics can be extended, i.e. operator overloading. As such the \code{main} routine of a thread can be defined as :
    818825\begin{lstlisting}
    819826        thread struct foo {};
    820827
    821         void ?main(thread foo* this) {
    822                 /*... Some useful code ...*/
    823         }
    824 \end{lstlisting}
    825 
    826 With these semantics it is trivial to write a thread type that takes a function pointer as parameter and executes it on its stack asynchronously :
     828        void ?main(foo* this) {
     829                sout | "Hello World!" | endl;
     830        }
     831\end{lstlisting}
     832
     833In this example, threads of type \code{foo} will start there execution in the \code{void ?main(foo*)} routine which in this case prints \code{"Hello World!"}. While this proposoal encourages this approach which is enforces strongly type programming. Users may prefer to use the routine based thread semantics for the sake of simplicity. With these semantics it is trivial to write a thread type that takes a function pointer as parameter and executes it on its stack asynchronously :
    827834\begin{lstlisting}
    828835        typedef void (*voidFunc)(void);
    … …  
    833840
    834841        //ctor
    835         void ?{}(thread FuncRunner* this, voidFunc inFunc) {
     842        void ?{}(FuncRunner* this, voidFunc inFunc) {
    836843                func = inFunc;
    837844        }
    838845
    839846        //main
    840         void ?main(thread FuncRunner* this) {
     847        void ?main(FuncRunner* this) {
    841848                this->func();
    842849        }
    843850\end{lstlisting}
    844851
    845 % In this example \code{func} is a function pointer stored in \acrfull{tls}, which is \CFA is both easy to use and completly typesafe.
    846 
    847 Of course for threads to be useful, it must be possible to start and stop threads and wait for them to complete execution. While using an \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.
    848 \begin{lstlisting}
    849 thread struct FuncRunner; //FuncRunner declared above
    850 
    851 void world() {
     852Of course for threads to be useful, it must be possible to start and stop threads and wait for them to complete execution. While using an \acrshort{api} such as \code{fork} and \code{join} is relatively common in the literature, such an interface is unnecessary. 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.
     853\begin{lstlisting}
     854thread struct World; //FuncRunner declared above
     855
     856void ?main(thread World* this) {
    852857        sout | "World!" | endl;
    853858}
    854859
    855860void main() {
    856         FuncRunner run = {world};
     861        World w;
    857862        //Thread run forks here
    858863
    … …  
    863868}
    864869\end{lstlisting}
    865 This 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.
    866 
    867 These semantics also naturally scale to multiple threads meaning basic synchronisation is very simple :
     870This semantic has several advantages over explicit semantics : typesafety is guaranteed, a thread is always started and stopped exaclty once and users cannot make any progamming errors. However, one of the apparent drawbacks of this system is that threads now always form a lattice, that is they are always destroyed in opposite order of construction. While this seems like a significant limitation, existing \CFA semantics can solve this problem. Indeed, by using dynamic allocation to create threads will naturally let threads outlive the scope in which the thread was created much like dynamically allocating memory will let objects outlive the scope in which thy were created :
     871
    868872\begin{lstlisting}
    869873        thread struct MyThread {
    … …  
    872876
    873877        //ctor
    874         void ?{}(thread MyThread* this) {}
     878        void ?{}(MyThread* this,
     879                     bool is_special = false) {
     880                //...
     881        }
    875882
    876883        //main
    877         void ?main(thread MyThread* this) {
     884        void ?main(MyThread* this) {
     885                //...
     886        }
     887
     888        void foo() {
     889                MyThread* special_thread;
     890                {
     891                        MyThread thrds = {false};
     892                        //Start a thread at the beginning of the scope
     893
     894                        DoStuff();
     895
     896                        //create a other thread that will outlive the thread in this scope
     897                        special_thread = new MyThread{true};
     898
     899                        //Wait for the thread to finish
     900                }
     901                DoMoreStuff();
     902
     903                //Now wait for the special
     904        }
     905\end{lstlisting}
     906
     907Another advantage of this semantic is that it naturally scale to multiple threads meaning basic synchronisation is very simple :
     908
     909\begin{lstlisting}
     910        thread struct MyThread {
     911                //...
     912        };
     913
     914        //ctor
     915        void ?{}(MyThread* this) {}
     916
     917        //main
     918        void ?main(MyThread* this) {
    878919                //...
    879920        }
    … …  
    889930\end{lstlisting}
    890931
     932\subsection{Coroutines : A stepping stone}\label{coroutine}
     933While the main focus of this proposal is concurrency and paralellism, it is important to adress coroutines which are actually a significant underlying aspect of the concurrency system. Indeed, while having nothing todo with parallelism and arguably very little to do with concurrency, coroutines need to deal with context-switchs and and other context management operations. Therefore, this proposal includes coroutines both as an intermediate step for the implementation of threads and a first class feature of \CFA.
     934
     935The core API of coroutines revolve around two features : independent stacks and suspedn/resume. Much like threads the syntax for declaring a coroutine is declaring a type and a main routine for it to start :
     936\begin{lstlisting}
     937        coroutine struct MyCoroutine {
     938                //...
     939        };
     940
     941        //ctor
     942        void ?{}(MyCoroutine* this) {
     943
     944        }
     945
     946        //main
     947        void ?main(MyCoroutine* this) {
     948                sout | "Hello World!" | endl;
     949        }
     950\end{lstlisting}
     951
     952One a coroutine is created, users can context switch to it using \code{suspend} and come back using \code{resume}. Here is an example of a solution to the fibonnaci problem using coroutines :
     953\begin{lstlisting}
     954        coroutine struct Fibonacci {
     955                int fn; // used for communication
     956        };
     957
     958        void ?main(Fibonacci* this) {
     959                int fn1, fn2;           // retained between resumes
     960                this->fn = 0;
     961                fn1 = this->fn;
     962                suspend(this);          // return to last resume
     963
     964                this->fn = 1;
     965                fn2 = fn1;
     966                fn1 = this->fn;
     967                suspend(this);          // return to last resume
     968
     969                for ( ;; ) {
     970                        this->fn = fn1 + fn2;
     971                        fn2 = fn1;
     972                        fn1 = this->fn;
     973                        suspend(this);  // return to last resume
     974                }
     975        }
     976
     977        int next(Fibonacci& this) {
     978                resume(&this); // transfer to last suspend
     979                return this.fn;
     980        }
     981
     982        void main() {
     983                Fibonacci f1, f2;
     984                for ( int i = 1; i <= 10; i += 1 ) {
     985                        sout | next(f1) | '§\verb+ +§' | next(f2) | endl;
     986                }
     987        }
     988\end{lstlisting}
     989
    891990\newpage
    892 \large{\textbf{WORK IN PROGRESS}}
     991\bf{WORK IN PROGRESS}
    893992\subsection{The \CFA Kernel : Processors, Clusters and Threads}\label{kernel}
    894993
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