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Timestamp:
Dec 15, 2016, 5:16:42 PM (9 years ago)
Author:
Rob Schluntz <rschlunt@…>
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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43385ca, f7ff3fb
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5802a4f (diff), 596f987b (diff)
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Merge branch 'master' into tuples

Conflicts:

src/ResolvExpr/CommonType.cc
src/tests/.expect/32/extension.txt
src/tests/.expect/32/gccExtensions.txt
src/tests/.expect/64/declarationSpecifier.txt
src/tests/.expect/64/extension.txt
src/tests/.expect/64/gccExtensions.txt
src/tests/.expect/castError.txt
src/tests/Makefile.am

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  • doc/proposals/concurrency/concurrency.tex

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    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 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 parallelism, message passing and implicit threading.
    103 
    104 There 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.
     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.
    105105
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    113113
    114114\section{Concurrency}
    115 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 relies on message passing~\cite{Thoth,Harmony,V-Kernel} or other paradigms that closely relate to networking concepts. 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 will still have to take both paradigms into account. 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}. 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 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. 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.
     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.
    116116
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    144144
    145145\subsubsection{Call semantics} \label{call}
    146 The above monitor example displays some of their 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 
    148 Another 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 large counter :
    149 
    150 \begin{lstlisting}
    151         mutex struct counter_t { /*...*/ };
    152 
    153         void ?{}(counter_t & nomutex this);
    154         size_t ++?(counter_t & mutex this);
     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
    155155
    156156        //need for mutex is platform dependent here
    157         void ?{}(size_t * this, counter_t & mutex cnt);
    158 \end{lstlisting}
    159 *semantics of the declaration of \code{mutex struct counter_t} are discussed in details in section \ref{data}
    160 
    161 Here, 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 object not yet constructed should never be shared and therefore do 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.
    162 
    163 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 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". 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.
    164 
    165 Regardless of which keyword is kept, it is important to establish when mutex/nomutex may be used as a type qualifier. Consider :
     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:
    166165\begin{lstlisting}
    167166        int f1(monitor & mutex m);
    … …  
    171170        int f5(graph(monitor*) & mutex m);
    172171\end{lstlisting}
    173 
    174 The problem is to indentify which object(s) should be acquired. Furthermore, each object needs to be acquired 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 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} will be acquired, passing an array to this routine would be type safe and result in undefined behavior. For this reason, it would also be reasonnable to disallow mutex in the context where arrays may be passed.
     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.
    175173
    176174% ######     #    #######    #
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    183181
    184182\subsubsection{Data semantics} \label{data}
    185 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}:
    186184\begin{lstlisting}
    187185        mutex struct counter_t {
    … …  
    203201\end{lstlisting}
    204202
    205 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 :
     203This simple counter is used as follows:
    206204\begin{center}
    207205\begin{tabular}{c @{\hskip 0.35in} c @{\hskip 0.35in} c}
    208206\begin{lstlisting}
     207        //shared counter
    209208        counter_t cnt;
    210209
     210        //multiple threads access counter
    211211        thread 1 : cnt++;
    212212        thread 2 : cnt++;
    … …  
    218218\end{center}
    219219
    220 These simple mutual exclusion semantics also naturally expand to multi-monitor calls.
     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.
    221221\begin{lstlisting}
    222222        int f(MonitorA & mutex a, MonitorB & mutex b);
    … …  
    226226        f(a,b);
    227227\end{lstlisting}
    228 
    229 This code acquires both locks before entering the critical section (Referenced as \gls{group-acquire} from now on). In practice, writing multi-locking routines that can not lead to deadlocks can be 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 will be consistent across calls to routines using the same monitors as arguments. However, since \CFA monitors use multi-acquiring locks users can effectively force the acquiring order. For example, notice which routines use \code{mutex}/\code{nomutex} and how this affects aquiring order :
    230 \begin{lstlisting}
    231         void foo(A & mutex a, B & mutex a) {
    232                 //...
    233         }
    234 
    235         void bar(A & mutex a, B & nomutex a)
    236                 //...
    237                 foo(a, b);
    238                 //...
    239         }
    240 
    241         void baz(A & nomutex a, B & mutex a)
    242                 //...
    243                 foo(a, b);
    244                 //...
    245         }
    246 \end{lstlisting}
    247 
    248 Such a use will lead to nested monitor call problems~\cite{Lister77}, 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{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 deadlocks, depending on the implicit ordering matching the explicit ordering. As shown on several occasion\cit, solving this problems requires to :
     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 :
    249248\begin{enumerate}
    250         \item Dynamically track the monitor call order.
     249        \item Dynamically tracking of the monitor-call order.
    251250        \item Implement rollback semantics.
    252251\end{enumerate}
    253252
    254 While 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.
     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.
    255254
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    … …  
    271270
    272271\subsubsection{Implementation Details: Interaction with polymorphism}
    273 At first glance, interaction between monitors and \CFA's concept of polymorphism seem complex to support. However, it can be reasoned that entry-point locking can solve most of the issues that could be present with polymorphism.
    274 
    275 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. 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}\footnotemark 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}\footnotemark[\value{footnote}] calling a monitor routine becomes exactly the same as calling it from anywhere else.
    276 \footnotetext{See glossary for a definition of \gls{callsite-locking} and \gls{entry-point-locking}}
     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
    277342
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    … …  
    285350
    286351\subsection{Internal scheduling} \label{insched}
    287 Monitors also need to schedule waiting threads within 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 have threads wait and signaled from them. 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 :
    288353
    289354\begin{lstlisting}
    … …  
    303368\end{lstlisting}
    304369
    305 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.
     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.
    306371\begin{center}
    307372\begin{tabular}{ c @{\hskip 0.65in} c }
    … …  
    337402condition e;
    338403
     404//acquire a & b
    339405void foo(monitor & mutex a,
    340406           monitor & mutex b) {
    341407
    342         wait(e);
     408        wait(e); //release a & b
    343409}
    344410
    … …  
    352418condition e;
    353419
     420//acquire a
    354421void bar(monitor & mutex a,
    355422           monitor & nomutex b) {
    … …  
    357424}
    358425
     426//acquire a & b
    359427void foo(monitor & mutex a,
    360428           monitor & mutex b) {
    361         wait(e);
     429        wait(e);  //release a & b
    362430}
    363431
    … …  
    366434condition e;
    367435
     436//acquire a
    368437void bar(monitor & mutex a,
    369438           monitor & nomutex b) {
    … …  
    371440}
    372441
     442//acquire b
    373443void baz(monitor & nomutex a,
    374444           monitor & mutex b) {
    375         wait(e);
     445        wait(e);  //release b
    376446}
    377447
    … …  
    381451\end{center}
    382452
    383 Note that in \CFA, \code{condition} have no particular need to be stored inside a monitor, beyond any software engineering reasons. Context 1 is the simplest way of acquiring more than one monitor (\gls{group-acquire}), using a routine wiht 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 will 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.
     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.
    384454
    385455
    … …  
    389459condition e;
    390460
    391 //acquire a
     461//acquire b
    392462void foo(monitor & nomutex a,
    393463           monitor & mutex b) {
    … …  
    399469           monitor & nomutex b) {
    400470
    401         //release a
    402         //keep b
    403         wait(e);
     471        wait(e); //release a
     472                  //keep b
    404473}
    405474
    … …  
    418487           monitor & nomutex b) {
    419488
    420         //release b
    421         //keep a
    422         wait(e);
     489        wait(e); //release b
     490                  //keep a
    423491}
    424492
    … …  
    437505           monitor & nomutex b) {
    438506
    439         //release a & b
    440         //keep none
    441         wait(e);
     507        wait(e); //release a & b
     508                  //keep none
    442509}
    443510
    … …  
    446513\end{tabular}
    447514\end{center}
    448 Note the right-most example which uses a helper routine and therefore is not relevant to find which monitors will be released.
    449 
    450 These 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.
    451 
    452 Regardless of the context in which the \code{wait} statement is used, \code{signal} must used 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.
     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 :
     518\begin{center}
     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);
     538\end{lstlisting} &\begin{lstlisting}
     539        =>
     540\end{lstlisting} &\begin{lstlisting}
     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);
     551\end{lstlisting}
     552\end{tabular}
     553\end{center}
     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.
    453556
    454557Finally, 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.
    … …  
    464567\newpage
    465568\subsection{External scheduling} \label{extsched}
    466 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.
    467570
    468571\begin{center}
    … …  
    482585
    483586        public:
    484                 void f();
     587                void f() { /*...*/ }
    485588                void g() { _Accept(f); }
    486589        private:
    … …  
    490593\end{center}
    491594
    492 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.
    493596\\
    494597
    … …  
    673776% #       #     # #     # #     # ####### ####### ####### ####### ###  #####  #     #
    674777\section{Parallelism}
    675 Historically, 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 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.
     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.
    676779
    677780\subsection{User-level threads}
    678 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 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.
    679 
    680 Examples of languages that support are Erlang~\cite{Erlang} and \uC~\cite{uC++book}.
    681 
    682 \subsection{Fibers : user-level threads without preemption}
    683 In the middle of the flexibility versus complexity spectrum lay \glspl{fiber} which offer \glspl{uthread} without the complexity of preemption by using cooperative scheduling. On a single core machine this means users need not worry about concurrency. On multi-core machines, while concurrency is still a concern, it is only a problem for fibers across cores but not while on the same core. This extra guarantee plus the fact that creating and destroying fibers are implicit synchronizing points means preventing mutable shared ressources still leaves many control flow options. However, multi-core processors can still execute fibers in parallel. This means users either need to worry about mutual exclusion, deadlocks and race conditions, or limit themselves to subset of concurrency primitives, raising the complexity in both cases. In this aspect, fibers can be seen as a more powerfull alternative to \glspl{job}.
     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.
    684787
    685788An example of a language that uses fibers is Go~\cite{Go}
    686789
    687790\subsection{Jobs and thread pools}
    688 The 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 the 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 between different 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 will always results in idles cores.
     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.
    689792
    690793The gold standard of this implementation is Intel's TBB library~\cite{TBB}.
    691794
    692795\subsection{Paradigm performance}
    693 While 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 may allows fine grain context switching which may result in better resource 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.
    694797
    695798%  #####  #######    #          ####### ######  ######
    … …  
    702805
    703806\section{\CFA 's Thread Building Blocks}
    704 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 :
    705 \begin{center}
    706 \begin{tabular}[t]{| r | c | c |}
    707 \cline{2-3}
    708 \multicolumn{1}{ c| }{} & Has a stack & Preemptive \\
    709 \hline
    710 \Glspl{pool} & X & X \\
    711 \hline
    712 \Glspl{fiber} & \checkmark & X \\
    713 \hline
    714 \Glspl{uthread} & \checkmark & \checkmark \\
    715 \hline
    716 \end{tabular}
    717 \end{center}
    718 
    719 This table is missing several variations (for example jobs on \glspl{uthread} or \glspl{fiber}), but these variation affect mostly performance and do not effect the guarantees as the presented paradigm do.
    720 
    721 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.
    722808
    723809% ####### #     # ######  #######    #    ######   #####
    … …  
    730816
    731817\subsection{Thread Interface}
    732 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 with prefix \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 :
    733819
    734820\begin{lstlisting}
    … …  
    736822\end{lstlisting}
    737823
    738 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, this proposal considers that statically tying a \code{main} routine to a thread superseeds this approach. Since the \code{main} routine is definitely a special routine in \CFA, the existing syntax for declaring routines with unordinary name can be extended, i.e. operator overloading. As such the \code{main} routine of a thread can be defined as :
     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 :
    739825\begin{lstlisting}
    740826        thread struct foo {};
    741827
    742         void ?main(thread foo* this) {
    743                 /*... Some useful code ...*/
    744         }
    745 \end{lstlisting}
    746 
    747 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 :
    748834\begin{lstlisting}
    749835        typedef void (*voidFunc)(void);
    … …  
    754840
    755841        //ctor
    756         void ?{}(thread FuncRunner* this, voidFunc inFunc) {
     842        void ?{}(FuncRunner* this, voidFunc inFunc) {
    757843                func = inFunc;
    758844        }
    759845
    760846        //main
    761         void ?main(thread FuncRunner* this) {
     847        void ?main(FuncRunner* this) {
    762848                this->func();
    763849        }
    764850\end{lstlisting}
    765851
    766 % In this example \code{func} is a function pointer stored in \acrfull{tls}, which is \CFA is both easy to use and completly typesafe.
    767 
    768 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.
    769 \begin{lstlisting}
    770 thread struct FuncRunner; //FuncRunner declared above
    771 
    772 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) {
    773857        sout | "World!" | endl;
    774858}
    775859
    776860void main() {
    777         FuncRunner run = {world};
     861        World w;
    778862        //Thread run forks here
    779863
    … …  
    784868}
    785869\end{lstlisting}
    786 This 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. Furthermore it naturally follows the memory allocation semantics, which means users do not need to learn multiple semantics.
    787 
    788 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
    789872\begin{lstlisting}
    790873        thread struct MyThread {
    … …  
    793876
    794877        //ctor
    795         void ?{}(thread MyThread* this) {}
     878        void ?{}(MyThread* this,
     879                     bool is_special = false) {
     880                //...
     881        }
    796882
    797883        //main
    798         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) {
    799919                //...
    800920        }
    … …  
    808928                //Wait for the 10 threads to finish
    809929        }
    810 
    811         void bar() {
    812                 MyThread* thrds = new MyThread[10];
    813                 //Start 10 threads at the beginning of the scope
    814 
    815                 DoStuff();
    816 
    817                 //Wait for the 10 threads to finish
    818                 delete MyThread;
     930\end{lstlisting}
     931
     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                }
    819987        }
    820988\end{lstlisting}
    821989
    822990\newpage
    823 \large{\textbf{WORK IN PROGRESS}}
     991\bf{WORK IN PROGRESS}
    824992\subsection{The \CFA Kernel : Processors, Clusters and Threads}\label{kernel}
    825993
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