Changeset 687165a for doc/proposals/concurrency/concurrency.tex
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- Nov 23, 2016, 12:03:39 PM (8 years ago)
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doc/proposals/concurrency/concurrency.tex
rbbd44c5 r687165a 149 149 150 150 \begin{lstlisting} 151 mutex struct counter_t { /*...see section §\ref{data}§...*/ };151 mutex struct counter_t { /*...see section §\ref{data}§...*/ }; 152 152 153 153 void ?{}(counter_t & nomutex this); //constructor … … 567 567 \newpage 568 568 \subsection{External scheduling} \label{extsched} 569 A s 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 whata simple use \code{accept} versus \code{wait}/\code{signal} and its advantages.569 An 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. 570 570 571 571 \begin{center} … … 585 585 586 586 public: 587 void f() ;587 void f() { /*...*/ } 588 588 void g() { _Accept(f); } 589 589 private: … … 593 593 \end{center} 594 594 595 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.595 In 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. 596 596 \\ 597 597 … … 776 776 % # # # # # # # ####### ####### ####### ####### ### ##### # # 777 777 \section{Parallelism} 778 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.778 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 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. 779 779 780 780 \subsection{User-level threads} 781 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.782 783 Examples of languages that support are Erlang~\cite{Erlang} and \uC~\cite{uC++book}.784 785 \subs ection{Fibers : user-level threads without preemption}786 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}.781 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. 782 783 Examples of languages that support \glspl{uthread} are Erlang~\cite{Erlang} and \uC~\cite{uC++book}. 784 785 \subsubsection{Fibers : user-level threads without preemption} 786 A 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. 787 787 788 788 An example of a language that uses fibers is Go~\cite{Go} 789 789 790 790 \subsection{Jobs and thread pools} 791 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 willalways results in idles cores.791 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 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. 792 792 793 793 The gold standard of this implementation is Intel's TBB library~\cite{TBB}. 794 794 795 795 \subsection{Paradigm performance} 796 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 belargely amorticised by the actual work done.796 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 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. 797 797 798 798 % ##### ####### # ####### ###### ###### … … 805 805 806 806 \section{\CFA 's Thread Building Blocks} 807 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 : 808 \begin{center} 809 \begin{tabular}[t]{| r | c | c |} 810 \cline{2-3} 811 \multicolumn{1}{ c| }{} & Has a stack & Preemptive \\ 812 \hline 813 \Glspl{pool} & X & X \\ 814 \hline 815 \Glspl{fiber} & \checkmark & X \\ 816 \hline 817 \Glspl{uthread} & \checkmark & \checkmark \\ 818 \hline 819 \end{tabular} 820 \end{center} 821 822 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. 823 824 As shown in section \ref{cfaparadigms} these different blocks being available in \CFA it is trivial to reproduce any of these paradigm. 807 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, 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. 825 808 826 809 % ####### # # ###### ####### # ###### ##### … … 833 816 834 817 \subsection{Thread Interface} 835 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 comparatievelyto \glspl{kthread}). A thread can be declared using a struct declaration with prefix \code{thread} as follows :818 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 compared to \glspl{kthread}). A thread can be declared using a struct declaration with prefix \code{thread} as follows : 836 819 837 820 \begin{lstlisting} … … 839 822 \end{lstlisting} 840 823 841 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 namecan be extended, i.e. operator overloading. As such the \code{main} routine of a thread can be defined as :824 Obviously, 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 : 842 825 \begin{lstlisting} 843 826 thread struct foo {}; 844 827 845 void ?main( threadfoo* this) {846 /*... Some useful code ...*/847 } 848 \end{lstlisting} 849 850 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} 832 833 In 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 : 851 834 \begin{lstlisting} 852 835 typedef void (*voidFunc)(void); … … 857 840 858 841 //ctor 859 void ?{}( threadFuncRunner* this, voidFunc inFunc) {842 void ?{}(FuncRunner* this, voidFunc inFunc) { 860 843 func = inFunc; 861 844 } 862 845 863 846 //main 864 void ?main( threadFuncRunner* this) {847 void ?main(FuncRunner* this) { 865 848 this->func(); 866 849 } 867 850 \end{lstlisting} 868 851 869 % In this example \code{func} is a function pointer stored in \acrfull{tls}, which is \CFA is both easy to use and completly typesafe. 870 871 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. 872 \begin{lstlisting} 873 thread struct FuncRunner; //FuncRunner declared above 874 875 void world() { 852 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 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} 854 thread struct World; //FuncRunner declared above 855 856 void ?main(thread World* this) { 876 857 sout | "World!" | endl; 877 858 } 878 859 879 860 void main() { 880 FuncRunner run = {world};861 World w; 881 862 //Thread run forks here 882 863 … … 887 868 } 888 869 \end{lstlisting} 889 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. 890 891 These semantics also naturally scale to multiple threads meaning basic synchronisation is very simple : 870 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. 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 : 871 892 872 \begin{lstlisting} 893 873 thread struct MyThread { … … 896 876 897 877 //ctor 898 void ?{}(thread MyThread* this) {} 878 void ?{}(MyThread* this, 879 bool is_special = false) { 880 //... 881 } 899 882 900 883 //main 901 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} 906 907 Another 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) { 902 919 //... 903 920 } … … 911 928 //Wait for the 10 threads to finish 912 929 } 913 914 void bar() { 915 MyThread* thrds = new MyThread[10]; 916 //Start 10 threads at the beginning of the scope 917 918 DoStuff(); 919 920 //Wait for the 10 threads to finish 921 delete MyThread; 922 } 923 \end{lstlisting} 924 925 \newpage 926 \large{\textbf{WORK IN PROGRESS}} 930 \end{lstlisting} 931 932 \subsection{Coroutines : A stepping stone}\label{coroutine} 933 While 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. 934 935 The 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 //main 945 void ?main(MyCoroutine* this) { 946 sout | "Hello World!" | endl; 947 } 948 \end{lstlisting} 949 950 One 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 : 951 \begin{lstlisting} 952 coroutine struct Fibonacci { 953 int fn; // used for communication 954 }; 955 956 void ?main(Fibonacci* this) { 957 int fn1, fn2; // retained between resumes 958 this->fn = 0; 959 fn1 = this->fn; 960 suspend(this); // return to last resume 961 962 this->fn = 1; 963 fn2 = fn1; 964 fn1 = this->fn; 965 suspend(this); // return to last resume 966 967 for ( ;; ) { 968 this->fn = fn1 + fn2; 969 fn2 = fn1; 970 fn1 = this->fn; 971 suspend(this); // return to last resume 972 } 973 } 974 975 int next(Fibonacci& this) { 976 resume(&this); // transfer to last suspend 977 return this.fn; 978 } 979 980 void main() { 981 Fibonacci f1, f2; 982 for ( int i = 1; i <= 10; i += 1 ) { 983 sout | next(f1) | '§\verb+ +§' | next(f2) | endl; 984 } 985 } 986 \end{lstlisting} 987 927 988 \subsection{The \CFA Kernel : Processors, Clusters and Threads}\label{kernel} 928 989
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