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Parallelism in the Standard C++: What to Expect in C++ 17

Parallelism in the Standard C++: What to Expect in C++ 17

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Parallelism in the Standard C++: What to Expect in C++ 17

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  1. Parallelism in the Standard C++: What to Expect in C++ 17 Artur Laksberg Microsoft Corp. May 8th, 2014

  2. Agenda • Fundamentals • Task regions • Parallel Algorithms • Parallelization • Vectorization

  3. Part 1: The Fundamentals

  4. Renderscript OpenMP CUDA C++ AMP PPL TBB MPI OpenACC OpenCL Cilk Plus GCD

  5. Parallelism in C++11/14 • Fundamentals: • Memory model • Atomics • Basics: • thread • mutex • condition_variable • async • future

  6. Quicksort: Serial void quicksort(int *v, int start, int end) { if (start < end) { int pivot = partition(v, start, end); quicksort(v, start, pivot - 1); quicksort(v, pivot + 1, end); } }

  7. Quicksort: Use Threads Problem 1: expensive void quicksort(int *v, int start, int end) { if (start < end) { int pivot = partition(v, start, end); std::thread t1([&] { quicksort(v, start, pivot - 1); }); std::thread t2([&] { quicksort(v, pivot + 1, end); }); t1.join(); t2.join(); } } Problem 3: Exceptions?? Problem 2: Fork-join not enforced

  8. Quicksort: Fork-Join Parallelism parallel region void quicksort(int *v, int start, int end) { if (start < end) { int pivot = partition(v, start, end); quicksort(v, start, pivot - 1); quicksort(v, pivot + 1, end); } } task task

  9. Quicksort: Using Task Regions (N3832) parallel region void quicksort(int *v, int start, int end) { if (start < end) { task_region([&] (auto& r) { int pivot = partition(v, start, end); r.run([&] { quicksort(v, start, pivot - 1); }); r.run([&] { quicksort(v, pivot + 1, end); }); }); } } task task

  10. Under The Hood…

  11. Work Stealing Scheduling proc 2 proc 1 proc 3 proc 4

  12. Work Stealing Scheduling proc 2 proc 1 proc 3 proc 4 Old items New items

  13. Work Stealing Scheduling proc 2 proc 1 proc 3 proc 4 Old items New items

  14. Work Stealing Scheduling proc 2 proc 1 proc 3 proc 4 Old items New items “Thief”

  15. Fork-Join Parallelism and Work Stealing Q1: What thread runs f? Q2: What thread runs g? e(); task_region([] (auto& r) { r.run(f); g(); }); h(); e() g() f() Q3: What thread runs h? h()

  16. Work Stealing Design Choices • What Thread Executes After a Spawn? • Child Stealing • Continuation (parent) Stealing • What Thread Executes After a Join? • Stalling: initiating thread waits • Greedy: the last thread to reach join continues task_region([] (auto& r) { for(inti=0; i<n; ++i) r.run(f); });

  17. Part 2: The Algorithms

  18. Alex Stepanov: Start With The Algorithms

  19. Inspiration Performing Parallel Operations On Containers • Intel • Threading Building Blocks • Microsoft • Parallel Patterns Library, C++ AMP • Nvidia • Thrust

  20. Parallel STL • Just like STL, only parallel… • Can be faster • If you know what you’re doing • Two Execution Policies: • std:par • std::vec

  21. Parallelization: What’s a Big Deal? • Why not already parallel? std::sort(begin, end, [](int a, intb) { return a < b; }); • User-provided closures must be thread safe: int comparisons = 0; std::sort(begin, end, [&](int a, intb) { comparisons++; return a < b; }); • But also special-member functions, std::swap etc.

  22. It’s a Contract • What the user can do • What the implementer can do • Asymptotic Guarantees:std::sort: O(n*log(n)), std::stable_sort: O(n*log2(n)), what about parallel sort? • What is a valid implementation? (see next slide)

  23. Chaos Sort template<typename Iterator, typename Compare> void chaos_sort( Iterator first, Iterator last, Compare comp ) { auto n = last-first; std::vector<char> c(n); for(;;) { bool flag = false; for( size_ti=1; i<n; ++i ) { c[i] = comp(first[i],first[i-1]); flag |= c[i]; } if( !flag ) break; for( size_ti=1; i<n; ++i ) if( c[i] ) std::swap( first[i-1], first[i] ); } }

  24. Execution Policies • Built-in Execution Policies: extern constsequential_execution_policyseq; extern constparallel_execution_policy par; extern constvector_execution_policyvec; • Dynamic Execution Policy: class execution_policy { public: // ... consttype_info& target_type() const; template<class T> T *target(); template<class T> const T *target() const; };

  25. Using Execution Policy To Write Paralel Code std::vector<int> vec = ... // standard sequential sort std::sort(vec.begin(), vec.end()); using namespace std::experimental::parallel; // explicitly sequential sort sort(seq, vec.begin(), vec.end()); // permitting parallel execution sort(par, vec.begin(), vec.end()); // permitting vectorization as well sort(vec, vec.begin(), vec.end());

  26. Picking Execution Policy Dynamically size_tthreshold = ... execution_policyexec = seq; if(vec.size() > threshold) { exec = par; } sort(exec, vec.begin(), vec.end());

  27. Exception Handling • In C++ philosophy, no exception is silently ignored • Exception list: container of exception_ptr objects try { r = std::inner_product(std::par, a.begin(), a.end(), b.begin(), func1, func2, 0); } catch(constexception_list& list) { for(auto& exptr : list) { // process exception pointer exptr } }

  28. Vectorization: A Tale From Agriculture

  29. A Tale From Agriculture

  30. A Tale From Agriculture

  31. Idea: Fewer Tractors, Wider Plows

  32. Vectorization: What’s a Big Deal? int a[n] = ...; int b[n] = ...; for(int i=0; i<n; ++i) { a[i] = b[i] + c; } movdqu xmm1, XMMWORD PTR _b$[esp+eax+132] movdqu xmm0, XMMWORD PTR _a$[esp+eax+132] paddd xmm1, xmm2 paddd xmm1, xmm0 movdqu XMMWORD PTR _a$[esp+eax+132], xmm1 a[i:i+3] = b[i:i+3] + c;

  33. Vector Lane is not a Thread! • Taking locks • Thread with thread_id x takes a lock… • Then another “thread” with the same thread_id enters the lock… • Deadlock!!! • Exceptions • Can we unwind 1/4th of the stack?

  34. Vectorization: Not So Easy Any More… Aliasing? void f(int* a, int*b) { for(int i=0; i<n; ++i) { a[i] = b[i] + c; func(); } } mov ecx, DWORD PTR _b$[esp+esi+140] add ecx, edi add DWORD PTR _a$[esp+esi+140], ecx call func Side effects? Dependence? Exceptions?

  35. Vectorization Hazard: Locks Consider: f takes a lock, g releases the lock: ? for(int i=0; i<n; ++i) { lock.enter(); a[i] = b[i] + c; lock.release(); } for(int i=0; i<n; i+=4) { for(int j=0; j<4; ++j) lock.enter(); a[i:i+3] = b[i:i+3] + c; for(int j=0; j<4; ++j) lock.release(); } This transformation is not safe!

  36. How Do We Get This? void f(int* a, int*b) { for(int i=0; i<n; ++i) { a[i] = b[i] + c; func(); } } for(int i=0; i<n; i+=4) { a[i:i+3] = b[i:i+3] + c; for(int j=0; j<4; ++j) func(); } Need a helping hand from the programmer, because…

  37. Vector Loop with Parallel STL void f(int* a, int*b) { integer_iteratorbegin {0}; integer_iteratorend {n}; std::for_each( std::vec, begin, end, [&](inti) { a[i] = b[i] + c; func(); } }

  38. Parallelization vs. Vectorization Parallelization Vectorization Vector Lanes No stack Lock-step execution Very light-weight • Threads • Stack • Good for divergent code • Relatively heavy-weight

  39. When To Vectorize std::par std::vec Same as std::vec, plus: No Exceptions No Locks No/Little Divergence • No race conditions • No aliasing

  40. References • N3832: Task Region • N3872: A Primer on Scheduling Fork-Join Parallelism with Work Stealing • N3724: A Parallel Algorithms Library • N3850: Working Draft, Technical Specification for C++ Extensions for Parallelism • parallelstl.codeplex.com