Department of Informatics V. HPC-Lab. Session 2: OpenMP M. Bader, A. Breuer. Alex Breuer
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1 HPC-Lab Session 2: OpenMP M. Bader, A. Breuer
2 Meetings Date Schedule 10/13/14 Kickoff 10/20/14 Q&A 10/27/14 Presentation 1 11/03/14 H. Bast, Intel 11/10/14 Presentation 2 12/01/14 Presentation 3 12/08/14 Q&A 12/15/14 Presentation 4 12/22/14 Q&A 01/12/15 Report: Project phase 01/19/15 Presentation Project 2
3 What s OMP? OpenMP is a specification for a set of compiler directives, library routines, and environment variables that can be used to specify high-level parallelism in Fortran and C/C++ programs. 3
4 Shared-Memory-Parallelism Transparent programming view: Cores access same memory In hardware: Be aware of layout, e.g. NUMA; first touch policy Sandy Bridge CPU, openmp/ Knights Corner and Knights Landing, sc13- intel- reveals- knights- landing- high- performancecpu / 4
5 Fork-Join Model 5
6 A Simple Loop #include <omp.h> // some initialization #pragma omp parallel for for( i = 0; i < n; i++) { for( j = 0; j < n; j++) { // do some work (i, j) } } OpenMP - Parallel programming on shared memory systems 6
7 OMP: An Overview Compiler-directives: #pragma omp... [clause[[,] clause] ] Work-sharing #pragma omp for..., and many more Synchronization #pragma omp barrier, and many more Data Scope Clauses shared, private, firstprivate, reduction, Library routines omp_get_num_treads(), omp_get_thread_num(), 7
8 Compilation and Execution compile with additional Compiler-Flag -openmp (-fopenmp in case of gcc): icc -openmp -o my_algorithm alg.c export OMP_NUM_THREADS=number_of_threads./my_algorithm 8
9 Parallel Region #pragma omp parallel [clause[[,] clause]...] structured block code within parallel region are executed by all threads #include <omp.h> // more initialization #pragma omp parallel private(size, rank) { size = omp_get_num_threads(); rank = omp_get_thread_num(); printf("hello World! (Thread %d of %d)", rank, size); } 9
10 Work Sharing: for #pragma omp for [clause[[,] clause]...] for-loop within a parallel region and directly in front of a for-loop iterations are scheduled across different threads schedule clause determines how to map iterations to threads schedule(static[, chunksize]) default-value chunksize is #iterations divided by #hreads schedule(dynamic[, chunksize]) default-value chunksize is 1 schedule(runtime) Scheduling is given by environment variable OMP_SHEDULE implicit synchronization at the end of for-loop (can be disabled with nowait clause) tutorials/openmp/ shortcut possible: #pragma omp parallel for 10
11 Work Sharing: for #pragma omp for [clause[[,] clause]...] for-loop within a parallel region and directly in front of a for-loop iterations are scheduled across different threads schedule clause determines how to map iterations to threads schedule(static[, chunksize]) default-value chunksize is #iterations divided by #hreads schedule(dynamic[, chunksize]) default-value chunksize is 1 schedule(runtime) Scheduling is given by environment variable OMP_SHEDULE implicit synchronization at the end of for-loop (can be disabled with nowait clause) shortcut possible: #pragma omp parallel for Intel, Load Balance and Parallel Performance, 11
12 Department of Informatics V Work Sharing: task #pragma omp task [clause[[,] clause] ] structured block where clause can be final(expression): if expression is true, task is executed sequentially; no recursive task generation untied: the execution of a task is not tied to one single thread shared firstprivate private [ ] #pragma omp taskwait: waits for children completition Intel, Load Balance and Parallel Performance, 12
13 Work Sharing : single #pragma omp single [clause[[,] clause]...] structured block use only one (arbitrary) thread implicit synchronization #pragma omp master structured block use only master thread for structures block NO synchronization at the end tutorials/openmp/ 13
14 Synchronization #pragma omp barrier: blocks execution until all threads have reached the barrier #pragma omp critical structured block only one thread at a time can execute the structured block encapsulated by critical Warning: Use carefully; can definitely kill performance! 14
15 Reduction reduction (operator: list) executes a reduction of variables in list using operator available operators: +,,, &&, #pragma omp parallel for private(r), reduction(+: sum) for(i = 0; i < n; i++){ r = compute_r(i); sum = sum + r; } 15
16 Scopes private(list): declares variables in list as private for each thread (no copy!) shared(list): variables in list are used by all threads (race conditions are possible!), write accesses have to be handled by programmer firstprivate(list): private variables and init them with latest valid value before parallel region lastprivate(list): after parallel execution, in serial part reused latest modification. default data scope clause is shared, but exceptions exists be precise! local variables are always private loop-variables of for-loops are private (C++only) 16
17 Fill the Gaps k = compute_k(); #pragma omp parallel for private(?), shared(?), firstprivate(?), lastprivate(?), reduction(?) for( i = 0; i < n; i++ ) { k = compute_my_k( i, k ); r = compute_r( i, k, h ); sum = sum + r; } 17
18 NUMA Non-uniform memory access (NUMA): Not all cores can access all memory in the same time Pinning: Use close-to-core memory as far as possible OpenMP first touch policy: not the malloc counts but the initialization numactl has some options Georg Hager's Blog, 18
19 NUMA Non-uniform memory access Fetch Dual Compute Core Unit (NUMA): Not all cores can access Decode all memory in the same time Int Scheduler Int Scheduler Pinning: Use close-to-core memory as far as possible Pipeline Pipeline Pipeline Pipeline FP Scheduler Pipeline Pipeline Pipeline Pipeline OpenMP first touch policy: not L1 DCache 128-bit FMAC 128-bit FMAC L1 DCache the malloc counts but the initialization Shared L2 Cache numactl has some options Shared L3 Cache and NB AMD, Software Optimization Guide for AMD Family 15h Processors 19
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