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Introductions to Parallel Programming Using OpenMP

Introductions to Parallel Programming Using OpenMP . Zhenying Liu, Dr. Barbara Chapman High Performance Computing and Tools group Computer Science Department University of Houston. April 7, 2005. Content. Overview of OpenMP Acknowledgement OpenMP constructs (5 categories)

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Introductions to Parallel Programming Using OpenMP

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  1. Introductions to Parallel Programming Using OpenMP Zhenying Liu, Dr. Barbara Chapman High Performance Computing and Tools group Computer Science Department University of Houston April 7, 2005

  2. Content • Overview of OpenMP • Acknowledgement • OpenMP constructs (5 categories) • OpenMP exercises • References

  3. Overview of OpenMP • OpenMP is a set of extensions to Fortran/C/C++ • OpenMP contains compiler directives, library routines and environment variables. • Available on most single address space machines. • shared memory systems, including cc-NUMA • Chip MultiThreading: Chip MultiProcessing (Sun UltraSPARC IV), Simultaneous Multithreading (Intel Xeon) • not on distributed memory systems, classic MPPs, or PC clusters (yet!)

  4. Shared Memory Architecture • All processors have access to one global memory • All processors share the same address space • The system runs a single copy of the OS • Processors communicate by reading/writing to the global memory • Examples: multiprocessor PCs (Intel P4), Sun Fire 15K, NEC SX-7, Fujitsu PrimePower, IBM p690, SGI Origin 3000.

  5. Shared Memory Systems (cont) OpenMP Pthreads

  6. Processor Processor Processor ••••••••••••• M M M Interconnect Distributed Memory Systems MPI HPF

  7. Clustered of SMPs MPI hybrid MPI + OpenMP

  8. OpenMP Usage • Applications • Applications with intense computational needs • From video games to big science & engineering • Programmer Accessibility • From very early programmers in school to scientists to parallel computing experts • Available to millions of programmers • In every major (Fortran & C/C++) compiler

  9. OpenMP Syntax • Most of the constructs in OpenMP are compiler directives or pragmas. • For C and C++, the pragmas take the form: • #pragma omp construct [clause [clause]…] • For Fortran, the directives take one of the forms: • C$OMP construct [clause [clause]…] • !$OMP construct [clause [clause]…] • *$OMP construct [clause [clause]…] • Since the constructs are directives, an OpenMP program can be compiled by compilers that don’t support OpenMP.

  10. OpenMP: Programming Model • Fork-Join Parallelism: • Master thread spawns a team of threads as needed. • Parallelism is added incrementally: i.e. the sequential program evolves into a parallel program.

  11. OpenMP:How is OpenMP Typically Used? • OpenMP is usually used to parallelize loops: • Find your most time consuming loops. • Split them up between threads. Split-up this loop between multiple threads void main() { double Res[1000]; for(int i=0;i<1000;i++) { do_huge_comp(Res[i]); } } void main() { double Res[1000]; #pragma omp parallel for for(int i=0;i<1000;i++) { do_huge_comp(Res[i]); } } Sequential program Parallel program

  12. OpenMP:How do Threads Interact? • OpenMP is a shared memory model. • Threads communicate by sharing variables. • Unintended sharing of data can lead to race conditions: • race condition: when the program’s outcome changes as the threads are scheduled differently. • To control race conditions: • Use synchronization to protect data conflicts. • Synchronization is expensive so: • Change how data is stored to minimize the need for synchronization.

  13. OpenMP vs. POSIX Threads • POSIX threads is the other widely used shared programming API. • Fairly widely available, usually quite simple to implement on top of OS kernel threads. • Lower level of abstraction than OpenMP • library routines only, no directives • more flexible, but harder to implement and maintain • OpenMP can be implemented on top of POSIX threads • Not much difference in availability • not that many OpenMP C++ implementations • no standard Fortran interface for POSIX threads

  14. Content • Overview of OpenMP • Acknowledgement • OpenMP constructs (5 categories) • OpenMP exercises • References

  15. Acknowledgement • Slides provided by • Tim Mattson and Rudolf Eigenmann, SC ’99 • Mark Bull from EPCC • OpenMP program examples • Lawrence Livermore National Lab • NAS FT parallelization from PGI tutorial • Dr. Garbey provided us serial codes of Naiver-Stokes

  16. Content • Overview of OpenMP • Acknowledgement • OpenMP constructs (5 categories) • OpenMP exercises • References

  17. OpenMP Constructs • OpenMP’s constructs fall into 5 categories: • Parallel Regions • Worksharing • Data Environment • Synchronization • Runtime functions/environment variables • OpenMP is basically the same between Fortran and C/C++

  18. OpenMP: Parallel Regions • You create threads in OpenMP with the “omp parallel” pragma. • For example, To create a 4-thread Parallel region: • Each thread calls pooh(ID,A) for ID= 0to 3 double A[1000]; omp_set_num_threads(4); #pragma omp parallel { int ID =omp_get_thread_num(); pooh(ID,A); } Each thread redundantly executes the code within the structured block

  19. OpenMP: Work-Sharing Constructs • The “for” Work-Sharing construct splits up loop iterations among the threads in a team #pragma omp parallel #pragma omp for for (I=0;I<N;I++){ NEAT_STUFF(I); } By default, there is a barrier at the end of the “omp for”. Use the “nowait” clause to turn off the barrier.

  20. Work Sharing ConstructsA motivating example Sequential code for(i=0;I<N;i++) { a[i] = a[i] + b[i];} #pragma omp parallel { int id, i, Nthrds, istart, iend; id = omp_get_thread_num(); Nthrds = omp_get_num_threads(); istart = id * N / Nthrds; iend = (id+1) * N / Nthrds; for(i=istart;I<iend;i++) {a[i]=a[i]+b[i];} } OpenMP Parallel Region OpenMP Parallel Region and a work-sharing for construct #pragma omp parallel #pragma omp for schedule(static) for(i=0;I<N;i++) { a[i]=a[i]+b[i];} OpenMP parallel region and a work-sharing for construct

  21. OpenMP For Construct:The Schedule Clause • The schedule clause effects how loop iterations are mapped onto threads uschedule(static [,chunk]) • Deal-out blocks of iterations of size “chunk” to each thread. uschedule(dynamic[,chunk]) • Each thread grabs “chunk” iterations off a queue until all iterations have been handled. uschedule(guided[,chunk]) • Threads dynamically grab blocks of iterations. The size of the block starts large and shrinks down to size “chunk” as the calculation proceeds. uschedule(runtime) • Schedule and chunk size taken from the OMP_SCHEDULE environment variable.

  22. OpenMP: Work-Sharing Constructs • The Sections work-sharing construct gives a different structured block to each thread. #pragma omp parallel #pragma omp sections { X_calculation(); #pragma omp section y_calculation(); #pragma omp section z_calculation(); } By default, there is a barrier at the end of the “omp sections”. Use the “nowait” clause to turn off the barrier.

  23. Data Environment:Changing Storage Attributes • One can selectively change storage attributes constructs using the following clauses* • SHARED • PRIVATE • FIRSTPRIVATE • THREADPRIVATE • The value of a private inside a parallel loop can be transmitted to a global value outside the loop with: • LASTPRIVATE • The default status can be modified with: • DEFAULT (PRIVATE | SHARED | NONE) * All data clauses apply to parallel regions and worksharing constructs except “shared” which only applies to parallel regions.

  24. Data Environment:Default Storage Attributes • Shared Memory programming model: • Most variables are shared by default • Global variables are SHARED among threads • Fortran: COMMON blocks, SAVE variables, MODULE variables • C: File scope variables, static • But not everything is shared... • Stack variables in sub-programs called from parallel regions are PRIVATE • Automatic variables within a statement block are PRIVATE.

  25. Private Clause • private(var) creates a local copy of var for each thread. – The value is uninitialized – Private copy is not storage associated with the original • void wrong(){ int IS = 0; • #pragma parallel for private(IS) for(int J=1;J<1000;J++) • IS = IS + J; printf(“%i”, IS); • }

  26. OpenMP: Reduction • Another clause that effects the way variables are shared: • reduction (op : list) • The variables in “list” must be shared in the enclosing parallel region. • Inside a parallel or a worksharing construct: • A local copy of each list variable is made and initialized depending on the “op” (e.g. 0 for “+”) • pair wise “op” is updated on the local value • Local copies are reduced into a single global copy at the end of the construct.

  27. OpenMP: An Reduction Example #include <omp.h> #define NUM_THREADS 2 void main () { int i; double ZZ, func(), sum=0.0; omp_set_num_threads(NUM_THREADS) #pragma omp parallel for reduction(+:sum) private(ZZ) for (i=0; i< 1000; i++){ ZZ = func(i); sum = sum + ZZ; } }

  28. OpenMP: Synchronization • OpenMP has the following constructs to support synchronization: • barrier • critical section • atomic • flush • ordered • single • master

  29. Critical and Atomic • Only one thread at a time can enter a critical section C$OMP PARALLEL DO PRIVATE(B) C$OMP& SHARED(RES) DO 100 I=1,NITERS B = DOIT(I) C$OMP CRITICAL CALL CONSUME (B, RES) C$OMP END CRITICAL 100 CONTINUE • Atomic is a special case of a critical section that can be used for certain simple statements: C$OMP PARALLEL PRIVATE(B) B = DOIT(I) C$OMP ATOMIC X = X + B C$OMP END PARALLEL

  30. Master directive • The master construct denotes a structured block that is only executed by the master thread. The other threads just skip it (no implied barriers or flushes). #pragma omp parallel private (tmp) { do_many_things(); #pragma omp master { exchange_boundaries(); } #pragma barrier do_many_other_things(); }

  31. Single directive • The single construct denotes a block of code that is executed by only one thread. • A barrier and a flush are implied at the end of the single block. #pragma omp parallel private (tmp) { do_many_things(); #pragma omp single { exchange_boundaries(); } do_many_other_things(); }

  32. OpenMP: Library routines • Lock routines • omp_init_lock(), omp_set_lock(), omp_unset_lock(), omp_test_lock() • Runtime environment routines: • Modify/Check the number of threads • omp_set_num_threads(), omp_get_num_threads(), omp_get_thread_num(), omp_get_max_threads() • Turn on/off nesting and dynamic mode • omp_set_nested(), omp_set_dynamic(), omp_get_nested(), omp_get_dynamic() • Are we in a parallel region? • omp_in_parallel() • How many processors in the system? • omp_num_procs()

  33. OpenMP: Environment Variables • OMP_NUM_THREADS • bsh: • export OMP_NUM_THREADS=2 • csh: • setenv OMP_NUM_THREADS 4

  34. Content • Overview of OpenMP • Acknowledgement • OpenMP constructs (5 categories) • OpenMP exercises • References

  35. 1. Hello World! #include <omp.h> main () { int nthreads, tid; /* Fork a team of threads giving them their own copies of variables */ #pragma omp parallel private(nthreads, tid) { /* Obtain thread number */ tid = omp_get_thread_num(); printf("Hello World from thread = %d\n", tid); /* Only master thread does this */ if (tid == 0) { nthreads = omp_get_num_threads(); printf("Number of threads = %d\n", nthreads); } } /* All threads join master thread and disband */ }

  36. Example Code - Pthread Creation and Termination #include <pthread.h> #include <stdio.h> #define NUM_THREADS 5 void *PrintHello(void *threadid) { printf("\n%d: Hello World!\n", threadid); pthread_exit(NULL); } int main (int argc, char *argv[]) { pthread_t threads[NUM_THREADS]; int rc, t; for(t=0; t<NUM_THREADS; t++) { printf("Creating thread %d\n", t); rc = pthread_create(&threads[t], NULL, PrintHello, (void *)t); if (rc) { printf("ERROR; return code from pthread_create() is %d\n", rc); exit(-1); } } pthread_exit(NULL); }

  37. 2. Parallel Loop Reduction PROGRAM REDUCTION INTEGER I, N REAL A(100), B(100), SUM ! Some initializations N = 100 DO I = 1, N A(I) = I *1.0 B(I) = A(I) ENDDO SUM = 0.0 !$OMP PARALLEL DO REDUCTION(+:SUM) DO I = 1, N SUM = SUM + (A(I) * B(I)) ENDDO PRINT *, ' Sum = ', SUM END

  38. 3. Matrix-vector multiply using a parallel loop and critical directive /*** Spawn a parallel region explicitly scoping all variables ***/ #pragma omp parallel shared(a,b,c,nthreads,chunk) private(tid,i,j,k) { #pragma omp for schedule (static, chunk) for (i=0; i<NRA; i++) { printf("thread=%d did row=%d\n",tid,i); for(j=0; j<NCB; j++) for (k=0; k<NCA; k++) c[i][j] += a[i][k] * b[k][j]; } }

  39. Steps of Parallelization using OpenMP: An Example from a PGI Tutorial • Compile a code with the option to enable a profiler • Run the code and check if the results are correct • Find out the most time-consuming part of the code via the profiler information • Parallelize the time-consuming part • Repeat above steps until you get reasonable speedup

  40. How to Use a Profiler • PGI compiler • pgf90 -fast -Minfo -Mprof=func fftpde.F -o fftpde (function level) • -Mprof=lines (line level) • -mp for compiling OpenMP codes • pgprof pgprof.out (show the profiler result) • Pathscale compiler • pathf90 -Ofast -pg Fftpde.F -o Fftpde • pathprof Fftpde|more

  41. The most time-consuming loop in Fftpde.F: The OpenMP version of this loop in Fftpde_1.F: !$OMP PARALLEL PRIVATE(Z) !$OMP DO do k=1,n3 do j=1,n2 do i=1,n1 z(i)=cmplx(x1real(i,j,k),x1imag(i,j,k)) end do call fft(z,inverse,w,n1,m1) do i=1,n1 x1real(i,j,k)=real(z(i)) x1imag(i,j,k)=aimag(z(i)) end do end do end do !$OMP END PARALLEL do k=1,n3 do j=1,n2 do i=1,n1 z(i)=cmplx(x1real(i,j,k),x1imag(i,j,k)) end do call fft(z,inverse,w,n1,m1) do i=1,n1 x1real(i,j,k)=real(z(i)) x1imag(i,j,k)=aimag(z(i)) end do end do end do NEXT: compare the 1 and 2 processor profiles after adding OpenMP to this loop

  42. Parallelizing the Reminder of Fftpde.F • The DO 130 loop near line 64 (fftpde_2.F) • The DO 190 loop near line 115 (fftpde_3.F) • 3) The DO 220 loop near line 139 (fftpde_4.F) • 4) The DO 250 loop near line 155 (fftpde_5.F)

  43. !$OMP PARALLEL PRIVATE(KK,KL,T1,T2,IK) !$OMP DO DO 130 K = 1, N3 KK = K - 1 KL = KK T1 = S T2 = AN C C Find starting seed T1 for this KK using the binary rule for exponentiation. C DO 110 I = 1, 100 IK = KK / 2 IF (2 * IK .NE. KK) T2 = RANDLC (T1, T2) IF (IK .EQ. 0) GOTO 120 T2 = RANDLC (T2, T2) KK = IK 110 CONTINUE C C Compute 2 * NQ pseudorandom numbers. C 120 continue CALL VRANLC (N1*N2, T2, aa, x1real(1,1,k)) CALL VRANLC (N1*N2, T2, aa, x1imag(1,1,k)) 130 CONTINUE !$OMP END PARALLEL 1. Parallelize the DO 130 loop in Fftpde_2.F

  44. !$OMP PARALLEL PRIVATE(K1,J1,JK,I1) !$OMP DO DO 190 K = 1, N3 K1 = K - 1 IF (K .GT. N32) K1 = K1 - N3 C DO 180 J = 1, N2 J1 = J - 1 IF (J .GT. N22) J1 = J1 - N2 JK = J1 ** 2 + K1 ** 2 C DO 170 I = 1, N1 I1 = I - 1 IF (I .GT. N12) I1 = I1 - N1 X3(I,J,K) = EXP (AP * (I1 ** 2 + JK)) 170 CONTINUE C 180 CONTINUE 190 CONTINUE !$OMP END PARALLEL 2. Parallelize the DO 190 loop in Fftpde_3.F

  45. 3. Parallelize the DO 220 loop in Fftpde_4.F !$OMP PARALLEL PRIVATE(T1) !$OMP DO DO 220 K = 1, N3 DO 210 J = 1, N2 DO 200 I = 1, N1 T1 = X3(I,J,K) ** KT X2real(I,J,K) = T1 * X1real(I,J,K) X2imag(I,J,K) = T1 * X1imag(I,J,K) 200 CONTINUE 210 CONTINUE 220 CONTINUE !$OMP END PARALLEL

  46. 4. Parallelize the DO 250 loop in Fftpde_5.F !$OMP PARALLEL !$OMP DO DO 250 K = 1, N3 DO 240 J = 1, N2 DO 230 I = 1, N1 X2real(I,J,K) = RN * X2real(I,J,K) X2imag(I,J,K) = RN * X2imag(I,J,K) 230 CONTINUE 240 CONTINUE 250 CONTINUE !$OMP END PARALLEL

  47. Conclusion • OpenMP is successful in small-to-medium SMP systems • Multiple cores/CPUs dominate the future computer architectures; OpenMP would be the major parallel programming language in these architectures. • Simple: everybody can learn it in 2 weeks • Not so simple: Don’t stop learning! keep learning it for better performance

  48. Some Buggy Codes #pragma omp parallel for shared(a,b,c,chunk) private(i,tid) schedule(static,chunk) { tid = omp_get_thread_num(); for (i=0; i < N; i++) { c[i] = a[i] + b[i]; printf("tid= %d i= %d c[i]= %f\n", tid, i, c[i]); } } /* end of parallel for construct */ }

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