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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) OpenMP exercises

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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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