Hybrid Parallel Programming Introduction ITCS4145/5145, Parallel Programming C. Ferner and B. Wilkinson March 13, 2014. hybrid.ppt

Interconnection Network Hybrid Systems Since most computers are multi-core, most clusters have both shared-memory and distributed-memory. Interconnection Network Core Memory Multi-core Computer Core Memory Multi-core Computer Core Memory Multi-core Computer Core Memory Multi-core Computer

Hybrid (MPI-OpenMP) Parallel Computing We can use MPI to run processes concurrently on each computer We can use OpenMP to run threads concurrently on each core of a computer Advantage: we can make use of shared-memory where communication is required Why? – Because inter-computer communication is an order of magnitude slower than synchronization

Message-passing routines used to pass messages between computer systems and threads execute on each computer system using the multiple cores on the system

How to create a hybrid OpenMP- MPI program Write source code with both MPI routines and OpenMP directives/routines mpicc uses gcc linked with appropriate MPI libraries. gcc supports OpenMP with –fopenmp option. So can use that: mpicc -fopenmp -o hybrid hybrid.c Execute as an MPI program. For example on UNCC cluster cci-gridgw.uncc.edu mpiexec.hydra -f <machinesfile> -n <number of processes> ./hybrid (VERY IMPORTANT -- NOT FROM cci-grid05) 

#include <stdio. h> #include <string #include <stdio.h> #include <string.h> #include <stddef.h> #include <stdlib.h> #include "mpi.h" #define CHUNKSIZE 10 #define N 100 void openmp_code(){ … / next slide } main(int argc, char **argv ) { char message[20]; int i,rank, size, type=99; MPI_Status status; MPI_Init(&argc, &argv); MPI_Comm_size(MPI_COMM_WORLD,&size); MPI_Comm_rank(MPI_COMM_WORLD, &rank); if(rank == 0) { strcpy(message, "Hello, world"); for (i=1; i<size; i++) MPI_Send(message, 13, MPI_CHAR, i, type, MPI_COMM_WORLD); } else MPI_Recv(message, 20, MPI_CHAR, 0, type, MPI_COMM_WORLD, &status); openmp_code(); //all MPI processes run OpenMP code, no message passing printf( "Message from process =%d : %.13s\n", rank,message); MPI_Finalize(); Example

void openmp_code(){ int nthreads, tid, i, chunk; float a[N], b[N], c[N]; for (i=0; i < N; i++) a[i] = b[i] = i * 1.0; // initialize arrays chunk = CHUNKSIZE; #pragma omp parallel shared(a,b,c,nthreads,chunk) private(i,tid) { tid = omp_get_thread_num(); if (tid == 0) { nthreads = omp_get_num_threads(); printf("Number of threads = %d\n", nthreads); } printf("Thread %d starting...\n",tid); #pragma omp for schedule(dynamic,chunk) for (i=0; i<N; i++) { c[i] = a[i] + b[i]; printf("Thread %d: c[%d]= %f\n",tid,i,c[i]); } /* end of parallel section */

Parallelizing a double for loop int main(int argc, char *argv[]) { int i, j, blksz, rank, P, tid; char *usage = "Usage: %s \n"; FILE *fd; char message[80]; MPI_Init (&argc, &argv); MPI_Comm_size (MPI_COMM_WORLD, &P); MPI_Comm_rank (MPI_COMM_WORLD, &rank); blksz = (int) ceil (((double) N)/P); #pragma omp parallel private (tid, i, j) { tid = omp_get_thread_num(); for (i = rank*blksz; i < min((rank + 1) * blksz, N); i++) { #pragma omp for for (j = 0; j < N; j++) { printf ("rank %d, thread %d: executing loop iteration i=%d j=%d\n",rank tid,i,j); } Loop i parallelized across computers Loop j parallelized across threads Code and results from Dr. Ferner

rank 0, thread 4: executing loop iteration i=0 j=4 rank 0, thread 2: executing loop iteration i=0 j=2 rank 0, thread 1: executing loop iteration i=0 j=1 rank 1, thread 0: executing loop iteration i=2 j=0 rank 1, thread 4: executing loop iteration i=2 j=4 rank 1, thread 2: executing loop iteration i=2 j=2 rank 1, thread 3: executing loop iteration i=2 j=3 rank 0, thread 0: executing loop iteration i=0 j=0 rank 1, thread 1: executing loop iteration i=2 j=1 rank 0, thread 3: executing loop iteration i=0 j=3 rank 2, thread 2: executing loop iteration i=4 j=2 rank 2, thread 0: executing loop iteration i=4 j=0 rank 2, thread 3: executing loop iteration i=4 j=3 rank 2, thread 4: executing loop iteration i=4 j=4 rank 2, thread 1: executing loop iteration i=4 j=1 rank 0, thread 2: executing loop iteration i=1 j=2 rank 0, thread 4: executing loop iteration i=1 j=4 rank 0, thread 3: executing loop iteration i=1 j=3 rank 0, thread 0: executing loop iteration i=1 j=0 rank 0, thread 1: executing loop iteration i=1 j=1 rank 1, thread 0: executing loop iteration i=3 j=0 rank 1, thread 2: executing loop iteration i=3 j=2 rank 1, thread 3: executing loop iteration i=3 j=3 rank 1, thread 1: executing loop iteration i=3 j=1 rank 1, thread 4: executing loop iteration i=3 j=4 Simple Example Result

Hybrid (MPI-OpenMP) Parallel Computing Caution: Using the hybrid approach may not necessarily result in increased performance though – will strongly depend upon application.

Matrix Multiplication, C = A * B where A is an n x l matrix and B is an l x m matrix.

One way to parallelize Matrix multiplication using hybrid approach Parallelize i loop into partitioned among the computers with MPI for (i = 0; i < N; i++) for (j = 0; j < N; j++) { c[i][j] = 0.0; for (k = 0; k < N; k++) { c[i][j] += a[i][k] * b[k][j]; } Parallelize j loop into partitioned among cores within each computer, using OpenMP

Matrix Multiplication MPI_Init (&argc, &argv); MPI_Comm_size (MPI_COMM_WORLD, &P); MPI_Comm_rank (MPI_COMM_WORLD, &rank); blksz = (int) ceil (((double) N)/P); MPI_Scatter (a,N*blksz,MPI_FLOAT, a,N*blksz,MPI_FLOAT,0,MPI_COMM_WORLD); MPI_Bcast (b, N*N, MPI_FLOAT, 0, MPI_COMM_WORLD); #pragma omp parallel private (tid, i, j, k) { for (i = 0; i < blksz && rank * blksz < N; i++) { #pragma omp for nowait for (j = 0; j < N; j++) { c[i][j] = 0.0; for (k = 0; k < N; k++) { c[i][j] += a[i][k] * b[k][j]; } Parallelize i loop into partitioned among the computers with MPI Parallelize j loop on each computer into partitioned using OpenMP Code and results from Dr. Ferner

Matrix Multiplication Results $ diff out MMULT.o5356 1c1 < elapsed_time= 1.525183 (seconds) --- >elapsed_time= 0.659652 (seconds) $ diff out MMULT.o5357 > elapsed_time= 0.626821 (seconds) $ Sequential Execution Time Hybrid Execution Time Sequential Execution Time MPI-only Execution Time Hybrid did not do better than MPI only

Perhaps we could do better parallelizing the i loop both with MPI and OpenMP Parallelize i loop into partitioned among the computers/threads with MPI and OpenMP #pragma omp parallel private (tid, i, j, k) { #pragma omp for nowait for (i = 0; i < blksz && rank * blksz < N; i++) { for (j = 0; j < N; j++) { c[i][j] = 0.0; for (k = 0; k < N; k++) { c[i][j] += a[i][k] * b[k][j]; } But this loop is too complicated for OpenMP j loop not parallelized

An if statement can simplify the loop #pragma omp parallel private (tid, i, j,k) { #pragma omp for nowait for (i = 0; i < blksz; i++) { if (rank * blksz < N) { for (j = 0; j < N; j++) { c[i][j] = 0.0; for (k = 0; k < N; k++) { c[i][j] += a[i][k] * b[k][j]; } An if statement can simplify the loop

Matrix Multiplication Results $ diff out MMULT.o5356 1c1 < elapsed_time= 1.525183 (seconds) --- >elapsed_time= 0.688119 (seconds) Sequential Execution Time Hybrid Execution Time Still not better

Discussion Point Why does the hybrid approach not outperform MPI-only for this problem? For what kinds of problem might a hybrid approach do better?

Hybrid Parallel Programming with the Paraguin compiler The Paraguin compiler can also create hybrid programs This is because it uses mpicc, it will pass the OpenMP pragma through to the resulting source

Compiling First we need to compile to source code scc -DPARAGUIN -D__x86_64__ matrixmult.c -.out.c Then we can compile with MPI and openmp mpicc –fopenmp matrixmult.out.c –o matrixmult.out

Hybrid Matrix Multiplication using Paraguin #pragma paraguin begin_parallel #pragma paraguin scatter a #pragma paraguin bcast b #pragma paraguin forall for (i = 0; i < N; i++) { #pragma omp parallel for private(tID, j,k) num_threads(4) for (j = 0; j < N; j++) { c[i][j] = 0.0; for (k = 0; k < N; k++) { c[i][j] = c[i][j] + a[i][k] * b[k][j]; } The i loop will be partitioned among the computers The j loop will be partitioned among the 4 cores within a computer

Debug Statements <pid 0, thread 1>: c[0][1] += a[0][0] * b[1][0] <pid 0, thread 1>: c[0][1] += a[0][1] * b[1][1] <pid 0, thread 1>: c[0][1] += a[0][2] * b[1][2] <pid 0, thread 2>: c[0][2] += a[0][0] * b[2][0] <pid 0, thread 2>: c[0][2] += a[0][1] * b[2][1] <pid 0, thread 2>: c[0][2] += a[0][2] * b[2][2] <pid 1, thread 1>: c[1][1] += a[1][0] * b[1][0] <pid 1, thread 1>: c[1][1] += a[1][1] * b[1][1] <pid 1, thread 1>: c[1][1] += a[1][2] * b[1][2] <pid 2, thread 1>: c[2][1] += a[2][0] * b[1][0] <pid 2, thread 1>: c[2][1] += a[2][1] * b[1][1] <pid 2, thread 1>: c[2][1] += a[2][2] * b[1][2] <pid 0, thread 0>: c[0][0] += a[0][0] * b[0][0] <pid 0, thread 0>: c[0][0] += a[0][1] * b[0][1] <pid 0, thread 0>: c[0][0] += a[0][2] * b[0][2] <pid 2, thread 0>: c[2][0] += a[2][0] * b[0][0] <pid 2, thread 0>: c[2][0] += a[2][1] * b[0][1] <pid 2, thread 0>: c[2][0] += a[2][2] * b[0][2] <pid 1, thread 0>: c[1][0] += a[1][0] * b[0][0] <pid 1, thread 0>: c[1][0] += a[1][1] * b[0][1] <pid 1, thread 0>: c[1][0] += a[1][2] * b[0][2] <pid 1, thread 2>: c[1][2] += a[1][0] * b[2][0] <pid 1, thread 2>: c[1][2] += a[1][1] * b[2][1] <pid 1, thread 2>: c[1][2] += a[1][2] * b[2][2] <pid 2, thread 2>: c[2][2] += a[2][0] * b[2][0] <pid 2, thread 2>: c[2][2] += a[2][1] * b[2][1] <pid 2, thread 2>: c[2][2] += a[2][2] * b[2][2]

What does not work with Paraguin Consider: #pragma omp parallel structured_block Example: #pragma omp parallel private(tID) num_threads(4) { tID = omp_get_thread_num(); printf("<pid %d>: tid = %d\n", __guin_rank, tID); } Very Important Opening brace must be on a new line

What does not work with Paraguin The SUIF compiler removes the braces because they are not associated with a control structure A #pragma is not a control structure, but rather a pre-processor directive. After compiling with scc: #pragma omp parallel private(tID) num_threads(4) tID = omp_get_thread_num(); printf("<pid %d>: tid = %d\n", __guin_rank, tID); Braces are removed

The Fix The trick is to put in a control structure that basically does nothing: dummy = 0; #pragma omp parallel private(tID) num_threads(4) if (dummy == 0) { tID = omp_get_thread_num(); printf ("<pid %d>: tid = %d\n", __guin_rank, tID); } Note: “if (1)” does not work If statement will always be true. This code is basically left intact.

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