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

2. 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

3. 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

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

5. 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) 

6. #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

7. 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 */

8. 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

9. 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

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

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

12. 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

13. 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

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

15. 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

16. 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

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

18. 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?

19. 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

20. 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

21. 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

22. 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]

23. 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

24. 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

25. 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.

26. Questions