MPI_Scatterv segfault - c

I'm just starting out with MPI programming and decided to make a simple distributed qsort using OpenMPI. To distribute parts of the array I want to sort I'm trying to use MPI_Scatterv, however the following code segfaults on me:
#include <stdio.h>
#include <string.h>
#include <stdlib.h>
#include <time.h>
#include <mpi.h>
#define ARRAY_SIZE 26
#define BUFFER_SIZE 2048
int main(int argc, char** argv) {
int my_rank, nr_procs;
int* data_in, *data_out;
int* sizes;
int* offsets;
srand(time(0));
MPI_Init(&argc, &argv);
MPI_Comm_size(MPI_COMM_WORLD, &nr_procs);
MPI_Comm_rank(MPI_COMM_WORLD, &my_rank);
// everybody generates the control tables
int nr_workers = nr_procs-1;
sizes = malloc(sizeof(int)*nr_workers);
offsets = malloc(sizeof(int)*nr_workers);
int nr_elems = ARRAY_SIZE/nr_workers;
// basic distribution
for (int i = 0; i < nr_workers; ++i) {
sizes[i] = nr_elems;
}
// distribute the remainder
int left = ARRAY_SIZE%nr_workers;
int curr_worker = 0;
while (left) {
++sizes[curr_worker];
curr_worker = (++curr_worker)%nr_workers;
--left;
}
// offsets
int curr_offset = 0;
for (int i = 0; i < nr_workers; ++i) {
offsets[i] = curr_offset;
curr_offset += sizes[i];
}
if (my_rank == 0) {
// root
data_in = malloc(sizeof(int)*ARRAY_SIZE);
data_out = malloc(sizeof(int)*ARRAY_SIZE);
for (int i = 0; i < ARRAY_SIZE; ++i) {
data_in[i] = rand();
}
for (int i = 0; i < nr_workers; ++i) {
printf("%d at %d\n", sizes[i], offsets[i]);
}
MPI_Scatterv (data_in, sizes, offsets, MPI_INT, data_out, ARRAY_SIZE, MPI_INT, 0, MPI_COMM_WORLD);
} else {
// worker
printf("%d has %d elements!\n",my_rank, sizes[my_rank-1]);
// alloc the input buffer
data_in = malloc(sizeof(int)*sizes[my_rank-1]);
MPI_Scatterv(NULL, NULL, NULL, MPI_INT, data_in, sizes[my_rank-1], MPI_INT, 0, MPI_COMM_WORLD);
printf("%d got:\n", my_rank);
for (int i = 0; i < sizes[my_rank-1]; ++i) {
printf("%d ", data_in[i]);
}
printf("\n");
}
MPI_Finalize();
return 0;
}
How would I go about using Scatterv? Am I doing something wrong with allocating my input buffer from inside the worker code?

I changed some part in your code to get something working.
MPI_Scatter() will send data to every processors, including himself. According to your program, processor 0 expects ARRAY_SIZE integers, but sizes[0] is much smaller.
There are other problems on other processus : MPI_Scatter will send sizes[my_rank] integers, but sizes[my_rank-1] will be expected...
Here is a code that scatters data_in from 0 to all processors, including 0. Therefore i added 1 to nr_workers :
#include <stdio.h>
#include <string.h>
#include <stdlib.h>
#include <time.h>
#include <mpi.h>
#define ARRAY_SIZE 26
#define BUFFER_SIZE 2048
int main(int argc, char** argv) {
int my_rank, nr_procs;
int* data_in, *data_out;
int* sizes;
int* offsets;
srand(time(0));
MPI_Init(&argc, &argv);
MPI_Comm_size(MPI_COMM_WORLD, &nr_procs);
MPI_Comm_rank(MPI_COMM_WORLD, &my_rank);
// everybody generates the control tables
int nr_workers = nr_procs;
sizes = malloc(sizeof(int)*nr_workers);
offsets = malloc(sizeof(int)*nr_workers);
int nr_elems = ARRAY_SIZE/nr_workers;
// basic distribution
for (int i = 0; i < nr_workers; ++i) {
sizes[i] = nr_elems;
}
// distribute the remainder
int left = ARRAY_SIZE%nr_workers;
int curr_worker = 0;
while (left) {
++sizes[curr_worker];
curr_worker = (++curr_worker)%nr_workers;
--left;
}
// offsets
int curr_offset = 0;
for (int i = 0; i < nr_workers; ++i) {
offsets[i] = curr_offset;
curr_offset += sizes[i];
}
if (my_rank == 0) {
// root
data_in = malloc(sizeof(int)*ARRAY_SIZE);
for (int i = 0; i < ARRAY_SIZE; ++i) {
data_in[i] = rand();
printf("%d %d \n",i,data_in[i]);
}
for (int i = 0; i < nr_workers; ++i) {
printf("%d at %d\n", sizes[i], offsets[i]);
}
} else {
printf("%d has %d elements!\n",my_rank, sizes[my_rank]);
}
data_out = malloc(sizeof(int)*sizes[my_rank]);
MPI_Scatterv (data_in, sizes, offsets, MPI_INT, data_out, sizes[my_rank], MPI_INT, 0, MPI_COMM_WORLD);
printf("%d got:\n", my_rank);
for (int i = 0; i < sizes[my_rank]; ++i) {
printf("%d ", data_out[i]);
}
printf("\n");
free(data_out);
if(my_rank==0){
free(data_in);
}
MPI_Finalize();
return 0;
}
Regarding memory managment, data_in and data_out should be freed at the end of the code.
Is it what you wanted to do ? Good luck with qsort ! I think you are not the first one to sort integers using MPI. See parallel sort using mpi. Your way to generate random numbers on the 0 processus and then scatter them is the right way to go. I think you will be interrested by his TD_Trier() function for communication. Even if you change tri_fusion(T, 0, size - 1); for qsort(...)...
Bye,
Francis

Related

MPI Scatter Array of Matrices Struct

I have an array of type Matrix structs which the program got from user's input. I need to distribute the matrices to processes with OpenMPI. I tried using Scatter but I am quite confused about the arguments needed for the program to work (and also how to receive the data in each local arrays). Here is my current code:
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <mpi.h>
#define nil NULL
#define NMAX 100
#define DATAMAX 1000
#define DATAMIN -1000
typedef struct Matrix
{
int mat[NMAX][NMAX]; // Matrix cells
int row_eff; // Matrix effective row
int col_eff; // Matrix effective column
} Matrix;
void init_matrix(Matrix *m, int nrow, int ncol)
{
m->row_eff = nrow;
m->col_eff = ncol;
for (int i = 0; i < m->row_eff; i++)
{
for (int j = 0; j < m->col_eff; j++)
{
m->mat[i][j] = 0;
}
}
}
Matrix input_matrix(int nrow, int ncol)
{
Matrix input;
init_matrix(&input, nrow, ncol);
for (int i = 0; i < nrow; i++)
{
for (int j = 0; j < ncol; j++)
{
scanf("%d", &input.mat[i][j]);
}
}
return input;
}
void print_matrix(Matrix *m)
{
for (int i = 0; i < m->row_eff; i++)
{
for (int j = 0; j < m->col_eff; j++)
{
printf("%d ", m->mat[i][j]);
}
printf("\n");
}
}
int main(int argc, char **argv)
{
MPI_Init(&argc, &argv);
// Get number of processes
int size;
MPI_Comm_size(MPI_COMM_WORLD, &size);
// Get process rank
int rank;
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
// Get matrices from user inputs
int kernel_row, kernel_col, num_targets, target_row, target_col;
// reads kernel's row and column and initalize kernel matrix from input
scanf("%d %d", &kernel_row, &kernel_col);
Matrix kernel = input_matrix(kernel_row, kernel_col);
// reads number of target matrices and their dimensions.
// initialize array of matrices and array of data ranges (int)
scanf("%d %d %d", &num_targets, &target_row, &target_col);
Matrix *arr_mat = (Matrix *)malloc(num_targets * sizeof(Matrix));
for (int i = 0; i < num_targets; i++)
{
arr_mat[i] = input_matrix(target_row, target_col);
}
// Get number of matrices per process
int num_mat_per_proc = ceil(num_targets / size);
// Init local matrices and scatter the global matrices
Matrix *local_mats = (Matrix *)malloc(num_mat_per_proc * sizeof(Matrix));
MPI_Scatter(arr_mat, sizeof(local_mats), MPI_BYTE, &local_mats, sizeof(local_mats), MPI_BYTE, 0, MPI_COMM_WORLD);
if (rank == 0)
{
// Range arrays -> array of convolution results
int arr_range[num_targets];
printf("From master \n");
for (int i = 0; i < 3; i++)
{
print_matrix(&arr_mat[i]);
}
}
else
{
printf("From slave %d = \n", rank);
print_matrix(&local_mats[0]);
}
MPI_Finalize();
}
So here's a few doubts I have about the current implementation:
Can I accept the input just like that or should I make it so that it only happens in rank 0?
How do I implement the scatter part and possibly using Scatterv because the amount of arrays might not be divisible to the number of processes?
Can I accept the input just like that or should I make it so that it
only happens in rank 0?
No, You should use command line arguments or read from file as best practice.
If you want to use scanf, then use it inside rank 0. STDIN is forwarded to rank 0 (this is not supported in standard as far as I know, But I guess this should work and will be implementation dependent)
How do I implement the scatter part and possibly using Scatterv
because the amount of arrays might not be divisible to the number of
processes?
If you different size to send for different processes, then you should use scatterv.
Scatter Syntax:
MPI_Scatter(
void* send_data,
int send_count,
MPI_Datatype send_datatype,
void* recv_data,
int recv_count,
MPI_Datatype recv_datatype,
int root,
MPI_Comm communicator)
Your usage:
MPI_Scatter(arr_mat, sizeof(local_mats), MPI_BYTE, &local_mats, sizeof(local_mats), MPI_BYTE, 0, MPI_COMM_WORLD);
Potential error points:
In send_count: Size to send (as Gilles Gouaillardet Pointed out in comments). Sizeof(local_mats) instead it should be num_mat_per_proc * sizeof(Matrix).
recv_count: I believe size to receive should not be sizeof(local_mats).
Since you use the same type (MPI_BYTES) for SEND and RECV, your send_count == recv_count

c mpi (spawn scatter gather) for variable number of processes

I am building up an example with variable no. of processes and bind them to the sockets in a small network with different architecture and number of cpus.
I compile and run with:
mpiicpc avg_4.c -qopenmp -axSSE4.2,AVX,CORE-AVX2 -O3 -par-affinity=noverbose,granularity=core,compact -o b
mpiexec.hydra -machinefile f19 -genv I_MPI_PIN=1 -genv I_MPI_PIN_DOMAIN=socket -genv I_MPI_PIN_ORDER=compact -n 1 ./b
The network (master + slave19) f19 is:
s19:1
ma:1
#include <stdio.h>
#include <stdlib.h>
#include <omp.h>
#include <sched.h>
#include <mpi.h>
int *create_mlu(int n_omp, int ws) {
int *mlu = (int *)calloc(n_omp * ws, sizeof(int));
for (int i = 0; i < ws; i++)
for (int j = 0; j < n_omp; j++)
mlu[j + i*n_omp] = j + 100 * i;
return mlu;
}
int *C4_Re(int *mal, int n_omp, int wr, int ws) {
int *rM8 = (int *)malloc(sizeof(int) * n_omp);
char nod[MPI_MAX_PROCESSOR_NAME];
int n_l; MPI_Get_processor_name(nod, &n_l);
#pragma omp parallel for
for (int i = 0; i < n_omp; i++) {
rM8[i] = mal[i] + 10 * omp_get_thread_num();
printf("ws%2d\t\tmpi%2d\t\tmaxTh%2d\t\tmaxPr%2d\t\tomp%2d\t\tcore%3d\t\trM8%4d\t\tnod %s\n", ws, wr, omp_get_num_threads(), omp_get_num_procs(), omp_get_thread_num(), sched_getcpu(), rM8[i], nod);
}
return rM8;
}
int main(void) {
MPI_Init(NULL, NULL);
int ts[2] = {7, 9}; //no of processes
for (int t = 0; t < 2; t++) {
int ws = ts[t];
int errcodes[ws];
MPI_Comm parentcomm, intercomm;
MPI_Comm_get_parent(&parentcomm);
if (parentcomm == MPI_COMM_NULL) {
MPI_Comm_spawn("./b", MPI_ARGV_NULL, ws, MPI_INFO_NULL, 0, MPI_COMM_WORLD, &intercomm, errcodes);
//printf("I'm the parent.\n");
}
else {
int wr; MPI_Comm_rank(MPI_COMM_WORLD, &wr);// printf("wr %d\n", wr);
//int ps; MPI_Comm_size(parentcomm, &ps);// printf("ps %d\n", ps);
//int pr; MPI_Comm_rank(parentcomm, &pr);// printf("pr %d\n", pr);
int n_omp = 8, *mlu = NULL;
if (wr == 0) {
mlu = create_mlu(n_omp, ws);
//for (int i = 0; i < n_omp*ws; i++) printf("\tmlu[%2d] = %d\n", i, mlu[i]);
}
int *mal = (int *)malloc(n_omp * sizeof(int));
MPI_Scatter(mlu, n_omp, MPI_INT, mal, n_omp, MPI_INT, 0, MPI_COMM_WORLD);
//for (int i = 0; i < n_omp; i++) printf("\t\tmal[%2d] = %d\trank %d\n", i, mal[i], wr);
int *rM8 = NULL;
rM8 = C4_Re(mal, n_omp, wr, ws);
int *rS8 = NULL;
if (wr == 0)
rS8 = (int *)malloc(sizeof(int) * ws * n_omp);
MPI_Gather(rM8, n_omp, MPI_INT, rS8, n_omp, MPI_INT, 0, MPI_COMM_WORLD);
if (wr == 0) {
//for (int i = 0; i < n_omp * ws; i++) printf("\t\trS8[%2d] = %d\n", i, rS8[i]);
free(mlu);
free(rS8); }
free(mal);
free(rM8);
}
//fflush(stdout);
}
fflush(stdout);
MPI_Finalize();
return 0;
}
I have a memory corruption which I need help to find
Some results look like
ws 7 rM8-37253944 nod ma mpi 7 maxTh 6 maxPr 6 omp 4 core 4
but they must look like
ws 7 rM8 624 nod ma mpi 6 maxTh 6 maxPr 6 omp 2 core 2
addition questions
1 - why using parentcomm for Scatter and Gather is not correct? In my opinion parentcomm is the new communicator
2 - should I create different comunicators for 7 and 9?
3 - mpicc gives me wrong results I don't know why

distributed algorithm in C

I am a beginner in C. I have to create a distributed architecture with the library MPI. The following code is:
#include <stdio.h>
#include <string.h>
#include <stdlib.h>
#include <time.h>
#include <mpi.h>
int main(int argc, char **argv)
{
int N, w = 1, L = 2, M = 50; // with N number of threads
int T= 2;
int myid;
int buff;
float mit[N][T]; // I initialize a 2d array
for(int i = 0; i < N; ++i){
mit[i][0]= M / (float) N;
for (int j = 1; j < T; ++j){
mit[i][j] = 0;
}
}
float tab[T]; // 1d array
MPI_Status stat;
/*********************************************
start
*********************************************/
MPI_Init(&argc,&argv); // Initialisation
MPI_Comm_size(MPI_COMM_WORLD, &N);
MPI_Comm_rank(MPI_COMM_WORLD, &myid);
for(int j = 0; j < T; j++) {
for(int i = 0; i < N; i++) { // I iterate for each slave
if (myid !=0) {
float y = ((float) rand()) / (float) RAND_MAX;
mit[i][j + 1] = mit[i][j]*(1 + w * L * y);
buff=mit[i][j+1];
MPI_Send(&buff, 128, MPI_INT, 0, 0, MPI_COMM_WORLD); // I send the variable buff to the master
buff=0;
}
if( myid == 0 ) { // Master
for(int i = 1; i < N; i++){
MPI_Recv(&buff, 128, MPI_INT, i, 0, MPI_COMM_WORLD, &stat);
tab[j] += buff; // I need to receive all the variables buff sent by the salves, sum them and stock into the tab at the index j
}
printf("\n%.20f\n",tab[j]); // I print the result of the sum at index j
}
}
}
MPI_Finalize();
return 0;
}
}
I use the command in the terminal: mpicc .c -o my_file to compile the program
Then mpirun -np 101 my_file_c to start the program with 101 threads
But the problem is I have the following error int the terminal:
It seems that [at least] one of the processes that was started with
> mpirun did not invoke MPI_INIT before quitting (it is possible that
> more than one process did not invoke MPI_INIT -- mpirun was only
> notified of the first one, which was on node n0).
>
> mpirun can *only* be used with MPI programs (i.e., programs that
> invoke MPI_INIT and MPI_FINALIZE). You can use the "lamexec" program
> to run non-MPI programs over the lambooted nodes.
It seems that I have a problem with the master but i don't know why...
Any idea ???
Thank you :)
This behavior is very likely the result of a memory corruption.
You cannot
int buff=mit[i][j+1];
MPI_Send(&buff, 128, MPI_INT, ...);
depending on what you want to achieve, you can try instead
int buff=mit[i][j+1];
MPI_Send(&buff, 1, MPI_INT, ...);
// ...
MPI_Recv(&buff, 1, MPI_INT, ...);
or
int *buff=&mit[i][j+1];
MPI_Send(buff, 128, MPI_INT, ...);
// fix MPI_Recv()

How to handle MPI sendcount of zero

What is the correct way to handle a sendcount = 0 when using MPI_Gatherv (or any other function that requires a sendcount) when setting up the displs argument?
I have data that needs to be received by all processors, but all processors might not have any data to send themselves. As an MWE, I tried (on just two processors):
#include <stdlib.h>
#include <stdio.h>
#include <mpi.h>
int main(void)
{
int ntasks;
int thistask;
int n = 0;
int i;
int totcounts = 0;
int *data;
int *rbuf;
int *rcnts;
int *displs;
int *master_data;
int *master_displs;
// Set up mpi
MPI_Init(NULL, NULL);
MPI_Comm_size(MPI_COMM_WORLD, &ntasks);
MPI_Comm_rank(MPI_COMM_WORLD, &thistask);
// Allocate memory for arrays needed by allgatherv
rbuf = calloc(ntasks, sizeof(int));
rcnts = calloc(ntasks, sizeof(int));
displs = calloc(ntasks, sizeof(int));
master_displs = calloc(ntasks, sizeof(int));
// Initialize the counts and displacement arrays
for(i = 0; i < ntasks; i++)
{
rcnts[i] = 1;
displs[i] = i;
}
// Allocate data on just one task, but not others
if(thistask == 1)
{
n = 3;
data = calloc(n, sizeof(int));
for(i = 0; i < n; i++)
{
data[i] = i;
}
}
// Get n so each other processor knows about what others are sending
MPI_Allgatherv(&n, 1, MPI_INT, rbuf, rcnts, displs, MPI_INT, MPI_COMM_WORLD);
// Now that we know how much data each processor is sending, we allocate the array
// to hold it all
for(i = 0; i < ntasks; i++)
{
totcounts += rbuf[i];
}
master_data = calloc(totcounts, sizeof(int));
// Get displs for master data
master_displs[0] = 0;
for(i = 1; i < ntasks; i++)
{
master_displs[i] = master_displs[i - 1] + rbuf[i - 1];
}
// Send each processor's data to all others
MPI_Allgatherv(&data, n, MPI_INT, master_data, rbuf, master_displs, MPI_INT, MPI_COMM_WORLD);
// Print it out to see if it worked
if(thistask == 0)
{
for(i = 0; i < totcounts; i++)
{
printf("master_data[%d] = %d\n", i, master_data[i]);
}
}
// Free
if(thistask == 1)
{
free(data);
}
free(rbuf);
free(rcnts);
free(displs);
free(master_displs);
free(master_data);
MPI_Finalize();
return 0;
}
The way that I've set up master_displs works when every processor has a non-zero n (that is, they have data to send). In this case, both entries will be zero. However, the results of this program are garbage. How would I set up the master_displs array to ensure that master_data holds the correct information (in this case, just master_data[i] = i, as received from task 1)?

Problem with MPI matrix-matrix multiply: Cluster slower than single computer

I code a small program using MPI to parallelize matrix-matrix multiplication. The problem is: When running the program on my computer, it takes about 10 seconds to complete, but about 75 seconds on a cluster. I think I have some synchronization problem, but I cannot figure it out (yet).
Here's my source code:
/*matrix.c
mpicc -o out matrix.c
mpirun -np 11 out
*/
#include <mpi.h>
#include <stdio.h>
#include <string.h>
#include <stdlib.h>
#define N 1000
#define DATA_TAG 10
#define B_SENT_TAG 20
#define FINISH_TAG 30
int master(int);
int worker(int, int);
int main(int argc, char **argv) {
int myrank, p;
double s_time, f_time;
MPI_Init(&argc,&argv);
MPI_Comm_rank(MPI_COMM_WORLD, &myrank);
MPI_Comm_size(MPI_COMM_WORLD, &p);
if (myrank == 0) {
s_time = MPI_Wtime();
master(p);
f_time = MPI_Wtime();
printf("Complete in %1.2f seconds\n", f_time - s_time);
fflush(stdout);
}
else {
worker(myrank, p);
}
MPI_Finalize();
return 0;
}
int *read_matrix_row();
int *read_matrix_col();
int send_row(int *, int);
int recv_row(int *, int, MPI_Status *);
int send_tag(int, int);
int write_matrix(int *);
int master(int p) {
MPI_Status status;
int *a; int *b;
int *c = (int *)malloc(N * sizeof(int));
int i, j; int num_of_finish_row = 0;
while (1) {
for (i = 1; i < p; i++) {
a = read_matrix_row();
b = read_matrix_col();
send_row(a, i);
send_row(b, i);
//printf("Master - Send data to worker %d\n", i);fflush(stdout);
}
wait();
for (i = 1; i < N / (p - 1); i++) {
for (j = 1; j < p; j++) {
//printf("Master - Send next row to worker[%d]\n", j);fflush(stdout);
b = read_matrix_col();
send_row(b, j);
}
}
for (i = 1; i < p; i++) {
//printf("Master - Announce all row of B sent to worker[%d]\n", i);fflush(stdout);
send_tag(i, B_SENT_TAG);
}
//MPI_Barrier(MPI_COMM_WORLD);
for (i = 1; i < p; i++) {
recv_row(c, MPI_ANY_SOURCE, &status);
//printf("Master - Receive result\n");fflush(stdout);
num_of_finish_row++;
}
//printf("Master - Finish %d rows\n", num_of_finish_row);fflush(stdout);
if (num_of_finish_row >= N)
break;
}
//printf("Master - Finish multiply two matrix\n");fflush(stdout);
for (i = 1; i < p; i++) {
send_tag(i, FINISH_TAG);
}
//write_matrix(c);
return 0;
}
int worker(int myrank, int p) {
int *a = (int *)malloc(N * sizeof(int));
int *b = (int *)malloc(N * sizeof(int));
int *c = (int *)malloc(N * sizeof(int));
int i;
for (i = 0; i < N; i++) {
c[i] = 0;
}
MPI_Status status;
int next = (myrank == (p - 1)) ? 1 : myrank + 1;
int prev = (myrank == 1) ? p - 1 : myrank - 1;
while (1) {
recv_row(a, 0, &status);
if (status.MPI_TAG == FINISH_TAG)
break;
recv_row(b, 0, &status);
wait();
//printf("Worker[%d] - Receive data from master\n", myrank);fflush(stdout);
while (1) {
for (i = 1; i < p; i++) {
//printf("Worker[%d] - Start calculation\n", myrank);fflush(stdout);
calc(c, a, b);
//printf("Worker[%d] - Exchange data with %d, %d\n", myrank, next, prev);fflush(stdout);
exchange(b, next, prev);
}
//printf("Worker %d- Request for more B's row\n", myrank);fflush(stdout);
recv_row(b, 0, &status);
//printf("Worker %d - Receive tag %d\n", myrank, status.MPI_TAG);fflush(stdout);
if (status.MPI_TAG == B_SENT_TAG) {
break;
//printf("Worker[%d] - Finish calc one row\n", myrank);fflush(stdout);
}
}
//wait();
//printf("Worker %d - Send result\n", myrank);fflush(stdout);
send_row(c, 0);
for (i = 0; i < N; i++) {
c[i] = 0;
}
}
return 0;
}
int *read_matrix_row() {
int *row = (int *)malloc(N * sizeof(int));
int i;
for (i = 0; i < N; i++) {
row[i] = 1;
}
return row;
}
int *read_matrix_col() {
int *col = (int *)malloc(N * sizeof(int));
int i;
for (i = 0; i < N; i++) {
col[i] = 1;
}
return col;
}
int send_row(int *row, int dest) {
MPI_Send(row, N, MPI_INT, dest, DATA_TAG, MPI_COMM_WORLD);
return 0;
}
int recv_row(int *row, int src, MPI_Status *status) {
MPI_Recv(row, N, MPI_INT, src, MPI_ANY_TAG, MPI_COMM_WORLD, status);
return 0;
}
int wait() {
MPI_Barrier(MPI_COMM_WORLD);
return 0;
}
int calc(int *c_row, int *a_row, int *b_row) {
int i;
for (i = 0; i < N; i++) {
c_row[i] = c_row[i] + a_row[i] * b_row[i];
//printf("%d ", c_row[i]);
}
//printf("\n");fflush(stdout);
return 0;
}
int exchange(int *row, int next, int prev) {
MPI_Request request; MPI_Status status;
MPI_Isend(row, N, MPI_INT, next, DATA_TAG, MPI_COMM_WORLD, &request);
MPI_Irecv(row, N, MPI_INT, prev, MPI_ANY_TAG, MPI_COMM_WORLD, &request);
MPI_Wait(&request, &status);
return 0;
}
int send_tag(int worker, int tag) {
MPI_Send(0, 0, MPI_INT, worker, tag, MPI_COMM_WORLD);
return 0;
}
int write_matrix(int *matrix) {
int i;
for (i = 0; i < N; i++) {
printf("%d ", matrix[i]);
}
printf("\n");
fflush(stdout);
return 0;
}
Well, you have a fairly small matrix (N=1000), and secondly you distribute your algorithm on a row/column basis rather than blocked.
For a more realistic version using better algorithms, you might want to acquire an optimized BLAS library (e.g. GOTO is free), test single-thread performance with that one, then get PBLAS and link it against your optimized BLAS, and compare MPI parallel performance using the PBLAS version.
I see some errors in your program:
First, why are you calling the wait function since its implementation is simply calling MPI_Barrier. MPI_Barrier is a primitive synchronization that blocks all threads until they reach the "barrier" by calling MPI_Barrier. My question is: do you want the master to be synchronized with the workers? In this context, that would not be optimal because a worker doesn't need to wait for the master to begin its calculation.
Second, there are 2 unnecessary for loops.
for (i = 1; i < N / (p - 1); i++) {
for (j = 1; j < p; j++) {
b = read_matrix_col();
send_row(b, j);
}
}
for (i = 1; i < p; i++) {
send_tag(i, B_SENT_TAG);
}
In the first i-loop, you don't use the variable in your statement. Since the j-loop and the second i-loop are the same, you could do:
for (i = 0; i < p; i++) {
b = read_matrix_col();
send_row(b, j);
send_tag(i, B_SENT_TAG);
}
In terms of data transfer, your program is not optimized because you are sending an array of 1000 integers of data for each data transfer. There should be a better way to optimise the data transfer, but I will let you look at it. So make the corrections I told you and tell us what is your new performance.
And as #janneb said, you can use the BLAS library for better performance for matrix multiplication. Good luck!
I did not look over your code, but I can provide some hints about why your result may not unexpected:
As already mentioned, N=1000 may be too small. You should make more tests to see the scalability of your program (try setting N=100, 500, 1000, 5000, 10000, etc.) and compare results on both your system and the cluster.
Compare results between your system (one processor I presume) and a single processor on the cluster. Usually in production environments like servers or clusters a single processor is less powerful than the best processors designed for desktop use, but they provide stability, reliability and other features useful for environments which run 24h/day at full capacity.
If your processor has multiple cores, more than one MPI processes may run at the same time and synchronization between them is negligible compared to the synchronization between nodes in a cluster.
Are the nodes from the cluster statically assigned to you? Maybe other users' programs can be scheduled on the nodes you are running at the same time as you.
Read documentation about the cluster's architecture. Some architectures may be more suitable for particular classes of problems.
Assess latency of the network of the cluster. Ping-ing from each node to another many times and computing the mean value may give a rough estimate.
Last but perhaps the most important, your algorithm may not be optimal. Read a/some books on matrix multiplication (I can recommend "Matrix Computations", Golub and Van Loan).

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