Gather a split 2D array with MPI in C - c

I need to adapt this part of a very long code to mpi in c.
for (i = 0; i < total; i++) {
sum = A[next][0][0]*B[i][0] + A[next][0][1]*B[i][1] + A[next][0][2]*B[i][2];
next++;
while (next < last) {
col = column[next];
sum += A[next][0][0]*B[col][0] + A[next][0][1]*B[col][1] + A[next][0][2]*B[col][2];
final[col][0] += A[next][0][0]*B[i][0] + A[next][1][0]*B[i][1] + A[next][2][0]*B[i][2];
next++;
}
final[i][0] += sum;}
And I was thinking of code like this:
for (i = 0; i < num_threads; i++) {
for (j = 0; j < total; j++) {
check_thread[i][j] = false;
}
}
part = total / num_threads;
for (i = thread_id * part; i < ((thread_id + 1) * part); i++) {
sum = A[next][0][0]*B[i][0] + A[next][0][1]*B[i][1] + A[next][0][2]*B[i][2];
next++;
while (next < last) {
col = column[next];
sum += A[next][0][0]*B[col][0] + A[next][0][1]*B[col][1] + A[next][0][2]*B[col][2];
if (!check_thread[thread_id][col]) {
check_thread[thread_id][col] = true;
temp[thread_id][col] = 0.0;
}
temp[thread_id][col] += A[next][0][0]*B[i][0] + A[next][1][0]*B[i][1] + A[next][2][0]*B[i][2];
next++;
}
if (!check_thread[thread_id][i]) {
check_thread[thread_id][i] = true;
temp[thread_id][i] = 0.0;
}
temp[thread_id][i] += sum;
}
*
for (i = 0; i < total; i++) {
for (j = 0; j < num_threads; j++) {
if (check_thread[j][i]) {
final[i][0] += temp[j][i];
}
}
}
Then I need to gather all the temporary parts in one, I was thinking of MPI_Allgather and something like this just before the last two for (where *):
MPI_Allgather(temp, (part*sizeof(double)), MPI_DOUBLE, temp, sizeof(**temp), MPI_DOUBLE, MPI_COMM_WORLD);
But I get an execution error, Is it possible to send and receive in the same variable?, if not, what could be the other solution in this case?.

You are calling the MPI_Allgather with the wrong parameters:
MPI_Allgather(temp, (part*sizeof(double)), MPI_DOUBLE, temp, sizeof(**temp), MPI_DOUBLE, MPI_COMM_WORLD);
Instead you should have (source) :
MPI_Allgather
Gathers data from all tasks and distribute the combined data to all
tasks
Input Parameters
sendbuf starting address of send buffer (choice)
sendcount number of elements in send buffer (integer)
sendtype data type of send buffer elements (handle)
recvcount number of elements received from any process (integer)
recvtype data type of receive buffer elements (handle)
comm communicator (handle)
Your sendcount and recvcount arguments are both wrong, instead of (part*sizeof(double)) and sizeof(**temp) you should pass the number of elements from the matrix temp that will be gather by all processes involved.
The matrix can be gather in a single call if that matrix is continuously allocated in memory, if it was created as an array of pointers, then you will have to call MPI_Allgather for each row of the matrix, or use MPI_Allgatherv instead.
Is it possible to send and receive in the same variable?
Yes, by using the In-place Option
When the communicator is an intracommunicator, you can perform an
all-gather operation in-place (the output buffer is used as the input
buffer). Use the variable MPI_IN_PLACE as the value of sendbuf. In
this case, sendcount and sendtype are ignored. The input data of each
process is assumed to be in the area where that process would receive
its own contribution to the receive buffer. Specifically, the outcome
of a call to MPI_Allgather that used the in-place option is identical
to the case in which all processes executed n calls to
MPI_GATHER ( MPI_IN_PLACE, 0, MPI_DATATYPE_NULL, recvbuf,
recvcount, recvtype, root, comm )

Related

Heap corruption with pthreads in C

I've been writing a C program to simulate the motion of n bodies under the influence of gravity. I have a working version that uses a single thread, and I'm attempting to write a version that uses multi-threading with the POSIX pthreads library. Essentially, the program initializes a specified number n of bodies, and stores their randomly selected initial positions as well as masses and radii in an array 'data', made using the pointer-to-pointer method. Data is a pointer in the global scope, and it is allocated the correct amount of memory in the 'populate()' function in main(). Then, I spawn twelve threads (I am using a 6 core processor, so I thought 12 would be a good starting point), and each thread is assigned a set of objects in the simulation. The thread function below calculates the interaction between all objects in 'data' and the object currently being operated on. See the function below:
void* calculate_step(void*index_val) {
int index = * (int *)index_val;
long double x_dist;
long double y_dist;
long double distance;
long double force;
for (int i = 0; i < (rows/nthreads); ++i) { //iterate over every object assigned to this thread
data[i+index][X_FORCE] = 0; //reset all forces to 0
data[i+index][Y_FORCE] = 0;
data[i+index][X_ACCEL] = 0;
data[i+index][X_ACCEL] = 0;
for (int j = 0; j < rows; ++j) { //iterate over every possible pair with this object i
if (i != j && data[j][DELETED] != 1 && data[i+index][DELETED] != 1) { //continue if not comparing an object with itself and if the other object has not been deleted previously.
x_dist = data[j][X_POS] - data[i+index][X_POS];
y_dist = data[j][Y_POS] - data[i+index][X_POS];
distance = sqrtl(powl(x_dist, 2) + powl(y_dist, 2));
if (distance > data[i+index][RAD] + data[j][RAD]) {
force = G * data[i+index][MASS] * data[j][MASS] /
powl(distance, 2); //calculate accel, vel, pos, data for pair of non-colliding objects
data[i+index][X_FORCE] += force * (x_dist / distance);
data[i+index][Y_FORCE] += force * (y_dist / distance);
data[i+index][X_ACCEL] = data[i+index][X_FORCE]/data[i+index][MASS];
data[i+index][X_VEL] += data[i+index][X_ACCEL]*dt;
data[i+index][X_POS] += data[i+index][X_VEL]*dt;
data[i+index][Y_ACCEL] = data[i+index][Y_FORCE]/data[i+index][MASS];
data[i+index][Y_VEL] += data[i+index][Y_ACCEL]*dt;
data[i+index][Y_POS] += data[i+index][Y_VEL]*dt;
}
else{
if (data[i+index][MASS] < data[j][MASS]) {
int temp;
temp = i;
i = j;
j = temp;
} //conserve momentum
data[i+index][X_VEL] = (data[i+index][X_VEL] * data[i+index][MASS] + data[j][X_VEL] * data[j][MASS])/(data[i+index][MASS] + data[i+index][MASS]);
data[i+index][Y_VEL] = (data[i+index][Y_VEL] * data[i+index][MASS] + data[j][Y_VEL] * data[j][MASS])/(data[i+index][MASS] + data[i+index][MASS]);
//conserve center of mass position
data[i+index][X_POS] = (data[i+index][X_POS] * data[i+index][MASS] + data[j][X_POS] * data[j][MASS])/(data[i+index][MASS] + data[i+index][MASS]);
data[i+index][Y_POS] = (data[i+index][Y_POS] * data[i+index][MASS] + data[j][Y_POS] * data[j][MASS])/(data[i+index][MASS] + data[i+index][MASS]);
//conserve mass
data[i+index][MASS] += data[j][MASS];
//increase radius proportionally to dM
data[i+index][RAD] = powl(powl(data[i+index][RAD], 3) + powl(data[j][RAD], 3), ((long double) 1 / (long double) 3));
data[j][DELETED] = 1;
data[j][MASS] = 0;
data[j][RAD] = 0;
}
}
}
}
return NULL;
}
This calculates values for velocity, acceleration, etc. and writes them to the array. Each thread does this once for each object assigned to it (i.e. 36 objects means each thread calculates values for 3 objects). The thread then returns and the main loop jumps to the next time step (usually increments of 0.01 seconds), and the process repeats again. If two balls collide, their masses, momenta and centers of mass are added, and one of the objects' 'DELETED' index in its row in the array is marked with a row. This object is then ignored in all future iterations. See the main loop below:
int main() {
pthread_t *thread_array; //pointer to future thread array
long *thread_ids;
short num_obj;
short sim_time;
printf("Number of objects to simulate: \n");
scanf("%hd", &num_obj);
num_obj = num_obj - num_obj%12;
printf("Timespan of the simulation: \n");
scanf("%hd", &sim_time);
printf("Length of time steps: \n");
scanf("%f", &dt);
printf("Relative complexity score: %.2f\n", (((float)sim_time/dt)*((float)(num_obj^2)))/1000);
thread_array = malloc(nthreads*sizeof(pthread_t));
thread_ids = malloc(nthreads*sizeof(long));
populate(num_obj);
int index;
for (int i = 0; i < nthreads; ++i) { //initialize all threads
}
time_t start = time(NULL);
print_data();
for (int i = 0; i < (int)((float)sim_time/dt); ++i) { //main loop of simulation
for (int j = 0; j < nthreads; ++j) {
index = j*(rows/nthreads);
thread_ids[j] = j;
pthread_create(&thread_array[j], NULL, calculate_step, &index);
}
for (int j = 0; j < nthreads; ++j) {
pthread_join(thread_array[j], NULL);
//pthread_exit(NULL);
}
}
time_t end = time(NULL) - start;
printf("\n");
print_data();
printf("Took %zu seconds to simulate %d frames with %d objects initially, now %d objects.\n", end, (int)((float)sim_time/dt), num_obj, rows);
}
Every time the program runs, I get the following message:
Number of objects to simulate:
36
Timespan of the simulation:
10
Length of time steps:
0.01
Relative complexity score: 38.00
Process finished with exit code -1073740940 (0xC0000374)
which seams to indicate the heap is getting corrupted. I am guessing this has to do with the data array pointer being a global variable, but that was my workaround for only being allowed to pass one arg to the pthreads function.
I have tried stepping through the program with the debugger, and it seems it works when I run it in debug mode (I am using CLion), but not in regular compile mode. Furthermore, when i debug the program and it outputs the values of the data array for the last simulation 'frame', the first chunk of values which were supposed to be handled by the first thread that spawns are unchanged. When I go through it with the debugger however I can see that thread being created in the thread generation loop. What are some issues with this code structure and what could be causing the heap corruption and the first thread doing nothing?

MPI_Gather on 2D matrix only showing data gather from master process [duplicate]

This question already has answers here:
MPI_Bcast a dynamic 2d array
(5 answers)
Closed 2 years ago.
I am trying to use MPI_Gather to gather individual two-dimensional arrays into the master process for it to then print out the contents of the the entire matrix. I split the workload across num_processes processes and have each work on their own private matrix.
I'll give a snippet of my code along with some pseudo-code to demonstrate what I'm doing. Note I created my own MPI_Datatype as I am transferring struct types.
Point **matrix = malloc(sizeof(Point *) * (num_rows/ num_processes));
for (i = 0; i < num_rows/num_processes; i++)
matrix [i] = malloc(sizeof(Point) * num_cols);
for( i = 0; i< num_rows/num_processes; i++)
for (j = 0; j < num_cols; j++)
matrix[i][j] = *Point type*
if (processor == 0) {
full_matrix = malloc(sizeof(Point *) * num_rows);
for (i = 0; i < num_rows/num_processes; i++)
matrix [i] = malloc(sizeof(Point) * num_cols);
MPI_Gather(matrix, num_rows/num_processes*num_cols, MPI_POINT_TYPE, full_matrix, num_rows/num_processes*num_cols, MPI_POINT_TYPE, 0, MPI_COMM_WORLD);
} else {
MPI_Gather(matrix, num_rows/num_processes*num_cols, MPI_POINT_TYPE, NULL, 0, MPI_DATATYPE_NULL, 0, MPI_COMM_WORLD);
}
// ...print full_matrix...
The double for-loop prior to the gather computes the correct values as my own testing showed, but the gather onto full_matrix only contains the data from its own processes, i.e. the master process, as its printing later showed.
I'm having trouble figuring out why this is given the master process transfers the data correctly. Is the problem how I allocate memory for each process?
The problem is that MPI_Gather expects the contents of the buffer to be adjacent in memory, but calling malloc repeatedly doesn't guarantee that, as each invocation can return a pointer to an arbitrary memory position.
The solution is to store the Matrix in a whole chunk of memory, like so:
Point *matrix = malloc(sizeof(Point) * (num_rows / num_processes) * num_cols);
With this method you will have to access the data in the form matrix[i * N + j]. If you want to keep the current code, you can create the adjacent memory as before, and use another vector to store a pointer to each row:
Point *matrixAdj = malloc(sizeof(Point) * (num_rows / num_processes) * num_cols);
Point **matrix = malloc(sizeof(Point *) * num_rows);
for (int i = 0; i < num_rows; ++i) {
matrix[i] = &matrixAdj[i * num_rows];
}

scatter variable length data

I'm a new programmer to MPI. I'm writing a simple program to multiply a matrix by a vector. What I do is I first broadcast the vector to all the nodes and then send a bunch of rows of the matrix to each of the nodes using scatter.
My problem is, the number of rows in the array is not a multiple of the number of nodes available. So different nodes end up having different number of rows. At the moment I'm using point to point communication in a loop to do this. But I prefer if I could use MPI_Scatter instead. But MPI_Scatter only sends data of same length to all the nodes.
Is there anyway that I could use scatter to send data even when each of nodes get different size of data chunk?
MPI_Scatterv is made for exactly this case. You specify both a vector of sendcounts, as well as a vector of offset. It can be a bit tricky to properly create those, so there is an example:
int remainder = rows % comm_size;
int local_rows = (rows / comm_size)
if (comm_rank < remainder) {
local_rows++;
}
int* sendcounts = NULL;
int* displacements = NULL;
double* data = NULL;
if (comm_rank = root) {
data = ...;
sendcounts = malloc(sizeof(int) * comm_size);
displacements = malloc(sizeof(int) * comm_size);
int sum = 0;
for (int i = 0; i < comm_size; i++) {
sendcounts[i] = (rows / comm_size) * columns;
if (remainder > 0) {
sendcounts[i] += columns;
remainder--;
}
displacements[i] = sum;
sum += sendcounts[i];
}
}
double* local_data = malloc(sizeof(*local_data) * local_rows * columns);
MPI_Scatterv(data, sendcounts, displacements, MPI_DOUBLE,
local_data, local_rows * columns, MPI_DOUBLE, root, MPI_COMM_WORLD);

MPI runtime error: Either Scatterv count error, segmentationfault, or gets stuck

/*
Matricefilenames:
small matrix A.bin of dimension 100 × 50
small matrix B.bin of dimension 50 × 100
large matrix A.bin of dimension 1000 × 500
large matrix B.bin of dimension 500 × 1000
An MPI program should be implemented such that it can
• accept two file names at run-time,
• let process 0 read the A and B matrices from the two data files,
• let process 0 distribute the pieces of A and B to all the other processes,
• involve all the processes to carry out the the chosen parallel algorithm
for matrix multiplication C = A * B ,
• let process 0 gather, from all the other processes, the different pieces
of C ,
• let process 0 write out the entire C matrix to a data file.
*/
int main(int argc, char *argv[]) {
printf("Oblig 2 \n");
double **matrixa;
double **matrixb;
int ma,na,my_ma,my_na;
int mb,nb,my_mb,my_nb;
int i,j,k;
int myrank,numprocs;
int konstanta,konstantb;
MPI_Init(&argc,&argv);
MPI_Comm_rank(MPI_COMM_WORLD,&myrank);
MPI_Comm_size(MPI_COMM_WORLD,&numprocs);
if(myrank==0) {
read_matrix_binaryformat ("small_matrix_A.bin", &matrixa, &ma, &na);
read_matrix_binaryformat ("small_matrix_B.bin", &matrixb, &mb, &nb);
}
//mpi broadcast
MPI_Bcast(&ma,1,MPI_INT,0,MPI_COMM_WORLD);
MPI_Bcast(&mb,1,MPI_INT,0,MPI_COMM_WORLD);
MPI_Bcast(&na,1,MPI_INT,0,MPI_COMM_WORLD);
MPI_Bcast(&nb,1,MPI_INT,0,MPI_COMM_WORLD);
fflush(stdout);
int resta = ma % numprocs;//rest antall som har den største verdien
//int restb = mb % numprocs;
if (myrank == 0) {
printf("ma : %d",ma);
fflush(stdout);
printf("mb : %d",mb);
fflush(stdout);
}
MPI_Barrier(MPI_COMM_WORLD);
if (resta == 0) {
my_ma = ma / numprocs;
printf("null rest\n ");
fflush(stdout);
} else {
if (myrank < resta) {
my_ma = ma / numprocs + 1;//husk + 1
} else {
my_ma = ma / numprocs; //heltalls divisjon gir nedre verdien !
}
}
my_na = na;
my_nb = nb;
double **myblock = malloc(my_ma*sizeof(double*));
for(i=0;i<na;i++) {
myblock[i] = malloc(my_na*sizeof(double));
}
//send_cnt for scatterv
//________________________________________________________________________________________________________________________________________________
int* send_cnta = (int*)malloc(numprocs*sizeof(int));//array med antall elementer sendt til hver prosess array[i] = antall elementer , i er process
int tot_elemsa = my_ma*my_na;
MPI_Allgather(&tot_elemsa,1,MPI_INT,&send_cnta[0],1,MPI_INT,MPI_COMM_WORLD);//arrays i c må sendes &array[0]
//send_disp for scatterv
//__________________________________________________________________________________
int* send_dispa = (int*)malloc(numprocs*sizeof(int)); //hvorfor trenger disp
// int* send_dispb = (int*)malloc(numprocs*sizeof(int));
//disp hvor i imagechars første element til hver prosess skal til
fflush(stdout);
if(resta==0) {
send_dispa[myrank]=myrank*my_ma*my_na;
} else if(myrank<=resta) {
if(myrank<resta) {
send_dispa[myrank]=myrank*my_ma*my_na;
} else {//my_rank == rest
send_dispa[myrank]=myrank*(my_ma+1)*my_na;
konstanta=myrank*(my_ma+1)*my_na;
}
}
MPI_Bcast(&konstanta,1,MPI_INT,resta,MPI_COMM_WORLD);
if (myrank>resta){
send_dispa[myrank]=((myrank-resta)*(my_ma*my_na))+konstanta;
}
MPI_Allgather(&send_dispa[myrank],1,MPI_INT,&send_dispa[0],1,MPI_INT,MPI_COMM_WORLD);
//___________________________________________________________________________________
printf("print2: %d" , myrank);
fflush(stdout);
//recv_buffer for scatterv
double *recv_buffera=malloc((my_ma*my_na)*sizeof(double));
MPI_Scatterv(&matrixa[0], &send_cnta[0], &send_dispa[0], MPI_UNSIGNED_CHAR, &recv_buffera[0], my_ma*my_na, MPI_UNSIGNED_CHAR, 0, MPI_COMM_WORLD);
for(i=0; i<my_ma; i++) {
for(j=0; j<my_na; j++) {
myblock[i][j]=recv_buffera[i*my_na + j];
}
}
MPI_Finalize();
return 0;
}
OLD:I get three type of errors. I can get scatterv count error, segmentationfault 11, or the processes just get stuck. It seems to be random which error I get. I run the code with 2 procs each time. When it gets stuck it gets stuck before the printf("print2: %d" , myrank);. When my friend runs the code on his own computer also with two prosesses, he does not get past by the first MPI_Bcast. Nothing is printed out when he runs it. Here is a link for the errors I get: http://justpaste.it/zs0
UPDATED PROBLEM: Now I get only a segmentation fault after " printf("print2: %d" , myrank); " before the scatterv call. EVEN if I remove all the code after the printf statement I get the segmentation fault, but only if I run the code for more than two procs.
I'm having a little difficulty tracing what you were trying to do. I think you're making the scatterv call more complicated than it needs to be though. Here's a snippet I had from a similar assignment this year. Hopefully it's a clearer example of how scatterv works.
/*********************************************************************
* Scatter A to All Processes
* - Using Scatterv for versatility.
*********************************************************************/
int *send_counts; // Send Counts
int *displacements; // Send Offsets
int chunk; // Number of Rows per Process (- Root)
int chunk_size; // Number of Doubles per Chunk
int remainder; // Number of Rows for Root Process
double * rbuffer; // Receive Buffer
// Do Some Math
chunk = m / (p - 1);
remainder = m % (p - 1);
chunk_size = chunk * n;
// Setup Send Counts
send_counts = malloc(p * sizeof(int));
send_counts[0] = remainder * n;
for (i = 1; i < p; i++)
send_counts[i] = chunk_size;
// Setup Displacements
displacements = malloc(p * sizeof(int));
displacements[0] = 0;
for (i = 1; i < p; i++)
displacements[i] = (remainder * n) + ((i - 1) * chunk_size);
// Allocate Receive Buffer
rbuffer = malloc(send_counts[my_rank] * sizeof(double));
// Scatter A Over All Processes!
MPI_Scatterv(A, // A
send_counts, // Array of counts [int]
displacements, // Array of displacements [int]
MPI_DOUBLE, // Sent Data Type
rbuffer, // Receive Buffer
send_counts[my_rank], // Receive Count - Per Process
MPI_DOUBLE, // Received Data Type
root, // Root
comm); // Comm World
MPI_Barrier(comm);
Also, this causes a segfault on my machine, no mpi... Pretty sure it's the way myblock is being allocated. You should do what #Hristo suggested in the comments. Allocate both matrices and the resultant matrix as flat arrays. That would eliminate the use of double pointers and make your life a whole lot simpler.
#include <stdio.h>
#include <stdlib.h>
void main ()
{
int na = 5;
int my_ma = 5;
int my_na = 5;
int i;
int j;
double **myblock = malloc(my_ma*sizeof(double*));
for(i=0;i<na;i++) {
myblock = malloc(my_na*sizeof(double));
}
unsigned char *recv_buffera=malloc((my_ma*my_na)*sizeof(unsigned char));
for(i=0; i<my_ma; i++) {
for(j=0; j<my_na; j++) {
myblock[i][j]=(float)recv_buffera[i*my_na + j];
}
}
}
Try allocating more like this:
// Allocate A, b, and y. Generate random A and b
double *buff=0;
if (my_rank==0)
{
int A_size = m*n, b_size = n, y_size = m;
int size = (A_size+b_size+y_size)*sizeof(double);
buff = (double*)malloc(size);
if (buff==NULL)
{
printf("Process %d failed to allocate %d bytes\n", my_rank, size);
MPI_Abort(comm,-1);
return 1;
}
// Set pointers
A = buff; b = A+m*n; y = b+n;
// Generate matrix and vector
genMatrix(m, n, A);
genVector(n, b);
}

Sending distributed chunks of a 2D array to the root process in MPI

I have a 2D array which is distributed across a MPI process grid (3 x 2 processes in this example). The values of the array are generated within the process which that chunk of the array is distributed to, and I want to gather all of those chunks together at the root process to display them.
So far, I have the code below. This generates a cartesian communicator, finds out the co-ordinates of the MPI process and works out how much of the array it should get based on that (as the array need not be a multiple of the cartesian grid size). I then create a new MPI derived datatype which will send the whole of that processes subarray as one item (that is, the stride, blocklength and count are different for each process, as each process has different sized arrays). However, when I come to gather the data together with MPI_Gather, I get a segmentation fault.
I think this is because I shouldn't be using the same datatype for sending and receiving in the MPI_Gather call. The data type is fine for sending the data, as it has the right count, stride and blocklength, but when it gets to the other end it'll need a very different derived datatype. I'm not sure how to calculate the parameters for this datatype - does anyone have any ideas?
Also, if I'm approaching this from completely the wrong angle then please let me know!
#include<stdio.h>
#include<array_alloc.h>
#include<math.h>
#include<mpi.h>
int main(int argc, char ** argv)
{
int size, rank;
int dim_size[2];
int periods[2];
int A = 2;
int B = 3;
MPI_Comm cart_comm;
MPI_Datatype block_type;
int coords[2];
float **array;
float **whole_array;
int n = 10;
int rows_per_core;
int cols_per_core;
int i, j;
int x_start, x_finish;
int y_start, y_finish;
/* Initialise MPI */
MPI_Init(&argc, &argv);
/* Get the rank for this process, and the number of processes */
MPI_Comm_size(MPI_COMM_WORLD, &size);
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
if (rank == 0)
{
/* If we're the master process */
whole_array = alloc_2d_float(n, n);
/* Initialise whole array to silly values */
for (i = 0; i < n; i++)
{
for (j = 0; j < n; j++)
{
whole_array[i][j] = 9999.99;
}
}
for (j = 0; j < n; j ++)
{
for (i = 0; i < n; i++)
{
printf("%f ", whole_array[j][i]);
}
printf("\n");
}
}
/* Create the cartesian communicator */
dim_size[0] = B;
dim_size[1] = A;
periods[0] = 1;
periods[1] = 1;
MPI_Cart_create(MPI_COMM_WORLD, 2, dim_size, periods, 1, &cart_comm);
/* Get our co-ordinates within that communicator */
MPI_Cart_coords(cart_comm, rank, 2, coords);
rows_per_core = ceil(n / (float) A);
cols_per_core = ceil(n / (float) B);
if (coords[0] == (B - 1))
{
/* We're at the far end of a row */
cols_per_core = n - (cols_per_core * (B - 1));
}
if (coords[1] == (A - 1))
{
/* We're at the bottom of a col */
rows_per_core = n - (rows_per_core * (A - 1));
}
printf("X: %d, Y: %d, RpC: %d, CpC: %d\n", coords[0], coords[1], rows_per_core, cols_per_core);
MPI_Type_vector(rows_per_core, cols_per_core, cols_per_core + 1, MPI_FLOAT, &block_type);
MPI_Type_commit(&block_type);
array = alloc_2d_float(rows_per_core, cols_per_core);
if (array == NULL)
{
printf("Problem with array allocation.\nExiting\n");
return 1;
}
for (j = 0; j < rows_per_core; j++)
{
for (i = 0; i < cols_per_core; i++)
{
array[j][i] = (float) (i + 1);
}
}
MPI_Barrier(MPI_COMM_WORLD);
MPI_Gather(array, 1, block_type, whole_array, 1, block_type, 0, MPI_COMM_WORLD);
/*
if (rank == 0)
{
for (j = 0; j < n; j ++)
{
for (i = 0; i < n; i++)
{
printf("%f ", whole_array[j][i]);
}
printf("\n");
}
}
*/
/* Close down the MPI environment */
MPI_Finalize();
}
The 2D array allocation routine I have used above is implemented as:
float **alloc_2d_float( int ndim1, int ndim2 ) {
float **array2 = malloc( ndim1 * sizeof( float * ) );
int i;
if( array2 != NULL ){
array2[0] = malloc( ndim1 * ndim2 * sizeof( float ) );
if( array2[ 0 ] != NULL ) {
for( i = 1; i < ndim1; i++ )
array2[i] = array2[0] + i * ndim2;
}
else {
free( array2 );
array2 = NULL;
}
}
return array2;
}
This is a tricky one. You're on the right track, and yes, you will need different types for sending and receiving.
The sending part is easy -- if you're sending the whole subarray array, then you don't even need the vector type; you can send the entire (rows_per_core)*(cols_per_core) contiguous floats starting at &(array[0][0]) (or array[0], if you prefer).
It's the receiving that's the tricky part, as you've gathered. Let's start with the simplest case -- assuming that everything divides evenly so all the blocks have the same size. Then you can use the very helfpul MPI_Type_create_subarray (you could always cobble this together with vector types, but for higher-dimensional arrays this becomes tedious, as you need to create 1 intermediate type for each dimension of the array except the last...
Also, rather than hardcoding the decomposition, you can use the also-helpful MPI_Dims_create to create an as-square-as-possible decomposition of your ranks. Note
that this doesn't necessarily have anything to do with MPI_Cart_create, although you can use it for the requested dimensions. I'm going to skip the cart_create stuff here, not because it's not useful, but because I want to focus on the gather stuff.
So if everyone has the same size of array, then root is receiving the same data type from everyone, and one can use a very simple subarray type to get their data:
MPI_Type_create_subarray(2, whole_array_size, sub_array_size, starts,
MPI_ORDER_C, MPI_FLOAT, &block_type);
MPI_Type_commit(&block_type);
where sub_array_size[] = {rows_per_core, cols_per_core}, whole_array_size[] = {n,n}, and for here, starts[]={0,0} - eg, we'll just assume that everything starts the start.
The reason for this is that we can then use Gatherv to explicitly set the displacements into the array:
for (int i=0; i<size; i++) {
counts[i] = 1; /* one block_type per rank */
int row = (i % A);
int col = (i / A);
/* displacement into the whole_array */
disps[i] = (col*cols_per_core + row*(rows_per_core)*n);
}
MPI_Gatherv(array[0], rows_per_core*cols_per_core, MPI_FLOAT,
recvptr, counts, disps, resized_type, 0, MPI_COMM_WORLD);
So now everyone sends their data in one chunk, and it's received into the type into the right part of the array. For this to work, I've resized the type so that it's extent is just one float, so the displacements can be calculated in that unit:
MPI_Type_create_resized(block_type, 0, 1*sizeof(float), &resized_type);
MPI_Type_commit(&resized_type);
The whole code is below:
#include<stdio.h>
#include<stdlib.h>
#include<math.h>
#include<mpi.h>
float **alloc_2d_float( int ndim1, int ndim2 ) {
float **array2 = malloc( ndim1 * sizeof( float * ) );
int i;
if( array2 != NULL ){
array2[0] = malloc( ndim1 * ndim2 * sizeof( float ) );
if( array2[ 0 ] != NULL ) {
for( i = 1; i < ndim1; i++ )
array2[i] = array2[0] + i * ndim2;
}
else {
free( array2 );
array2 = NULL;
}
}
return array2;
}
void free_2d_float( float **array ) {
if (array != NULL) {
free(array[0]);
free(array);
}
return;
}
void init_array2d(float **array, int ndim1, int ndim2, float data) {
for (int i=0; i<ndim1; i++)
for (int j=0; j<ndim2; j++)
array[i][j] = data;
return;
}
void print_array2d(float **array, int ndim1, int ndim2) {
for (int i=0; i<ndim1; i++) {
for (int j=0; j<ndim2; j++) {
printf("%6.2f ", array[i][j]);
}
printf("\n");
}
return;
}
int main(int argc, char ** argv)
{
int size, rank;
int dim_size[2];
int periods[2];
MPI_Datatype block_type, resized_type;
float **array;
float **whole_array;
float *recvptr;
int *counts, *disps;
int n = 10;
int rows_per_core;
int cols_per_core;
int i, j;
int whole_array_size[2];
int sub_array_size[2];
int starts[2];
int A, B;
/* Initialise MPI */
MPI_Init(&argc, &argv);
/* Get the rank for this process, and the number of processes */
MPI_Comm_size(MPI_COMM_WORLD, &size);
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
if (rank == 0)
{
/* If we're the master process */
whole_array = alloc_2d_float(n, n);
recvptr = &(whole_array[0][0]);
/* Initialise whole array to silly values */
for (i = 0; i < n; i++)
{
for (j = 0; j < n; j++)
{
whole_array[i][j] = 9999.99;
}
}
print_array2d(whole_array, n, n);
puts("\n\n");
}
/* Create the cartesian communicator */
MPI_Dims_create(size, 2, dim_size);
A = dim_size[1];
B = dim_size[0];
periods[0] = 1;
periods[1] = 1;
rows_per_core = ceil(n / (float) A);
cols_per_core = ceil(n / (float) B);
if (rows_per_core*A != n) {
if (rank == 0) fprintf(stderr,"Aborting: rows %d don't divide by %d evenly\n", n, A);
MPI_Abort(MPI_COMM_WORLD,1);
}
if (cols_per_core*B != n) {
if (rank == 0) fprintf(stderr,"Aborting: cols %d don't divide by %d evenly\n", n, B);
MPI_Abort(MPI_COMM_WORLD,2);
}
array = alloc_2d_float(rows_per_core, cols_per_core);
printf("%d, RpC: %d, CpC: %d\n", rank, rows_per_core, cols_per_core);
whole_array_size[0] = n;
sub_array_size [0] = rows_per_core;
whole_array_size[1] = n;
sub_array_size [1] = cols_per_core;
starts[0] = 0; starts[1] = 0;
MPI_Type_create_subarray(2, whole_array_size, sub_array_size, starts,
MPI_ORDER_C, MPI_FLOAT, &block_type);
MPI_Type_commit(&block_type);
MPI_Type_create_resized(block_type, 0, 1*sizeof(float), &resized_type);
MPI_Type_commit(&resized_type);
if (array == NULL)
{
printf("Problem with array allocation.\nExiting\n");
MPI_Abort(MPI_COMM_WORLD,3);
}
init_array2d(array,rows_per_core,cols_per_core,(float)rank);
counts = (int *)malloc(size * sizeof(int));
disps = (int *)malloc(size * sizeof(int));
/* note -- we're just using MPI_COMM_WORLD rank here to
* determine location, not the cart_comm for now... */
for (int i=0; i<size; i++) {
counts[i] = 1; /* one block_type per rank */
int row = (i % A);
int col = (i / A);
/* displacement into the whole_array */
disps[i] = (col*cols_per_core + row*(rows_per_core)*n);
}
MPI_Gatherv(array[0], rows_per_core*cols_per_core, MPI_FLOAT,
recvptr, counts, disps, resized_type, 0, MPI_COMM_WORLD);
free_2d_float(array);
if (rank == 0) print_array2d(whole_array, n, n);
if (rank == 0) free_2d_float(whole_array);
MPI_Finalize();
}
Minor thing -- you don't need the barrier before the gather. In fact, you hardly ever really need a barrier, and they're expensive operations for a few reasons, and can hide problems -- my rule of thumb is to never, ever, use barriers unless you know exactly why the rule needs to be broken in this case. In this case in particular, the collective gather routine does exactly the same syncronization as the barrier, so just use that.
Now, moving onto the harder stuff. If things don't divide evenly, you have a few options. The simplest, though not necessarily the best, is just to pad the array so that it does divide evenly, even if just for this operation.
If you can arrange it so that the number of columns does divide evenly, even if the number of rows doesn't, then you can still use the gatherv and create a vector type for each part of the row, and gatherv that the appropriate number of rows from each processor. That would work fine.
If you definately have the case where neither can be counted on to divide, and you can't pad data for sending, then there are three sub-options I can see:
As susterpatt suggests, do point-to-point. For small numbers of tasks, this is fine, but as it gets larger, this will be significantly less efficient than the collective operations.
Create a communicator consisting of all the processors not on the outer edges, and use exactly the code above to gather their code; and then point-to-point the edge tasks' data.
Don't gather to process 0 at all; use the Distributed array type to describe the layout of the array, and use MPI-IO to write all the data to a file; once that's done, you can have process zero display the data in some way if you like.
It looks like the first argument to you MPI_Gather call should probably be array[0], and not array.
Also, if you need to get different amounts of data from each rank, you might be better off using MPI_Gatherv.
Finally, not that gathering all your data in once place to do output is not scalable in many circumstances. As the amount of data grows, eventually, it will exceed the memory available to rank 0. You might be much better off distributing the output work (if you are writing to a file, using MPI IO or other library calls) or doing point-to-point sends to rank 0 one at a time, to limit the total memory consumption.
On the other hand, I would not recommend coordinating each of your ranks printing to standard output, one after another, because some major MPI implementations don't guarantee that standard output will be produced in order. Cray's MPI, in particular, jumbles up standard output pretty thoroughly if multiple ranks print.
Accordding to this (emphasis by me):
The type-matching conditions for the collective operations are more strict than the corresponding conditions between sender and receiver in point-to-point. Namely, for collective operations, the amount of data sent must exactly match the amount of data specified by the receiver. Distinct type maps between sender and receiver are still allowed.
Sounds to me like you have two options:
Pad smaller submatrices so that all processes send the same amount of data, then crop the matrix back to its original size after the Gather. If you're feeling adventurous, you might try defining the receiving typemap so that paddings are automatically overwritten during the Gather operation, thus eliminating the need for the crop afterwards. This could get a bit complicated though.
Fall back to point-to-point communication. Much more straightforward, but possibly higher communication costs.
Personally, I'd go with option 2.

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