Suppose I have a very large array of things and I have to do some operation on all these things.
In case operation fails for one element, I want to stop the work [this work is distributed across number of processors] across all the array.
I want to achieve this while keeping the number of sent/received messages to a minimum.
Also, I don't want to block processors if there is no need to.
How can I do it using MPI?
This seems to be a common question with no easy answer. Both other answer have scalability issues. The ring-communication approach has linear communication cost, while in the one-sided MPI_Win-solution, a single process will be hammered with memory requests from all workers. This may be fine for low number of ranks, but pose issues when increasing the rank count.
Non-blocking collectives can provide a more scalable better solution. The main idea is to post a MPI_Ibarrier on all ranks except on one designated root. This root will listen to point-to-point stop messages via MPI_Irecv and complete the MPI_Ibarrier once it receives it.
The tricky part is that there are four different cases "{root, non-root} x {found, not-found}" that need to be handled. Also it can happen that multiple ranks send a stop message, requiring an unknown number of matching receives on the root. That can be solved with an additional reduction that counts the number of ranks that sent a stop-request.
Here is an example how this can look in C:
#include <stdio.h>
#include <stdlib.h>
#include <mpi.h>
const int iter_max = 10000;
const int difficulty = 20000;
int find_stuff()
{
int num_iters = rand() % iter_max;
for (int i = 0; i < num_iters; i++) {
if (rand() % difficulty == 0) {
return 1;
}
}
return 0;
}
const int stop_tag = 42;
const int root = 0;
int forward_stop(MPI_Request* root_recv_stop, MPI_Request* all_recv_stop, int found_count)
{
int flag;
MPI_Status status;
if (found_count == 0) {
MPI_Test(root_recv_stop, &flag, &status);
} else {
// If we find something on the root, we actually wait until we receive our own message.
MPI_Wait(root_recv_stop, &status);
flag = 1;
}
if (flag) {
printf("Forwarding stop signal from %d\n", status.MPI_SOURCE);
MPI_Ibarrier(MPI_COMM_WORLD, all_recv_stop);
MPI_Wait(all_recv_stop, MPI_STATUS_IGNORE);
// We must post some additional receives if multiple ranks found something at the same time
MPI_Reduce(MPI_IN_PLACE, &found_count, 1, MPI_INT, MPI_SUM, root, MPI_COMM_WORLD);
for (found_count--; found_count > 0; found_count--) {
MPI_Recv(NULL, 0, MPI_CHAR, MPI_ANY_SOURCE, stop_tag, MPI_COMM_WORLD, &status);
printf("Additional stop from: %d\n", status.MPI_SOURCE);
}
return 1;
}
return 0;
}
int main()
{
MPI_Init(NULL, NULL);
int rank;
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
srand(rank);
MPI_Request root_recv_stop;
MPI_Request all_recv_stop;
if (rank == root) {
MPI_Irecv(NULL, 0, MPI_CHAR, MPI_ANY_SOURCE, stop_tag, MPI_COMM_WORLD, &root_recv_stop);
} else {
// You may want to use an extra communicator here, to avoid messing with other barriers
MPI_Ibarrier(MPI_COMM_WORLD, &all_recv_stop);
}
while (1) {
int found = find_stuff();
if (found) {
printf("Rank %d found something.\n", rank);
// Note: We cannot post this as blocking, otherwise there is a deadlock with the reduce
MPI_Request req;
MPI_Isend(NULL, 0, MPI_CHAR, root, stop_tag, MPI_COMM_WORLD, &req);
if (rank != root) {
// We know that we are going to receive our own stop signal.
// This avoids running another useless iteration
MPI_Wait(&all_recv_stop, MPI_STATUS_IGNORE);
MPI_Reduce(&found, NULL, 1, MPI_INT, MPI_SUM, root, MPI_COMM_WORLD);
MPI_Wait(&req, MPI_STATUS_IGNORE);
break;
}
MPI_Wait(&req, MPI_STATUS_IGNORE);
}
if (rank == root) {
if (forward_stop(&root_recv_stop, &all_recv_stop, found)) {
break;
}
} else {
int stop_signal;
MPI_Test(&all_recv_stop, &stop_signal, MPI_STATUS_IGNORE);
if (stop_signal)
{
MPI_Reduce(&found, NULL, 1, MPI_INT, MPI_SUM, root, MPI_COMM_WORLD);
printf("Rank %d stopping after receiving signal.\n", rank);
break;
}
}
};
MPI_Finalize();
}
While this is not the simplest code, it should:
Introduce no additional blocking
Scale with the implementation of a barrier (usually O(log N))
Have a worst-case-latency from one found, to all stop of 2 * loop time ( + 1 p2p + 1 barrier + 1 reduction).
If many/all ranks find a solution at the same time, it still works but may be less efficient.
A possible strategy to derive this global stop condition in a non-blocking fashion is to rely on MPI_Test.
scenario
Consider that each process posts an asynchronous receive of type MPI_INT to its left rank with a given tag to build a ring. Then start your computation. If a rank encounters the stop condition it sends its own rank to its right rank. In the meantime each rank uses MPI_Test to check for the completion of the MPI_Irecv during the computation if it is completed then enter a branch first waiting the message and then transitively propagating the received rank to the right except if the right rank is equal to the payload of the message (not to loop).
This done you should have all processes in the branch, ready to trigger an arbitrary recovery operation.
Complexity
The topology retained is a ring as it minimizes the number of messages at most (n-1) however it augments the propagation time. Other topologies could be retained with more messages but lower spatial complexity for example a tree with a n.ln(n) complexity.
Implementation
Something like this.
int rank, size;
MPI_Init(&argc,&argv);
MPI_Comm_rank( MPI_COMM_WORLD, &rank);
MPI_Comm_size( MPI_COMM_WORLD, &size);
int left_rank = (rank==0)?(size-1):(rank-1);
int right_rank = (rank==(size-1))?0:(rank+1)%size;
int stop_cond_rank;
MPI_Request stop_cond_request;
int stop_cond= 0;
while( 1 )
{
MPI_Irecv( &stop_cond_rank, 1, MPI_INT, left_rank, 123, MPI_COMM_WORLD, &stop_cond_request);
/* Compute Here and set stop condition accordingly */
if( stop_cond )
{
/* Cancel the left recv */
MPI_Cancel( &stop_cond_request );
if( rank != right_rank )
MPI_Send( &rank, 1, MPI_INT, right_rank, 123, MPI_COMM_WORLD );
break;
}
int did_recv = 0;
MPI_Test( &stop_cond_request, &did_recv, MPI_STATUS_IGNORE );
if( did_recv )
{
stop_cond = 1;
MPI_Wait( &stop_cond_request, MPI_STATUS_IGNORE );
if( right_rank != stop_cond_rank )
MPI_Send( &stop_cond_rank, 1, MPI_INT, right_rank, 123, MPI_COMM_WORLD );
break;
}
}
if( stop_cond )
{
/* Handle the stop condition */
}
else
{
/* Cleanup */
MPI_Cancel( &stop_cond_request );
}
That is a question I've asked myself a few times without finding any completely satisfactory answer... The only thing I thought of (beside MPI_Abort() that does it but which is a bit extreme) is to create an MPI_Win storing a flag that will be raise by whichever process facing the problem, and checked by all processes regularly to see if they can go on processing. This is done using non-blocking calls, the same way as described in this answer.
The main weaknesses of this are:
This depends on the processes to willingly check the status of the flag. There is no immediate interruption of their work to notifying them.
The frequency of this checking must be adjusted by hand. You have to find the trade-off between the time you waste processing data while there's no need to because something happened elsewhere, and the time it takes to check the status...
In the end, what we would need is a way of defining a callback action triggered by an MPI call such as MPI_Abort() (basically replacing the abort action by something else). I don't think this exists, but maybe I overlooked it.
Related
In MPI, if I perform an MPI_Scatter on MPI_COMM_WORLD, all the nodes receive some data (including the sending root).
How do I perform an MPI_Scatter from a root node to all the other nodes and make sure the root node does not receive any data?
Is creating a new MPI_Comm containing all the nodes but the root a viable approach?
Let's imagine your code looks like that:
int rank, size; // rank of the process and size of the communicator
int root = 0; // root process of our scatter
int recvCount = 4; // or whatever
double *sendBuf = rank == root ? new double[recvCount * size] : NULL;
double *recvBuf = new double[recvCount];
MPI_Scatter( sendBuf, recvCount, MPI_DOUBLE,
recvBuf, recvCount, MPI_DOUBLE,
root, MPI_COMM_WORLD );
So in here, indeed, the root process will send data to itself although this could be avoided.
Here are the two obvious methods that come to mind to achieve that.
Using MPI_IN_PLACE
The call to MPI_Scatter() wouldn't have to change. The only change in the code would be for the definition of the receiving buffer, which would become something like this:
double *recvBuf = rank == root ?
static_cast<double*>( MPI_IN_PLACE ) :
new double[recvCount];
Using MPI_Scatterv()
With that, you'd have to define an array of integers describing the individual receiving sizes, an array of displacements describing the starting indexes and use them in a call to MPI_Scatterv() which would replace you call to MPI_Scatter() like this:
int sendCounts[size] = {recvCount}; // everybody receives recvCount data
sendCounts[root] = 0; // but the root process
int displs[size];
for ( int i = 0; i < size; i++ ) {
displs[i] = i * recvCount;
}
MPI_Scatterv( sendBuf, sendCounts, displs, MPI_DOUBLE,
recvBuf, recvCount, MPI_DOUBLE,
root, MPI_COMM_WORLD );
Of course in both cases no data would be on receiving buffer for process root and this would have to be accounted for in your code.
I personally prefer the first option, but both work.
Regarding MPI_Comm_create, the MPI standard says
MPI_COMM_CREATE(comm, group, newcomm)
... The function is collective and must be called by all processes in
the group of comm.
I took this to mean that, for instance, if the comm argument is MPI_COMM_WORLD, then all processes must call MPI_COMM_WORLD.
However, I wrote a variation on a code available on the internet demonstrating the use of MPI_Comm_create. It is below. You can see that there are two spots where MPI_Comm_create is called, and not by all processes. And yet the code runs just fine.
Did I get lucky? Did I stumble onto some implementation-dependent feature? Am I misunderstanding the MPI standard? Is the idea that the two calls together result in everyone calling MPI_Comm_create so "at the end of the day" it's OK? Thanks. Here's the code:
#include <stdio.h>
#include <stdlib.h>
#include "mpi.h"
int main(int argc, char **argv) {
MPI_Comm even_comm, odd_comm;
MPI_Group even_group, odd_group, world_group;
int id, even_id, odd_id;
int *even_ranks, *odd_ranks;
int num_ranks, num_even_ranks, num_odd_ranks;
int err_mpi, i, j;
int even_sum, odd_sum;
err_mpi = MPI_Init(&argc, &argv);
MPI_Comm_size(MPI_COMM_WORLD, &num_ranks);
MPI_Comm_rank(MPI_COMM_WORLD, &id);
MPI_Comm_group(MPI_COMM_WORLD, &world_group);
num_even_ranks = (num_ranks+1)/2;
even_ranks = (int *)malloc(num_even_ranks*sizeof(int));
j=0;
for (i=0; i<num_ranks; i++){
if (i%2 == 0) {
even_ranks[j] = i;
j++;
}
}
num_odd_ranks = num_ranks/2;
odd_ranks = (int *)malloc(num_odd_ranks*sizeof(int));
j=0;
for (i=0; i<num_ranks; i++){
if (i%2 == 1) {
odd_ranks[j] = i;
j++;
}
}
if (id%2 == 0){
MPI_Group_incl(world_group, num_even_ranks, even_ranks, &even_group);
// RIGHT HERE, all procs are NOT calling!
MPI_Comm_create(MPI_COMM_WORLD, even_group, &even_comm);
MPI_Comm_rank(even_comm, &even_id);
odd_id = -1;
} else {
MPI_Group_incl(world_group, num_odd_ranks, odd_ranks, &odd_group);
// RIGHT HERE, all procs are NOT calling!
MPI_Comm_create(MPI_COMM_WORLD, odd_group, &odd_comm);
MPI_Comm_rank(odd_comm, &odd_id);
even_id = -1;
}
// Just to have something to do, we'll some up the ranks of
// the various procs in each communicator.
if (even_id != -1) MPI_Reduce(&id, &even_sum, 1, MPI_INT, MPI_SUM, 0, even_comm);
if (odd_id != -1) MPI_Reduce(&id, &odd_sum, 1, MPI_INT, MPI_SUM, 0, odd_comm);
if (odd_id == 0) printf("odd sum: %d\n", odd_sum);
if (even_id == 0) printf("even sum: %d\n", even_sum);
MPI_Finalize();
}
Although the comm_create is called from two different lines of code, the important point is that all processes in COMM_WORLD are calling comm_create at the same time. The fact that they are not from the same line of code is not relevant - in fact, the MPI library doesn't even know where comm_create is being called from.
A simpler example would be calling Barrier from the two branches; regardless of which line is executed, all processes are executing the same barrier so the code will work as expected.
You could easily rewrite the code to be called from the same line: simply have variables called "num_ranks", "mycomm", "mygroup" and "myid" and an array called "ranks" and set them equal to the even or odd variables depending on rank. All processes could then call:
MPI_Group_incl(world_group, num_ranks, ranks, &mygroup);
MPI_Comm_create(MPI_COMM_WORLD, mygroup, &mycomm);
MPI_Comm_rank(mycomm, &myid);
and if you really wanted you could reassign these back afterwards, e.g.
if (id%2 == 0) even_comm = mycomm;
I am trying to implement a logical ring with MPI; where every process receives a message from process with id one less than that of current process and forwards to the next process in cyclic fashion. My aim is to traffic buffer such that it loses some messages or maybe puts them in out of order.
A cycle of communication will finish when a message dispatched by root node comes back again to root.
here is the code that I have tried:
I am just including relevant parts of it.
if(procId!=root)
{
sleep(100);
while(1)
{
tm = MPI_Wtime();
MPI_Irecv( &message, STR_LEN, MPI_CHAR,
((procId-1)>=0?(procId-1):(numProc-1)),RETURN_DATA_TAG,
MPI_COMM_WORLD,&receiveRequest);
MPI_Wait(&receiveRequest,&status);
printf("%d: Received\n",procId);
if(!strncmp(message,"STOP",4)&&(procId==(numProc-1)))
break;
MPI_Ssend( message, STR_LEN, MPI_CHAR,
(procId+1)%numProc, SEND_DATA_TAG, MPI_COMM_WORLD);
if(!strncmp(message,"STOP",4))
break;
printf("%d: Sent\n",procId);
}
}
else
{
for(iter=0;iter<benchmarkSize;iter++)
{
//Synthesize the message
message[STR_LEN-1] = '\0';
iErr = MPI_Bsend( message, STR_LEN, MPI_CHAR,
(root+1)%numProc, SEND_DATA_TAG, MPI_COMM_WORLD);
if (iErr != MPI_SUCCESS) {
char error_string[BUFSIZ];
int length_of_error_string;
MPI_Error_string(iErr, error_string, &length_of_error_string);
fprintf(stderr, "%3d: %s\n", procId, error_string);
}
tm = MPI_Wtime();
while(((MPI_Wtime()-tm)*1000)<delay);
printf("Root: Sending\n");
}
for(iter=0;iter<benchmarkSize;iter++)
{
MPI_Recv(message,STR_LEN,MPI_CHAR,
(numProc-1),RETURN_DATA_TAG,MPI_COMM_WORLD,&status);
//We should not wait for the messages to be received but wait for certain amount of time
//Extract the fields in the message
if(((prevRcvdSeqNum+1)!=atoi(seqNum))&&(prevRcvdSeqNum!=0))
outOfOrderMsgs++;
prevRcvdSeqNum = atoi(seqNum);
printf("Seq Num: %d\n",atoi(seqNum));
rcvdMsgs++;
printf("Root: Receiving\n");
}
MPI_Isend( "STOP", 4, MPI_CHAR,
(root+1)%numProc, SEND_DATA_TAG, MPI_COMM_WORLD,&sendRequest);
MPI_Wait(&sendRequest,&status);
/*This is to ask all other processes to terminate, when the work is done*/
}
Now, I have these questions:
1) Why is it that when I inject some sleep in the other processes(I mean other than root) of
the ring; NO receive is taking place?
2) Even when the buffer size is only one, how is it that root node is able to dispatch messages through MPI_Bsend without an error? for example the case when it needs to send total 10 messages at a rate of 1000 per second and with buffer size of 1. MPI_Bsend is able to dispatch all the messages without any error of "buffer full"; irrespective of the presence of sleep() in other processes of the ring!
Thanks a ton!
I'm trying to ISend() two arrays: arr1,arr2 and an integer n which is the size of arr1,arr2. I understood from this post that sending a struct that contains all three is not an option since n is only known at run time. Obviously, I need n to be received first since otherwise the receiving process wouldn't know how many elements to receive. What's the most efficient way to achieve this without using the blokcing Send() ?
Sending the size of the array is redundant (and inefficient) as MPI provides a way to probe for incoming messages without receiving them, which provides just enough information in order to properly allocate memory. Probing is performed with MPI_PROBE, which looks a lot like MPI_RECV, except that it takes no buffer related arguments. The probe operation returns a status object which can then be queried for the number of elements of a given MPI datatype that can be extracted from the content of the message with MPI_GET_COUNT, therefore explicitly sending the number of elements becomes redundant.
Here is a simple example with two ranks:
if (rank == 0)
{
MPI_Request req;
// Send a message to rank 1
MPI_Isend(arr1, n, MPI_DOUBLE, 1, 0, MPI_COMM_WORLD, &req);
// Do not forget to complete the request!
MPI_Wait(&req, MPI_STATUS_IGNORE);
}
else if (rank == 1)
{
MPI_Status status;
// Wait for a message from rank 0 with tag 0
MPI_Probe(0, 0, MPI_COMM_WORLD, &status);
// Find out the number of elements in the message -> size goes to "n"
MPI_Get_count(&status, MPI_DOUBLE, &n);
// Allocate memory
arr1 = malloc(n*sizeof(double));
// Receive the message. ignore the status
MPI_Recv(arr1, n, MPI_DOUBLE, 0, 0, MPI_COMM_WORLD, MPI_STATUS_IGNORE);
}
MPI_PROBE also accepts the wildcard rank MPI_ANY_SOURCE and the wildcard tag MPI_ANY_TAG. One can then consult the corresponding entry in the status structure in order to find out the actual sender rank and the actual message tag.
Probing for the message size works as every message carries a header, called envelope. The envelope consists of the sender's rank, the receiver's rank, the message tag and the communicator. It also carries information about the total message size. Envelopes are sent as part of the initial handshake between the two communicating processes.
Firstly you need to allocate memory (full memory = n = elements) to arr1 and arr2 with rank 0. i.e. your front end processor.
Divide the array into parts depending on the no. of processors. Determine the element count for each processor.
Send this element count to the other processors from rank 0.
The second send is for the array i.e. arr1 and arr2
In other processors allocate arr1 and arr2 according to the element count received from main processor i.e. rank = 0. After receiving element count, receive the two arrays in the allocated memories.
This is a sample C++ Implementation but C will follow the same logic. Also just interchange Send with Isend.
#include <mpi.h>
#include <iostream>
using namespace std;
int main(int argc, char*argv[])
{
MPI::Init (argc, argv);
int rank = MPI::COMM_WORLD.Get_rank();
int no_of_processors = MPI::COMM_WORLD.Get_size();
MPI::Status status;
double *arr1;
if (rank == 0)
{
// Setting some Random n
int n = 10;
arr1 = new double[n];
for(int i = 0; i < n; i++)
{
arr1[i] = i;
}
int part = n / no_of_processors;
int offset = n % no_of_processors;
// cout << part << "\t" << offset << endl;
for(int i = 1; i < no_of_processors; i++)
{
int start = i*part;
int end = start + part - 1;
if (i == (no_of_processors-1))
{
end += offset;
}
// cout << i << " Start: " << start << " END: " << end;
// Element_Count
int e_count = end - start + 1;
// cout << " e_count: " << e_count << endl;
// Sending
MPI::COMM_WORLD.Send(
&e_count,
1,
MPI::INT,
i,
0
);
// Sending Arr1
MPI::COMM_WORLD.Send(
(arr1+start),
e_count,
MPI::DOUBLE,
i,
1
);
}
}
else
{
// Element Count
int e_count;
// Receiving elements count
MPI::COMM_WORLD.Recv (
&e_count,
1,
MPI::INT,
0,
0,
status
);
arr1 = new double [e_count];
// Receiving FIrst Array
MPI::COMM_WORLD.Recv (
arr1,
e_count,
MPI::DOUBLE,
0,
1,
status
);
for(int i = 0; i < e_count; i++)
{
cout << arr1[i] << endl;
}
}
// if(rank == 0)
delete [] arr1;
MPI::Finalize();
return 0;
}
#Histro The point I want to make is, that Irecv/Isend are some functions themselves manipulated by MPI lib. The question u asked depend completely on your rest of the code about what you do after the Send/Recv. There are 2 cases:
Master and Worker
You send part of the problem (say arrays) to the workers (all other ranks except 0=Master). The worker does some work (on the arrays) then returns back the results to the master. The master then adds up the result, and convey new work to the workers. Now, here you would want the master to wait for all the workers to return their result (modified arrays). So you cannot use Isend and Irecv but a multiple send as used in my code and corresponding recv. If your code is in this direction you wanna use B_cast and MPI_Reduce.
Lazy Master
The master divides the work but doesn't care of the result from his workers. Say you want to program a pattern of different kinds for same data. Like given characteristics of population of some city, you want to calculate the patterns like how many are above 18, how
many have jobs, how much of them work in some company. Now these results don't have anything to do with one another. In this case you don't have to worry about whether the data is received by the workers or not. The master can continue to execute the rest of the code. This is where it is safe to use Isend/Irecv.
My first thought was MPI_Scatter and send-buffer allocation should be used in if(proc_id == 0) clause, because the data should be scattered only once and each process needs only a portion of data in send-buffer, however it didn't work correctly.
It appears that send-buffer allocation and MPI_Scatter must be executed by all processes before the application goes right.
So I wander, what's the philosophy for the existence of MPI_Scatter since all processes have access to the send-buffer.
Any help will be grateful.
Edit:
Code I wrote like this:
if (proc_id == 0) {
int * data = (int *)malloc(size*sizeof(int) * proc_size * recv_size);
for (int i = 0; i < proc_size * recv_size; i++) data[i] = i;
ierr = MPI_Scatter(&(data[0]), recv_size, MPI_INT, &recievedata, recv_size, MPI_INT, 0, MPI_COMM_WORLD);
}
I thought, that's enough for root processes to scatter data, what the other processes need to do is just receiving data. So I put MPI_Scatter, along with send buffer definition & allocation, in the if(proc_id == 0) statement. No compile/runtime error/warning, but the receive buffer of other processes didn't receive it's corresponding part of data.
Your question isn't very clear, and would be a lot easier to understand if you showed some code that you were having trouble with. Here's what I think you're asking -- and I'm only guessing this because this is an error I've seen people new to MPI in C make.
If you have some code like this:
#include <stdio.h>
#include <stdlib.h>
#include <mpi.h>
int main(int argc, char **argv) {
int proc_id, size, ierr;
int *data;
int recievedata;
ierr = MPI_Init(&argc, &argv);
ierr|= MPI_Comm_size(MPI_COMM_WORLD,&size);
ierr|= MPI_Comm_rank(MPI_COMM_WORLD,&proc_id);
if (proc_id == 0) {
data = (int *)malloc(size*sizeof(int));
for (int i=0; i<size; i++) data[i] = i;
}
ierr = MPI_Scatter(&(data[0]), 1, MPI_INT,
&recievedata, 1, MPI_INT, 0, MPI_COMM_WORLD);
printf("Rank %d recieved <%d>\n", proc_id, recievedata);
if (proc_id == 0) free(data);
ierr = MPI_Finalize();
return 0;
}
why doesn't it work, and why do you get a segmentation fault? Of course the other processes don't have access to data; that's the whole point.
The answer is that in the non-root processes, the sendbuf argument (the first argument to MPI_Scatter()) isn't used. So the non-root processes don't need access to data. But you still can't go around dereferencing a pointer that you haven't defined. So you need to make sure all the C code is valid. But data can be NULL or completely undefined on all the other processes; you just have to make sure you're not accidentally dereferencing it. So this works just fine, for instance:
#include <stdio.h>
#include <stdlib.h>
#include <mpi.h>
int main(int argc, char **argv) {
int proc_id, size, ierr;
int *data;
int recievedata;
ierr = MPI_Init(&argc, &argv);
ierr|= MPI_Comm_size(MPI_COMM_WORLD,&size);
ierr|= MPI_Comm_rank(MPI_COMM_WORLD,&proc_id);
if (proc_id == 0) {
data = (int *)malloc(size*sizeof(int));
for (int i=0; i<size; i++) data[i] = i;
} else {
data = NULL;
}
ierr = MPI_Scatter(data, 1, MPI_INT,
&recievedata, 1, MPI_INT, 0, MPI_COMM_WORLD);
printf("Rank %d recieved <%d>\n", proc_id, recievedata);
if (proc_id == 0) free(data);
ierr = MPI_Finalize();
return 0;
}
If you're using "multidimensional arrays" in C, and say scattering a row of a matrix, then you have to jump through an extra hoop or two to make this work, but it's still pretty easy.
Update:
Note that in the above code, all routines called Scatter - both the sender and the recievers. (Actually, the sender is also a receiver).
In the message passing paradigm, both the sender and the receiver have to cooperate to send data. In principle, these tasks could be on different computers, housed perhaps in different buildings -- nothing is shared between them. So there's no way for Task 1 to just "put" data into some part of Task 2's memory. (Note that MPI2 has "one sided messages", but even that requires a significant degree of cordination between sender and reciever, as a window has to be put asside to push data into or pull data out of).
The classic example of this is send/recieve pairs; it's not enough that (say) process 0 sends data to process 3, process 3 also has to recieve data.
The MPI_Scatter function contains both send and recieve logic. The root process (specified here as 0) sends out the data, and all the recievers recieve; everyone participating has to call the routine. Scatter is an example of an MPI Collective Operation, where all tasks in the communicator have to call the same routine. Broadcast, barrier, reduction operations, and gather operations are other examples.
If you have only process 0 call the scatter operation, your program will hang, waiting forever for the other tasks to participate.