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MPI Examples

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A.1 “Hello world”

A quick application to test the MPI libraries and the network.

 /*
  Hello World'' Type MPI Test Program
 */
 #include <mpi.h>
 #include <stdio.h>
 #include <string.h>
 #define BUFSIZE 128
 #define TAG 0
 int main(int argc, char *argv[])
 {
   char idstr[32];
   char buff[BUFSIZE];
   int numprocs;
   int myid;
   int i;
   MPI_Status stat;
   /* all MPI programs start with MPI_Init; all 'N' processes exist thereafter */
   MPI_Init(&argc,&argv);
   MPI_Comm_size(MPI_COMM_WORLD,&numprocs); /* find out how big the SPMD world is */
   MPI_Comm_rank(MPI_COMM_WORLD,&myid); /* and this processes' rank is */
   /* At this point, all the programs are running equivalently, the rank is used to
      distinguish the roles of the programs in the SPMD model, with rank 0 often used
      specially... */
   if(myid == 0)
   {
     printf("%d: We have %d processors\n", myid, numprocs);
     for(i=1;i<numprocs;i++)
     {
       sprintf(buff, "Hello %d! ", i);
       MPI_Send(buff, BUFSIZE, MPI_CHAR, i, TAG, MPI_COMM_WORLD);
     }
     for(i=1;i<numprocs;i++)
     {
       MPI_Recv(buff, BUFSIZE, MPI_CHAR, i, TAG, MPI_COMM_WORLD, &stat);
       printf("%d: %s\n", myid, buff);
     }
   }
   else
   {
     /* receive from rank 0: */
     MPI_Recv(buff, BUFSIZE, MPI_CHAR, 0, TAG, MPI_COMM_WORLD, &stat);
     sprintf(idstr, "Processor %d ", myid);
     strcat(buff, idstr);
     strcat(buff, "reporting for duty\n");
     /* send to rank 0: */
     MPI_Send(buff, BUFSIZE, MPI_CHAR, 0, TAG, MPI_COMM_WORLD);
   }
   /* MPI Programs end with MPI Finalize; this is a weak
      synchronization point */
   MPI_Finalize();
   return 0;
 }

A.2 MPI Skeleton

The sample code below contains the complete communications skeleton for a dynamically load balanced head/compute node application. Following the code is a description of some of the functions necessary for writing typical parallel applications.

include <mpi.h>
#define WORKTAG     1
#define DIETAG     2
main(argc, argv)
int argc;
char *argv[];
{
        int         myrank;
        MPI_Init(&argc, &argv);   /* initialize MPI */
        MPI_Comm_rank(
        MPI_COMM_WORLD,   /* always use this */
        &myrank);      /* process rank, 0 thru N-1 */
        if (myrank == 0) {
                head();
        } else {
                computenode();
        }
        MPI_Finalize();       /* cleanup MPI */
}
head()
{
        int     ntasks, rank, work;
        double       result;
        MPI_Status     status;
        MPI_Comm_size(
        MPI_COMM_WORLD,   /* always use this */
        &ntasks);          /* #processes in application */
/*
* Seed the compute nodes.
*/
        for (rank = 1; rank < ntasks; ++rank) {
                work = /* get_next_work_request */;
                MPI_Send(&work,         /* message buffer */
                1,              /* one data item */
                MPI_INT,        /* data item is an integer */
                rank,           /* destination process rank */
                WORKTAG,        /* user chosen message tag */
                MPI_COMM_WORLD);/* always use this */
        }
/*
* Receive a result from any compute node and dispatch a new work
* request work requests have been exhausted.
*/
        work = /* get_next_work_request */;
        while (/* valid new work request */) {
                MPI_Recv(&result,       /* message buffer */
                1,              /* one data item */
                MPI_DOUBLE,     /* of type double real */
                MPI_ANY_SOURCE, /* receive from any sender */
                MPI_ANY_TAG,    /* any type of message */
                MPI_COMM_WORLD, /* always use this */
                &status);       /* received message info */
                MPI_Send(&work, 1, MPI_INT, status.MPI_SOURCE,
                WORKTAG, MPI_COMM_WORLD);
                work = /* get_next_work_request */;
        }
/*
* Receive results for outstanding work requests.
*/
        for (rank = 1; rank < ntasks; ++rank) {
                MPI_Recv(&result, 1, MPI_DOUBLE, MPI_ANY_SOURCE,
                MPI_ANY_TAG, MPI_COMM_WORLD, &status);
        }
/*
* Tell all the compute nodes to exit.
*/
        for (rank = 1; rank < ntasks; ++rank) {
                MPI_Send(0, 0, MPI_INT, rank, DIETAG, MPI_COMM_WORLD);
        }
}
computenode()
{
        double              result;
        int                 work;
        MPI_Status          status;
        for (;;) {
                MPI_Recv(&work, 1, MPI_INT, 0, MPI_ANY_TAG,
                MPI_COMM_WORLD, &status);
/*
* Check the tag of the received message.
*/
                if (status.MPI_TAG == DIETAG) {
                        return;
                }
                result = /* do the work */;
                MPI_Send(&result, 1, MPI_DOUBLE, 0, 0, MPI_COMM_WORLD);
        }
}

Processes are represented by a unique rank (integer) and ranks are numbered 0, 1, 2, …, N-1. MPI_COMM_WORLD means all the processes in the MPI application. It is called a communicator and it provides all information necessary to do message passing. Portable libraries do more with communicators to provide synchronisation protection that most other systems cannot handle.

A.3 MPI Initialization And Finalization

As with other systems, two functions are provided to initialize and clean up an MPI process:

  MPI_Init(&argc, &argv);
  MPI_Finalize( );

A.4 What Is The Current Process? How Many Processes Are There?

Typically, a process in a parallel application needs to know who it is (its rank) and how many other processes exist.

A process finds out its own rank by calling:

MPI_Comm_rank( ):
Int myrank;
MPI_Comm_rank(MPI_COMM_WORLD, &myrank);

The total number of processes is returned by MPI_Comm_size( ):

int nprocs;
MPI_Comm_size(MPI_COMM_WORLD, &nprocs);

A.5 Sending Messages

A message is an array of elements of a given data type. MPI supports all the basic data types and allows a more elaborate application to construct new data types at runtime. A message is sent to a specific process and is marked by a tag (integer value) specified by the user. Tags are used to distinguish between different message types a process might send/receive. In the sample code above, the tag is used to distinguish between work and termination messages.

MPI_Send(buffer, count, datatype, destination, tag, MPI_COMM_WORLD);

A.6 Receiving Messages

A receiving process specifies the tag and the rank of the sending process. MPI_ANY_TAG and MPI_ANY_SOURCE may be used optionally to receive a message of any tag and from any sending process.

MPI_Recv(buffer, maxcount, datatype, source, tag, MPI_COMM_WORLD, &status);

Information about the received message is returned in a status variable. The received message tag is status.MPI_TAG and the rank of the sending process is status.MPI_SOURCE. Another function, not used in the sample code, returns the number of data type elements received. It is used when the number of elements received might be smaller than maxcount.

MPI_Get_count(&status, datatype, &nelements);

A.7 Blocking, Non-Blocking, And Persistent Messages

MPI_Send and MPI_Receive cause the running program to wait for non-local communication from a network. Most communication networks function at least an order of magnitude slower than local computations. When an MPI process has to wait for non-local communication CPU cycles are lost because the operating system has to block the process, then has to wait for communication, and then resume the process.

An optimal efficiency is usually best achieved by overlapping communication and computation. Blocking messaging functions only allow one communication to occur at a time. Non-blocking messaging functions allow the application to initiate multiple communication operations, enabling the MPI implementation to proceed simultaneously. Persistent non-blocking messaging functions allow a communication state to persist, so that the MPI implementation does not waste time on initializing or terminating a communication.

A.7.1 Blocking Messages

In the following example, the communication implementation executes in a sequential fashion causing each process, MPI_Recv, then MPI_Send, to block while waiting for its neighbor:

Example

 while (looping) {
   if (i_have_a_left_neighbor)
     MPI_Recv(inbuf, count, dtype, left, tag, comm, &status);
   if (i_have_a_right_neighbor)
     MPI_Send(outbuf, count, dtype, right, tag, comm);
   do_other_work();
 }

MPI also has the potential to allow both communications to occur simultaneously, as in the following communication implementation example:

A.7.2 Non-Blocking Messages

Example

 while (looping) {
   count = 0;
   if (i_have_a_left_neighbor)
     MPI_Irecv(inbuf, count, dtype, left, tag, comm, &req[count++]);
   if (i_have_a_right_neighbor)
     MPI_Isend(outbuf, count, dtype, right, tag, comm, &req[count++]);
   MPI_Waitall(count, req, &statuses);
   do_other_work();
 }

In the example, MPI_Waitall potentially allows both communications to occur simultaneously. However, the process as show is blocked until both communications are complete.

A.7.3 Persistent, Non-Blocking Messages

A more efficient use of the waiting time means to carry out some other work in the meantime that does not depend on that communication. If the same buffers and communication parameters are to be used in each iteration, then a further optimization is to use the MPI persistent mode. The following code instructs MPI to set up the communications once, and communicate similar messages every time:

Example

  int count = 0;
  if (i_have_a_left_neighbor)
    MPI_Recv_init(inbuf, count, dtype, left, tag, comm, &req[count++]);
  if (i_have_a_right_neighbor)
    MPI_Send_init(outbuf, count, dtype, right, tag, comm, &req[count++]);
  while (looping) {
   MPI_Startall(count, req);
    do_some_work();
    MPI_Waitall(count, req, &statuses);
   do_rest_of_work();
 }

In the example, MPI_Send_init and MPI_Recv_init perform a persistent communication initialization.