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Up to date as of January 23, 2010

From Wikibooks, the open-content textbooks collection

< Message-Passing Interface

This page lists brief explanations for the functions used in MPI.


int MPI_Send( void *buf, int count, MPI_Datatype datatype, int dest, 
              int tag, MPI_Comm comm )

This sends the contents of buf to the destination of rank dest while the receiving end calls MPI_Recv.


int MPI_Recv( void *buf, int count, MPI_Datatype datatype, int source, 
              int tag, MPI_Comm comm, MPI_Status *status )

This fills the buf with data comming from to the source of rank source while the sender calls MPI_Send.


int MPI_Bcast ( void *buffer, int count, MPI_Datatype datatype, int root, 
                MPI_Comm comm )

This sends the contents of buffer on root to all other processes. So afterwards the first count elements of buffer is the same across all nodes. [1]

The performance of MPI_Bcast can be between O(N) and O(log2N).[2][3]

Contents

MPI 2.0 Connection commands

int MPI_Open_port(MPI_Info info, char *port_name)

This creates a port to which other processes can connect. The buffer passed as port_name string must be at least MPI_MAX_PORT_NAME long and will contain a unique identifier which other processes will need to know in order to connect.


int MPI_Comm_accept(char *port_name, MPI_Info info, int root, MPI_Comm comm, 
                    MPI_Comm *newcomm)

After calling MPI_Open_port(), this function waits for a connection.


int MPI_Comm_connect(char *port_name, MPI_Info info, int root, MPI_Comm comm, 
                      MPI_Comm *newcomm)

This opens a connection to another process which is waiting on MPI_Comm_accept(). The port_name argument must be the same as the result of the other process's port_name returned from MPI_Open_port.

Reduction

The following functions reduce arrays of data across processors to scalars on one or many processors by applying simple functions such as summation.


int MPI_Scan ( void *sendbuf, void *recvbuf, int count, MPI_Datatype datatype,
               MPI_Op op, MPI_Comm comm )

See [4].


int MPI_Allreduce ( void *sendbuf, void *recvbuf, int count, 
                    MPI_Datatype datatype, MPI_Op op, MPI_Comm comm )

This performs an operation specified by MPI_Op on all nodes to each node's sendbuf. For example, if node 0 had {0, 1, 2} and node 1 had {3, 4, 5} in its sendbuf, respectively, and if both called MPI_Allreduce(sendbuf, foo, 3, MPI_INT, MPI_SUM, world), the contents of the buffer pointed to by foo on both would be

{0+3, 1+4, 2+5} = {3, 5, 7}


Performance for P processes: O(NlogP).[5]


int MPI_Comm_split(MPI_Comm comm, int color, int key, MPI_Comm *newcomm);

MPI_Comm_split() creates a new communicator in each process. The resulting communicators are common to the processes that provided the same color argument. Processes can opt not to get a communicator by providing MPI_UNDEFINED as the color, which will produce MPI_COMM_NULL for that process.

For example, the following code splits MPI_COMM_WORLD into three communicators "colored" 0, 1, and 2.

#include<iostream>
#include<mpi.h>

using namespace std;

int main(int argc, char** argv) {
  MPI_Init(&argc, &argv);
  int rank;
  MPI_Comm_rank(MPI_COMM_WORLD, &rank);
  MPI_Comm comm;
  MPI_Comm_split(MPI_COMM_WORLD, rank % 3, -rank*2, &comm); // The keys need not be positive or contiguous.
  int rank2;
  MPI_Comm_rank(comm, &rank2);
  cout << "My rank was " << rank << " now " << rank2 << " color: " << rank % 3 << "\n";
  MPI_Finalize();
}

Run with eight processes, this output the following (in undefined order):

My rank was 0 now 2 color: 0
My rank was 1 now 2 color: 1
My rank was 2 now 1 color: 2
My rank was 3 now 1 color: 0
My rank was 4 now 1 color: 1
My rank was 5 now 0 color: 2
My rank was 6 now 0 color: 0
My rank was 7 now 0 color: 1

Nonblocking asynchronous communication

The following functions work together to allow nonblocking asynchronous communications among processes.[6] One process sends while another receives. The sender must must check that the operation is complete before erasing the buffer. MPI_Wait() is a blocking wait whereas MPI_Test is nonblocking.

MPI_Isend() and MPI_Irecv() calls need not be in order. That is, process 42 could call MPI_Irecv() three times to begin receiving from processes 37, 38, and 39 which can send at their leisure.


int MPI_Isend(void* buf, int count, MPI_Datatype datatype, int dest, int tag, MPI_Comm comm, MPI_Request *request);

---

int MPI_Irecv(void* buf, int count, MPI_Datatype datatype, int source, int tag, MPI_Comm comm, MPI_Request recvtag, MPI_Comm comm, MPI_Status *status);

int MPI_Wait(MPI_Request *request, MPI_Status *status);

int MPI_Test(MPI_Request  *request, int* flag, MPI_Status* status);


Example code:

#include<iostream>
#include<mpi.h>

using namespace std;

int main(int argc, char** argv) {
  MPI_Init(&argc, &argv);
  int rank;
  MPI_Comm_rank(MPI_COMM_WORLD, &rank);
  MPI_Comm comm;

  int sources[] = {3,4,5};
  int dest = 1;
  int tag =42;
  if (rank == sources[0] || rank == sources[1] || rank == sources[2]) {
    double x[] = { 1*rank, 2*rank, 3*rank};
    MPI_Request r;
    MPI_Isend(x, 3, MPI_DOUBLE, dest, tag, MPI_COMM_WORLD, &r);
    cout << "Process " << rank << " waiting...\n";
    MPI_Status status;
    MPI_Wait(&r, &status);
    cout << "Process " << rank << " sent\n";
  } else if(rank == dest) {
    double x[3][3];
    MPI_Request r[3];
    for (int i = 0; i !=3; ++i) {
      MPI_Irecv(x[i], 3, MPI_DOUBLE, sources[i], tag, MPI_COMM_WORLD, &r[i]);
      cout << "Process " << rank << " waiting for " << sources[i] << " on recv.\
\n";
    }
    for (int i = 0; i !=3; ++i) {
      MPI_Status status;
      MPI_Wait(&r[i], &status);
    cout << "Process " << rank << " got " << x[i][0] << " " << x[i][1] << " " <\
< x[i][2] << ".\n";
    }
  }

  MPI_Finalize();
}

[Other functions]

int MPI_Init(int *argc, char ***argv);
int MPI_Finalize(void);
int MPI_Comm_rank(MPI_Comm comm, int *rank);
int MPI_Comm_size(MPI_Comm comm, int *size);
int MPI_Get_count(MPI_Status *status, MPI_Datatype datatype, int *count);
int MPI_Type_extent(MPI_Datatype datatype, MPI_Aint *extent);
int MPI_Type_struct(int count, int *array_of_blocklengths, MPI_Aint *array_of_displacements, MPI_Datatype *array_of_types, MPI_Datatype *newtype);
int MPI_Scatter(void* sendbuf, int sendcount, MPI_Datatype sendtype, void* recvbuf, int recvcount, MPI_Datatype recvcount, int root, MPI_Comm comm);  Performance potential: As good as log_2 (N) as bad as N. http://www.pdc.kth.se/training/Talks/MPI/Collective.I/less.html#perf_scatter_image
int MPI_Gather(void* sendbuf, int sendcount, MPI_Datatype sendype, void* recvbuf, int recvcount, MPI_Datatype recvtype, int root, MPI_Comm comm); -- O(NP) [7]
  • request);
int MPI_Sendrecv(void* sendbuf, int sendcount, MPI_Datatype datatype, int dest, int sendtag, void* recvbuf, int recvcount, MPI_Datatype recvtype, int source, int recvtag, MPI_Comm comm, MPI_Status *status);
int MPI_Sendrecv_replace(void* buf, int count, MPI_Datatype datatype, int dest, int sendtag, int source, int  int MPI_Request_free(MPI_Request *request);
int MPI_Group_rank(MPI_Group group, int *rank);
int MPI_Group_size(MPI_Group group, int *size);
int MPI_Comm_group(MPI_Comm comm, MPI_Group *group);
int MPI_Group_free(MPI_Group *group);
int MPI_Group_incl(MPI_Group *group, int n, int *ranks, MPI_Group *newgroup);
int MPI_Comm_create(MPI_Comm comm, MPI_Group group, MPI_Comm *newgroup);
int MPI_Wtime(void);
int MPI_Get_processor_name(char *name, int *resultlen);

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