How to Effectively Send and Receive 2D Arrays in MPI?

Sending and Receiving 2D Arrays using MPI

Problem:

You have a large 2D matrix that requires processing across multiple nodes using MPI. The only communication between nodes involves sharing edge values after each time step.

Approach:

Your proposed approach, outlined in the question, is suitable for this problem. The following code segments illustrate the suggested implementation:

// (assume A is a 2D array)
if (myrank == 0) {
  for (i = 0; i < x; i++) {
    for (j = 0; j < y; j++) {
      // Perform C++ code implementation
      MPI_Send(&A[i][0], 3, MPI_INT, 1, 0, MPI_COMM_WORLD);
      MPI_Recv(&B[0][0], 3, MPI_INT, 1, 0, MPI_COMM_WORLD, &status);
      MPI_Barrier(MPI_COMM_WORLD);
    }
  }
}

if (myrank == 1) {
  for (i = x; i < xx; i++) {
    for (j = 0; j < y; j++) {
      // Perform C++ code implementation
      MPI_Send(&B[i][0], 3, MPI_INT, 0, 0, MPI_COMM_WORLD);
      MPI_Recv(&A[0][0], 3, MPI_INT, 0, 0, MPI_COMM_WORLD, &status);
      MPI_Barrier(MPI_COMM_WORLD);
    }
  }
}

Additional MPI Functions:

• MPI_Allgather: Gathers data from all processors into a buffer accessible to all processors. Useful for collecting a distributed array onto a single processor.
• MPI_Alltoall: Provides a complete exchange of data between all processors.
• MPI_Sendrecv: A convenient function that combines a send and receive operation into a single call.
• MPI_Buffer_attach: Attaches a buffer to a process, without needing to explicitly allocate memory for it.

Considerations:

• Contiguous memory allocation is recommended for 2D arrays to simplify MPI communication.
• MPI_Barrier ensures synchronization between processes, but it can be replaced with non-blocking alternatives like MPI_Wait or MPI_Test.
• Blocking sends and receives are used here, but non-blocking options are available for enhanced efficiency.

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