How Can I Efficiently Send and Receive 2D Arrays Between Nodes Using MPI?

Sending and Receiving 2D Array over MPI

Utilizing MPI for parallel processing offers immense performance advantages, especially for computations involving large matrices. In such scenarios, splitting a matrix across multiple nodes can significantly optimize the process.

Implementing Edge Value Sharing

In the provided scenario, each node must share edge values with its neighbors. The suggested scheme to achieve this using MPI is as follows:

if (myrank == 0) {
  for (i = 0 to x) {
    for (y = 0 to y) {
      C++ CODE IMPLEMENTATION
      MPI_SEND(A[x][0], A[x][1], A[x][2], Destination= 1...)
      MPI_RECEIVE(B[0][0], B[0][1]......Sender = 1...)
    }
  }

if (myrank == 1) {
  for (i = x+1 to xx) {
    for (y = 0 to y) {
      C++ CODE IMPLEMENTATION
      MPI_SEND(B[x][0], B[x][1], B[x][2], Destination= 0...)
      MPI_RECEIVE(A[0][0], A[0][1]......Sender = 1...)
    }
  }
}

Optimizing Array Allocation

To simplify memory management and MPI communication, consider allocating arrays with contiguous elements instead of C's "multidimensional arrays." This can be achieved using functions like:

int **alloc_2d_int(int rows, int cols) {
    int *data = (int *)malloc(rows*cols*sizeof(int));
    int **array= (int **)malloc(rows*sizeof(int*));
    for (int i=0; i<rows; i++)
        array[i] = &amp;(data[cols*i]);

    return array;
}

int **A;
A = alloc_2d_init(N,M);

MPI Send/Receive

Once the arrays are allocated contiguously, sending and receiving entire N x M arrays becomes straightforward:

MPI_Send(&(A[0][0]), N*M, MPI_INT, destination, tag, MPI_COMM_WORLD);

Barriers vs. Blocking Sends/Receives

MPI offers multiple functions for communication, including blocking (e.g., MPI_Send) and non-blocking (e.g., MPI_Isend). For blocking operations, barriers are unnecessary, as communication is inherently synchronized.

Other MPI Functions

In addition to MPI_Send and MPI_Receive, consider using MPI_Sendrecv for more flexible communication or non-blocking operations like MPI_Isend and MPI_Irecv to overlap communication and computation.

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