How to Efficiently Exchange Edge Values of a 2D Array Using MPI for Distributed Matrix Computation?

Sending and Receiving 2D Array over MPI

Question:

To improve performance, a large 2D matrix computation needs to be split and executed on multiple nodes using MPI. The only inter-node communication required is the exchange of edge values. Describe an appropriate approach and suggest any additional MPI functions to consider.

Answer:

Your proposed approach, using MPI_SEND and MPI_RECEIVE to exchange edge values, is generally correct. However, there are some aspects to consider for efficient implementation:

Continguous Memory Allocation:

For optimal communication performance, allocate the 2D array contiguously in memory. This can be achieved using pointers to an array of arrays.

Avoiding Barriers:

MPI_Send and MPI_Recv are blocking functions, eliminating the need for explicit barriers. However, it is critical to avoid deadlocks by carefully ordering the sends and receives.

Alternative MPI Functions:

• MPI_Sendrecv: Allows for simultaneous send and receive operations.
• MPI_Nonblocking_Send and MPI_Nonblocking_Recv: Non-blocking communication functions that enable asynchronous data transfer.

Sample Code:

The following code snippet provides an example of edge value exchange using MPI_Sendrecv:

int myrank, num_procs;
MPI_Comm_rank(MPI_COMM_WORLD, &myrank);
MPI_Comm_size(MPI_COMM_WORLD, &num_procs);

int neigh = (myrank + 1) % num_procs;

MPI_Sendrecv(&A[x][0], N*M, MPI_INT, neigh, tagA, &B[0][0], N*M, MPI_INT, neigh, tagB, MPI_COMM_WORLD, &status);

By following these guidelines, you can effectively implement communication for your 2D matrix computation using MPI.

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