More on MPI Nonblocking point-to-point routines Deadlock Collective communication

Non-blocking send/recv Most hardware has a communication co-processor: communication can happen at the same time with computation. Proc 0 proc 1 … MPI_Send_start MPI_Recv_start Comp … Comp …. MPI_Send_wait MPI_Recv_wait No comm/comp overlaps Proc 0 proc 1 … MPI_Send MPI_Recv Comp … Comp …. No comm/comp overlaps

Non-blocking send/recv routines Non-blocking primitives provide the basic mechanisms for overlapping communication with computation. Non-blocking operations return (immediately) “request handles” that can be tested and waited on. MPI_Isend(start, count, datatype, dest, tag, comm, request) MPI_Irecv(start, count, datatype, dest, tag, comm, request) MPI_Wait(&request, &status)

One can also test without waiting: MPI_Test(&request, &flag, status) MPI allows multiple outstanding non-blocking operations. MPI_Waitall(count, array_of_requests, array_of_statuses) MPI_Waitany(count, array_of_requests, &index, &status)

Deadlocks Send a large message from process 0 to process 1 If there is insufficient storage at the destination, the send must wait for memory space What happens with this code? Process 0 Send(1) Recv(1) Process 1 Send(0) Recv(0) This is called “unsafe” because it depends on the availability of system buffers

Some Solutions to the “unsafe” Problem Order the operations more carefully: Process 0 Send(1) Recv(1) Process 1 Recv(0) Send(0) Supply receive buffer at same time as send: Process 0 Sendrecv(1) Process 1 Sendrecv(0)

More Solutions to the “unsafe” Problem Supply own space as buffer for send (buffer mode send) Process 0 Bsend(1) Recv(1) Process 1 Bsend(0) Recv(0) Use non-blocking operations: Process 0 Isend(1) Irecv(1) Waitall Process 1 Isend(0) Irecv(0)

MPI Collective Communication Send/recv routines are also called point-to-point routines (two parties). Some operations require more than two parties, e.g broadcast, reduce. Such operations are called collective operations, or collective communication operations. Non-blocking collective operations in MPI-3 only Three classes of collective operations: Synchronization data movement collective computation

Synchronization MPI_Barrier( comm ) Blocks until all processes in the group of the communicator comm call it.

Collective Data Movement P0 P1 P2 P3 A Broadcast Scatter B C D A B D C Gather

Collective Computation B D C ABCD AB ABC Reduce Scan

MPI Collective Routines Many Routines: Allgather, Allgatherv, Allreduce, Alltoall, Alltoallv, Bcast, Gather, Gatherv, Reduce, Reduce_scatter, Scan, Scatter, Scatterv All versions deliver results to all participating processes. V versions allow the hunks to have different sizes. Allreduce, Reduce, Reduce_scatter, and Scan take both built-in and user-defined combiner functions.

MPI discussion Ease of use MPI is hard to use!! Programmer takes care of the ‘logical’ distribution of the global data structure Programmer takes care of synchronizations and explicit communications None of these are easy. MPI is hard to use!!

MPI discussion Expressiveness Data parallelism Task parallelism There is always a way to do it if one does not care about how hard it is to write the program.

MPI discussion Exposing architecture features Force one to consider locality, this often leads to more efficient program. MPI standard does have some items to expose the architecture feature (e.g. topology). Performance is a strength in MPI programming. Would be nice to have both world of OpenMP and MPI.