MPI continue An example for designing explicit message

MPI (continue) • An example for designing explicit message passing programs • Advanced MPI concepts

An design example (SOR) • What is the task of a programmer of message passing programs? • How to write a shared memory parallel program? – Decide how to decompose the computation into parallel parts. – Create (and destroy) processes to support that decomposition. – Add synchronization to make sure dependences are covered. – Does it work for MPI programs?

SOR example

SOR shared memory program grid proc 1 1 2 3 4 proc 2 temp proc 3 1 2 3 4 proc. N

MPI program complication: memory is distributed grid 2 temp 2 proc 2 3 temp 3 proc 3 Can we still use The same code For sequential Program?

Exact same code does not work: need additional boundary elements grid 2 temp 2 proc 2 3 temp 3 proc 3

Boundary elements result in communications grid proc 2 proc 3

Assume now we have boundaries • Can we use the same code? for( i=from; i<to; i++ ) for( j=0; j<n; j++ ) temp[i][j] = 0. 25*( grid[i-1][j] + grid[i+1][j] + grid[i][j-1] + grid[i][j+1]); • Only if we declare a giant array (for the whole mesh on each process). – If not, we will need to translate the indices.

Index translation for( i=0; i<n/p; i++) for( j=0; j<n; j++ ) temp[i][j] = 0. 25*( grid[i-1][j] + grid[i+1][j] + grid[i][j-1] + grid[i][j+1]); • All variables are local to each process, need the logical mapping!

Task for a message passing programmer • • • Divide up program in parallel parts. Create and destroy processes to do above. Partition and distribute the data. Communicate data at the right time. Perform index translation. Still need to do synchronization? – Sometimes, but many times goes hand in hand with data communication.

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 … MPI_Send Comp … proc 1 MPI_Recv Comp …. No comm/comp overlaps Proc 0 proc 1 … MPI_Send_start Comp … MPI_Send_wait MPI_Recv_start Comp …. MPI_Recv_wait 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 nonblocking operations. MPI_Waitall(count, array_of_requests, array_of_statuses) MPI_Waitany(count, array_of_requests, &index, &status)

Sources of 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 Process 1 Send(1) Recv(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 Process 1 Send(1) Recv(0) Send(0) Supply receive buffer at same time as send: Process 0 Process 1 Sendrecv(1) Sendrecv(0)

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

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 P 0 A P 1 Broadcast P 2 P 3 P 0 ABCD Scatter P 1 P 2 P 3 Gather A A A B C D

Collective Computation P 0 P 1 P 2 P 3 A B C D Reduce Scan ABCD A AB ABCD

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

MPI discussion • Ease of 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 Open. MP and MPI.