Message Passing and MPI Collective Operations and Buffering

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# Message Passing and MPI Collective Operations and Buffering - PowerPoint PPT Presentation

## Message Passing and MPI Collective Operations and Buffering

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##### Presentation Transcript

1. Message Passing and MPICollective Operations and Buffering Laxmikant Kale CS 320

2. Example : Jacobi relaxation Pseudocode: A, Anew: NxN 2D-array of (FP) numbers loop (how many times?) for each I = 1, N for each J between 1, N Anew[I,J] = average of 4 neighbors and itself. Swap Anew and A End loop Red and Blue boundaries held at fixed values (say temperature) Discretization: divide the space into a grid of cells. For all cells except those on the boundary: iteratively compute temperature as average of their neighboring cells’

3. How to parallelize? • Decide to decompose data: • What options are there? (e.g. 16 processors) • Vertically • Horizontally • In square chunks • Pros and cons • Identify communication needed • Let us assume we will run for a fixed number of iterations • What data do I need from others? • From whom specifically? • Reverse the question: Who needs my data? • Express this with sends and recvs..

4. Ghost cells: a common apparition • The data I need from neighbors • But that I don’t modify (therefore “don’t own”) • Can be stored in my data structures • So that my inner loops don’t have to know about communication at all.. • They can be written as if they are sequential code.

5. Convergence Test • Notice that all processors must report their convergence • Only if all have converged the program has converged • Send data to one processor (say #0) • If you are running on 1000 processors? • Too much overhead on that one processor (serialization) • Use spanning tree: • Simple one: processor P’s parents are (P-1)/2 • Children: 2P+1 2P+2 • Is that the best spanning tree? • Depends on the machine! • MPI supports a single interface • Imple,ented differently on different machines

6. MPI_Reduce • Reduce data, and use the result on root. MPI_Reduce(data, result, size, MPI_Datatype, MPI_Op, amIroot, communicator) MPI_Allreduce(data, result, size, MPI_Datatype, MPI_Op, amIroot, communicator)

7. Others collective ops • Barriers, Gather, Scatter MPI_Barrier(MPI_Comm) MPI_Gather(sendBuf, size, dataType, recvBuf, rcvSize, recvType, root,comm) MPI_Scatter(…) MPI_AllGather(.. No root..) MPI_AllScatter(. .)

8. Collective calls • Message passing is often, but not always, used for SPMD style of programming: • SPMD: Single process multiple data • All processors execute essentially the same program, and same steps, but not in lockstep • All communication is almost in lockstep • Collective calls: • global reductions (such as max or sum) • syncBroadcast (often just called broadcast): • syncBroadcast(whoAmI, dataSize, dataBuffer); • whoAmI: sender or receiver

9. Other Operations • Collective Operations • Broadcast • Reduction • Scan • All-to-All • Gather/Scatter • Support for Topologies • Buffering issues: optimizing message passing • Data-type support