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MPI: Message Passing Interface

MPI: Message Passing Interface. Prabhaker Mateti Wright State University. Overview. MPI Hello World! Introduction to programming with MPI MPI library calls. MPI Overview. Similar to PVM Network of Heterogeneous Machines Multiple implementations Open source: MPICH LAM Vendor specific.

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MPI: Message Passing Interface

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  1. MPI: Message Passing Interface Prabhaker Mateti Wright State University

  2. Overview • MPI Hello World! • Introduction to programming with MPI • MPI library calls Mateti, MPI

  3. MPI Overview • Similar to PVM • Network of Heterogeneous Machines • Multiple implementations • Open source: • MPICH • LAM • Vendor specific Mateti, MPI

  4. MPI Features • Rigorously specified standard • Portable source code • Enables third party libraries • Derived data types to minimize overhead • Process topologies for efficiency on MPP • Van fully overlap communication • Extensive group communication Mateti, MPI

  5. MPI 2 • Dynamic Process Management • One-Sided Communication • Extended Collective Operations • External Interfaces • Parallel I/O • Language Bindings (C++ and Fortran-90) • http://www.mpi-forum.org/ Mateti, MPI

  6. MPI Overview • 125+ functions • typical applications need only about 6 Mateti, MPI

  7. #include <mpi.h> main(int argc, char *argv[]) { int myrank; MPI_Init(&argc, &argv); MPI_Comm_rank(MPI_COMM_WORLD, &myrank); if (myrank == 0) manager(); else worker(); MPI_Finalize(); } MPI_Initinitializes the MPI system MPI_Finalizecalled last by all processes MPI_Comm_rank identifies a process by its rank MPI_COMM_WORLD is the group that this process belongs to MPI: manager+workers Mateti, MPI

  8. manager() { MPI_Status status; MPI_Comm_size( MPI_COMM_WORLD, &ntasks); for (i = 1;i < ntasks;++i){ work= nextWork(); MPI_Send(&work, 1, MPI_INT,i,WORKTAG, MPI_COMM_WORLD); } … MPI_Reduce(&sub, &pi, 1, MPI_DOUBLE, MPI_SUM, 0, MPI_COMM_WORLD); } MPI_Comm_size MPI_Send MPI: manager() Mateti, MPI

  9. worker() { MPI_Status status; for (;;) { MPI_Recv(&work, 1, MPI_INT, 0, MPI_ANY_TAG, MPI_COMM_WORLD, &status); result = doWork(); MPI_Send(&result, 1, MPI_DOUBLE, 0, 0, MPI_COMM_WORLD); } } MPI_Recv MPI: worker() Mateti, MPI

  10. #include "mpi.h" int main(int argc, char *argv[]) { MPI_Init(&argc,&argv); MPI_Comm_size(MPI_COMM_WORLD,&np); MPI_Comm_rank(MPI_COMM_WORLD,&myid); n = ...; /* intervals */ MPI_Bcast(&n, 1, MPI_INT, 0, MPI_COMM_WORLD); sub = series_sum(n, np); MPI_Reduce(&sub, &pi, 1, MPI_DOUBLE, MPI_SUM, 0, MPI_COMM_WORLD); if (myid == 0) printf("pi is %.16f\n", pi); MPI_Finalize(); return 0; } MPI computes  Mateti, MPI

  11. Process groups • Group membership is static. • There are no race conditions caused by processes independently entering and leaving a group. • New group formation is collective and group membership information is distributed, not centralized. Mateti, MPI

  12. MPI_Send blocking send MPI_Send( &sendbuffer, /* message buffer */ n, /* n items of */ MPI_type, /* datatype in message */ destination, /* process rank */ WORKTAG, /* user chosen tag */ MPI_COMM /* group */ ); Mateti, MPI

  13. MPI_Recv blocking receive MPI_Recv( &recvbuffer, /* message buffer */ n, /* n data items */ MPI_type, /* of type */ MPI_ANY_SOURCE, /* from any sender */ MPI_ANY_TAG, /* any type of message */ MPI_COMM, /* group */ &status ); Mateti, MPI

  14. Send-receive succeeds … • Sender’s destination is a valid process rank • Receiver specified a valid source process • Communicator is the same for both • Tags match • Message data types match • Receiver’s buffer is large enough Mateti, MPI

  15. Message Order • P sends messages m1 first then m2 to Q • Q will receive m1 before m2 • P sends m1 to Q, then m2 to R • In terms of a global wall clock, conclude nothing re R receiving m2 before/after Q receiving m1. Mateti, MPI

  16. Blocking and Non-blocking • Send, receive can be blocking or not • A blocking send can be coupled with a non-blocking receive, and vice-versa • Non-blocking send can use • Standard mode MPI_Isend • Synchronous mode MPI_Issend • Buffered mode MPI_Ibsend • Ready mode MPI_Irsend Mateti, MPI

  17. MPI_Isend non-blocking MPI_Isend( &buffer, /* message buffer */ n, /* n items of */ MPI_type, /* datatype in message */ destination, /* process rank */ WORKTAG, /* user chosen tag */ MPI_COMM, /* group */ &handle ); Mateti, MPI

  18. MPI_Irecv MPI_Irecv( &result, /* message buffer */ n, /* n data items */ MPI_type, /* of type */ MPI_ANY_SOURCE, /* from any sender */ MPI_ANY_TAG, /* any type of message */ MPI_COMM_WORLD, /* group */ &handle ); Mateti, MPI

  19. MPI_Wait MPI_Wait( handle, &status ); Mateti, MPI

  20. MPI_Wait( handle, &status ); MPI_Test( handle, &status ); MPI_Wait, MPI_Test Mateti, MPI

  21. Collective Communication Mateti, MPI

  22. MPI_Bcast( buffer, count, MPI_Datatype, root, MPI_Comm ); All processes use the same count, data type, root, and communicator. Before the operation, the root’s buffer contains a message. After the operation, all buffers contain the message from the root MPI_Bcast Mateti, MPI

  23. MPI_Scatter( sendbuffer, sendcount, MPI_Datatype, recvbuffer, recvcount, MPI_Datatype, root, MPI_Comm); All processes use the same send and receive counts, data types, root and communicator. Before the operation, the root’s send buffer contains a message of length sendcount * N', where N is the number of processes. After the operation, the message is divided equally and dispersed to all processes (including the root) following rank order. MPI_Scatter Mateti, MPI

  24. MPI_Gather( sendbuffer, sendcount, MPI_Datatype, recvbuffer, recvcount, MPI_Datatype, root, MPI_Comm); This is the “reverse” of MPI_Scatter(). After the operation the root process has in its receive buffer the concatenation of the send buffers of all processes (including its own), with a total message length of recvcount * N, where N is the number of processes. The message is gathered following rank order. MPI_Gather Mateti, MPI

  25. MPI_Reduce( sndbuf, rcvbuf, count, MPI_Datatype datatype, MPI_Op, root, MPI_Comm); After the operation, the root process has in its receive buffer the result of the pair-wise reduction of the send buffers of all processes, including its own. MPI_Reduce Mateti, MPI

  26. MPI_MAX MPI_MIN MPI_SUM MPI_PROD MPI_LAND MPI_BAND MPI_LOR MPI_BOR MPI_LXOR MPI_BXOR MPI_MAXLOC MPI_MINLOC L logical B bit-wise Predefined Reduction Ops Mateti, MPI

  27. User Defined Reduction Ops void myOperator ( void * invector, void * inoutvector, int * length, MPI_Datatype * datatype) { … } Mateti, MPI

  28. Ten Reasons to Prefer MPI over PVM • MPI has more than one free, and quality implementations. • MPI can efficiently program MPP and clusters. • MPI is rigorously specified. • MPI efficiently manages message buffers. • MPI has full asynchronous communication. • MPI groups are solid, efficient, and deterministic. • MPI defines a 3rd party profiling mechanism. • MPI synchronization protects 3rd party software. • MPI is portable. • MPI is a standard. Mateti, MPI

  29. Summary • Introduction to MPI • Reinforced Manager-Workers paradigm • Send, receive: blocked, non-blocked • Process groups Mateti, MPI

  30. MPI resources • Open source implementations • MPICH • LAM • Books • Using MPIWilliam Gropp, Ewing Lusk, Anthony Skjellum • Using MPI-2William Gropp, Ewing Lusk, Rajeev Thakur • On-line tutorials • www.tc.cornell.edu/Edu/Tutor/MPI/ Mateti, MPI

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