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

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

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

4. 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

5. 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/

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

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

8. Mateti, MPI 8 MPI: manager() 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

9. Mateti, MPI 9 MPI: worker() worker() { MPI_Statusstatus; 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

10. Mateti, MPI 10 MPI computes  #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; }

11. 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.

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

13. Mateti, MPI 13 MPI_Recvblocking 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 );

14. 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

15. 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.

16. 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 modeMPI_Isend –Synchronous modeMPI_Issend –Buffered modeMPI_Ibsend –Ready modeMPI_Irsend

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

18. 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 );

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

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

21. Mateti, MPI 21 Collective Communication

22. Mateti, MPI 22 MPI_Bcast 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

23. Mateti, MPI 23 MPI_Scatter 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.

24. Mateti, MPI 24 MPI_Gather 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.

25. Mateti, MPI 25 MPI_Reduce 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.

26. Mateti, MPI 26 Predefined Reduction Ops MPI_MAX MPI_MIN MPI_SUM MPI_PROD MPI_LAND MPI_BAND MPI_LOR MPI_BOR MPI_LXOR MPI_BXOR MPI_MAXLOC MPI_MINLOC Llogical Bbit-wise

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

28. Mateti, MPI 28 Ten Reasons to Prefer MPI over PVM 1. MPI has more than one free, and quality implementations. 2. MPI can efficiently program MPP and clusters. 3. MPI is rigorously specified. 4. MPI efficiently manages message buffers. 5. MPI has full asynchronous communication. 6. MPI groups are solid, efficient, and deterministic. 7. MPI defines a 3rd party profiling mechanism. 8. MPI synchronization protects 3rd party software. 9. MPI is portable. 10. MPI is a standard.

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

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