MPI Part III NPACI Parallel Computing Institute August 19 - 23, 2002

1San DIEGO SUPERCOMPUTER CENTER

NATIONAL PARTNERSHIP FOR ADVANCED COMPUTATIONAL INFRASTRUCTURE

MPI Part III

NPACI Parallel Computing InstituteAugust 19 - 23, 2002

San Diego Supercomputer Center



Point to Point Communications in MPI

• Basic operations of Point to Point (PtoP) communication and issues of deadlock

• Several steps are involved in the PtoP communication

• Sending process– data is copied to the user buffer by the user

– User calls one of the MPI send routines

– System copies the data from the user buffer to the system buffer

– System sends the data from the system buffer to the destination processor



Point to Point Communications in MPI

• Receiving process– User calls one of the MPI receive subroutines– System receives the data from the source process, and

copies it to the system buffer– System copies the data from the system buffer to the

user buffer– User uses the data in the user buffer



sendbuf

Call send routine

Now sendbuf can be reused

Process 0 : User mode

Kernel mode

Copying data from sendbuf to systembuf

Send data from sysbuf to dest

dataProcess 1 : User mode Kernel mode

Call receive routinereceive data from src to systembuf

Copying data from sysbufto recvbuf

sysbuf

sysbuf

recvbuf

Now recvbuf contains valid data



Unidirectional communication

• Blocking send and blocking receive

if (myrank == 0) then call MPI_Send(…)

elseif (myrank == 1) thencall MPI_Recv(….)

endif

• Non-blocking send and blocking receive

if (myrank == 0) thencall MPI_ISend(…)call MPI_Wait(…)

else if (myrank == 1) thencall MPI_Recv(….)

endif



• Blocking send and non-blocking recvif (myrank == 0 ) then

call MPI_Send(…..)elseif (myrank == 1) then

call MPI_Irecv (…)call MPI_Wait(…)

endif

• Non-blocking send and non-blocking recvif (myrank == 0 ) then

call MPI_Isend (…)call MPI_Wait (…)

elseif (myrank == 1) thencall MPI_Irecv (….)call MPI_Wait(..)

endif

Unidirectional communication



Bidirectional communication

• Need to be careful about deadlock when two processes exchange data with each other

• Deadlock can occur due to incorrect order of send and recv or due to limited size of the system buffer

sendbuf

recvbuf

Rank 0 Rank 1

recvbuf

sendbuf



Bidirectional communication• Case 1 : both processes call send first, then recv

if (myrank == 0 ) thencall MPI_Send(….)call MPI_Recv (…)

elseif (myrank == 1) thencall MPI_Send(….)call MPI_Recv(….)

endif

• No deadlock as long as system buffer is larger than send buffer

• Deadlock if system buffer is smaller than send buf

• If you replace MPI_Send with MPI_Isend and MPI_Wait, it is still the same

• Moral : there may be error in coding that only shows up for larger problem size




• Following is free from deadlock

if (myrank == 0 ) thencall MPI_Isend(….)call MPI_Recv (…)call MPI_Wait(…)

elseif (myrank == 1) thencall MPI_Isend(….)call MPI_Recv(….)call MPI_Wait(….)

endif




• Case 2 : both processes call recv first, then send if (myrank == 0 ) then

call MPI_Recv(….)call MPI_Send (…)

elseif (myrank == 1) thencall MPI_Recv(….)call MPI_Send(….)

endif

• The above will always lead to deadlock (even if you replace MPI_Send with MPI_Isend and MPI_Wait)




• The following code can be safely executed

if (myrank == 0 ) thencall MPI_Irecv(….)call MPI_Send (…)call MPI_Wait(…)

elseif (myrank == 1) thencall MPI_Irecv(….)call MPI_Send(….)call MPI_Wait(….)

endif




• Case 3 : one process call send and recv in this order, and the other calls in the opposite order

if (myrank == 0 ) thencall MPI_Send(….)call MPI_Recv(…)

elseif (myrank == 1) thencall MPI_Recv(….)call MPI_Send(….)

endif

• The above is always safe

• You can replace both send and recv on both processor with Isend and Irecv



Scatter and Gather A Ap0

p1

p2

p3

p0

p1

p2

p3

A

A

A

broadcast

scatterA B C D A

B

C

Dgather

A

B

C

D

A B C D

A B C D

A B C D

A B C D

all gather

p0

p1

p2

p3

p0

p1

p2

p3

p0

p1

p2

p3

p0

p1

p2

p3



Scatter Operation using MPI_Scatter

• Similar to Broadcast but sends a section of an array to each processors

A(0) A(1) A(2) . . ………. A(N-1)

P0 P1 P2 . . . Pn-1

Goes to processors:

Data in an array on root node:



MPI_Scatter

• C – int MPI_Scatter(&sendbuf, sendcnts, sendtype, &recvbuf,

recvcnts, recvtype, root, comm );

• Fortran – MPI_Scatter(sendbuf,sendcnts,sendtype,

recvbuf,recvcnts,recvtype,root,comm,ierror)

• Parameters– sendbuf is an array of size (number processors*sendcnts)

– sendcnts number of elements sent to each processor

– recvcnts number of element(s) obtained from the root processor

– recvbuf contains element(s) obtained from the root processor, may be an array



Scatter Operation using MPI_Scatter

• Scatter with Sendcnts = 2

A(0) A(2) A(4) . . . A(2N-2) A(1) A(3) A(5) . . . A(2N-1)

P0 P1 P2 . . . Pn-1

B(0) B(0) B(0) B(0)B(1) B(1) B(1) B(1)

Goes to processors:

Data in an array on root node:



Gather Operation using MPI_Gather

• Used to collect data from all processors to the root, inverse of scatter

• Data is collected into an array on root processor

A(0) A(1) A(2) . . . A(N-1)

P0 P1 P2 . . . Pn-1

A0 A1 A2 . . . An-1

Data from variousProcessors:

Goes to an array on root node:



MPI_Gather

• C – int MPI_Gather(&sendbuf,sendcnts, sendtype, &recvbuf,

recvcnts,recvtype,root, comm );

• Fortran – MPI_Gather(sendbuf,sendcnts,sendtype,

recvbuf,recvcnts,recvtype,root,comm,ierror)

• Parameters– sendcnts number of elements sent from each processor

– sendbuf is an array of size sendcnts

– recvcnts number of elements obtained from each processor

– recvbuf of size recvcnts*number of processors



Code for Scatter and Gather

• A parallel program to scatter data using MPI_Scatter

• Each processor sums the data

• Use MPI_Gather to get the data back to the root processor

• Root processor prints the global data

• See attached Fortran and C code



module mpi!DEC$ NOFREEFORM include "mpif.h“!DEC$ FREEFORM

end module! This program shows how to use MPI_Scatter and MPI_Gather! Each processor gets different data from the root processor! by way of mpi_scatter. The data is summed and then sent back! to the root processor using MPI_Gather. The root processor! then prints the global sum.

module global integer numnodes,myid,mpi_err integer, parameter :: mpi_root=0end modulesubroutine init use mpi use global implicit none! do the mpi init stuff call MPI_INIT( mpi_err ) call MPI_COMM_SIZE( MPI_COMM_WORLD, numnodes, mpi_err ) call MPI_Comm_rank(MPI_COMM_WORLD, myid, mpi_err)



end subroutine initprogram test1 use mpi use global implicit none integer, allocatable :: myray(:),send_ray(:),back_ray(:) integer count integer size,mysize,i,k,j,total call init! each processor will get count elements from the root count=4 allocate(myray(count))! create the data to be sent on the root if(myid == mpi_root)then size=count*numnodes allocate(send_ray(0:size-1)) allocate(back_ray(0:numnodes-1)) do i=0,size-1 send_ray(i)= i enddo endif



call MPI_Scatter( send_ray, count, MPI_INTEGER, & myray, count, MPI_INTEGER, & mpi_root, MPI_COMM_WORLD,mpi_err)! each processor does a local sum total=sum(myray) write(*,*)"myid= ",myid," total= ",total! send the local sums back to the root call MPI_Gather( total, 1, MPI_INTEGER, & back_ray, 1, MPI_INTEGER, & mpi_root, MPI_COMM_WORLD,mpi_err)! the root prints the global sum if(myid == mpi_root)then write(*,*)"results from all processors= ",sum(back_ray) endif call mpi_finalize(mpi_err)

end program



#include <mpi.h>#include <stdio.h>#include <stdlib.h>/*! This program shows how to use MPI_Scatter and MPI_Gather! Each processor gets different data from the root processor! by way of mpi_scatter. The data is summed and then sent back! to the root processor using MPI_Gather. The root processor! then prints the global sum. *//* globals */int numnodes,myid,mpi_err;#define mpi_root 0/* end globals */void init_it(int *argc, char ***argv);void init_it(int *argc, char ***argv) { mpi_err = MPI_Init(argc,argv); mpi_err = MPI_Comm_size( MPI_COMM_WORLD, &numnodes ); mpi_err = MPI_Comm_rank(MPI_COMM_WORLD, &myid); }



int main(int argc,char *argv[]){ int *myray,*send_ray,*back_ray; int count; int size,mysize,i,k,j,total; init_it(&argc,&argv);/* each processor will get count elements from the root */ count=4; myray=(int*)malloc(count*sizeof(int));/* create the data to be sent on the root */ if(myid == mpi_root){ size=count*numnodes; send_ray=(int*)malloc(size*sizeof(int)); back_ray=(int*)malloc(numnodes*sizeof(int)); for(i=0;i<size;i++) send_ray[i]=i; }

/* send different data to each processor */



mpi_err = MPI_Scatter( send_ray, count, MPI_INT, myray, count, MPI_INT, mpi_root, MPI_COMM_WORLD);/* each processor does a local sum */ total=0; for(i=0;i<count;i++) total=total+myray[i]; printf("myid= %d total= %d\n ",myid,total);/* send the local sums back to the root */ mpi_err = MPI_Gather(&total, 1, MPI_INT, back_ray, 1, MPI_INT, mpi_root, MPI_COMM_WORLD);/* the root prints the global sum */ if(myid == mpi_root){ total=0; for(i=0;i<numnodes;i++) total=total+back_ray[i]; printf("results from all processors= %d \n ",total); } mpi_err = MPI_Finalize();}



Output of previous fortran code on 4 procs

ultra:/work/majumdar/examples/mpi % bsub -q hpc -m ultra -I -n 4 ./a.out

Job <48051> is submitted to queue <hpc>.

<<Waiting for dispatch ...>>

<<Starting on ultra>>

myid= 1 total= 22

myid= 2 total= 38

myid= 3 total= 54

myid= 0 total= 6

results from all processors= 120

( 0 through 15 added up = (15) (15 + 1) /2 = 120)



Global Sum with MPI_Reduce2d array spread across processors

A0+A1+A2 B0+B1+B2 C0+C1+C2NODE 0NODE 1NODE 2

X(0) X(1) X(2)

A0 B0 C0A1 B1 C1A2 B2 C2

NODE 0NODE 1NODE 2

X(0) X(1) X(2)



All Gather and All Reduce

• Gather and Reduce come in an "ALL" variation

• Results are returned to all processors

• The root parameter is missing from the call

• Similar to a gather or reduce followed by a broadcast



Global Sum with MPI_AllReduce 2d array spread across processors

A0 B0 C0A1 B1 C1A2 B2 C2

X(0) X(1) X(2)

NODE 0 NODE 1 NODE 2

A0+A1+A2 B0+B1+B2 C0+C1+C2A0+A1+A2 B0+B1+B2 C0+C1+C2A0+A1+A2 B0+B1+B2 C0+C1+C2

X(0) X(1) X(2)

NODE 0 NODE 1 NODE 2



All to All communication with MPI_Alltoall

• Each processor sends and receives data to/from all others

• C – int MPI_Alltoall(&sendbuf,sendcnts, sendtype, &recvbuf,

recvcnts, recvtype, MPI_Comm);

• Fortran – call MPI_Alltoall(sendbuf,sendcnts,sendtype,

recvbuf,recvcnts,recvtype,comm,ierror)



b0 b1 b2 b3c0 c1 c2 c3d0 d1 d2 d3

a0 a1 a2 a3a1 b1 c1 d1a2 b2 c2 d2a3 b3 c3 d3

a0 b0 c0 d0All to All



All to All with MPI_Alltoall

• Parameters– sendcnts # of elements sent to each processor

– sendbuf is an array of size sendcnts

– recvcnts # of elements obtained from each processor

– recvbuf of size recvcnts

• Note that both send buffer and receive buffer must be an array of size of the number of processors

• See attached Fortran and C codes



module mpi!DEC$ NOFREEFORM include "mpif.h“!DEC$ FREEFORM end module! This program shows how to use MPI_Alltoall. Each processor! send/rec a different random number to/from other processors. module global integer numnodes,myid,mpi_err integer, parameter :: mpi_root=0end modulesubroutine init use mpi use global implicit none! do the mpi init stuff call MPI_INIT( mpi_err ) call MPI_COMM_SIZE( MPI_COMM_WORLD, numnodes, mpi_err ) call MPI_Comm_rank(MPI_COMM_WORLD, myid, mpi_err)end subroutine init



program test1 use mpi use global implicit none integer, allocatable :: scounts(:),rcounts(:) integer ssize,rsize,i,k,j real z call init ! counts and displacement arrays allocate(scounts(0:numnodes-1)) allocate(rcounts(0:numnodes-1)) call seed_random! find data to send do i=0,numnodes-1 call random_number(z) scounts(i)=nint(10.0*z)+1 Enddo write(*,*)"myid= ",myid," scounts= ",scounts



! send the data call MPI_alltoall( scounts,1,MPI_INTEGER, & rcounts,1,MPI_INTEGER, MPI_COMM_WORLD,mpi_err) write(*,*)"myid= ",myid," rcounts= ",rcounts call mpi_finalize(mpi_err)end program subroutine seed_random use global implicit none integer the_size,j integer, allocatable :: seed(:) real z call random_seed(size=the_size) ! how big is the intrisic seed? allocate(seed(the_size)) ! allocate space for seed do j=1,the_size ! create the seed seed(j)=abs(myid*10)+(j*myid*myid)+100 ! abs is generic enddo call random_seed(put=seed) ! assign the seed deallocate(seed)end subroutine



#include <mpi.h>#include <stdio.h>|#include <stdlib.h>/*! This program shows how to use MPI_Alltoall. Each processor! send/rec a different random number to/from other processors. *//* globals */int numnodes,myid,mpi_err;#define mpi_root 0/* end module */void init_it(int *argc, char ***argv);void seed_random(int id);void random_number(float *z);void init_it(int *argc, char ***argv) { mpi_err = MPI_Init(argc,argv); mpi_err = MPI_Comm_size( MPI_COMM_WORLD, &numnodes ); mpi_err = MPI_Comm_rank(MPI_COMM_WORLD, &myid);}



int main(int argc,char *argv[]){ int *sray,*rray; int *scounts,*rcounts; int ssize,rsize,i,k,j; float z; init_it(&argc,&argv); scounts=(int*)malloc(sizeof(int)*numnodes); rcounts=(int*)malloc(sizeof(int)*numnodes); /*! seed the random number generator with a! different number on each processor*/ seed_random(myid);/* find data to send */ for(i=0;i<numnodes;i++){ random_number(&z); scounts[i]=(int)(10.0*z)+1; } printf("myid= %d scounts=",myid); for(i=0;i<numnodes;i++)

printf("%d ",scounts[i]);

printf("\n");



/* send the data */ mpi_err = MPI_Alltoall( scounts,1,MPI_INT, rcounts,1,MPI_INT, MPI_COMM_WORLD); printf("myid= %d rcounts=",myid); for(i=0;i<numnodes;i++) printf("%d ",rcounts[i]); printf("\n"); mpi_err = MPI_Finalize();}

void seed_random(int id){ srand((unsigned int)id);}

void random_number(float *z){ int i; i=rand(); *z=(float)i/32767;

}



Output of previous fortran code on 4 procs

ultra:/work/majumdar/examples/mpi % bsub -q hpc -m ultra -I -n 4 a.outJob <48059> is submitted to queue <hpc>.<<Waiting for dispatch ...>><<Starting on ultra>> myid= 1 scounts= 5 2 4 4 myid= 1 rcounts= 1 2 2 3 myid= 2 scounts= 9 2 2 6 myid= 2 rcounts= 7 4 2 11 myid= 3 scounts= 3 3 11 8 myid= 3 rcounts= 2 4 6 8 myid= 0 scounts= 11 1 7 2 myid= 0 rcounts= 11 5 9 3

--------------------------------------------11 1 7 2 11 5 9 35 2 4 4 1 2 2 39 2 2 6 7 4 2 113 3 11 8 2 4 6 8



The variable or “V” operators

• A collection of very powerful but difficult to setup global communication routines

• MPI_Gatherv: Gather different amounts of data from each processor to the root processor

• MPI_Alltoallv: Send and receive different amounts of data form all processors

• MPI_Allgatherv: Gather different amounts of data from each processor and send all data to each

• MPI_Scatterv: Send different amounts of data to each processor from the root processor

• We discuss MPI_Gatherv and MPI_Alltoallv



MPI_Gatherv• C

– int MPI_Gatherv (&sendbuf, sendcnts, sendtype, &recvbuf, &recvcnts, &rdispls,recvtype, comm);

• Fortran– MPI_Gatherv (sendbuf, sendcnts, sendtype, recvbuf,

recvcnts, rdispls, recvtype, comm, ierror)

• Parameters:– Recvcnts is now an array

– Rdispls is a displacement

· See attached codes



MPI GatherV

rank 0 = root rank 1 rank 2

1 2 3

sendbuf 2 3

sendbuf 3

sendbuf

Recvcounts(0) 1 0 = displs(0)


2 2


3 4

3 5

Recvbuf



MPI Gatherv codeSample program:

include ‘mpif.h’integer isend(3), irecv(3)integer ircnt(0:2), idisp(0:2)data icrnt/1,2,3/ idisp/0,1,3/call mpi_init(ierr)call mpi_comm_size(MPI_COMM_WORLD, nprocs,ierr)call mpi_comm_rank(MPI_COMM_WORLD,myrank,ierr)do I = 1,myrank+1

isend(I) = myrank+1enddoiscnt = myrank + 1call MPI_GATHERV(isend,iscnt,MPI_INTEGER,irecv,ircnt,idisp,MPI_INTEGER

& 0,MPI_COMM_WORLD, ierr)if (myrank .eq. 0) then

print *, ‘irecv =‘, irecvendifcall MPI_FINALIZE(ierr)end

Sample execution:% bsub –q hpc –m ultra –I –n 3 ./a.out% 0: irecv = 1 2 2 3 3 3



MPI_Alltoallv

• Send and receive different amounts of data form all processors

• C– int MPI_Alltoallv (&sendbuf, &sendcnts, &sdispls,

sendtype, &recvbuf, &recvcnts, &rdispls, recvtype, comm );

• Fortran– Call MPI_Alltoallv(sendbuf, sendcnts, sdispls,

sendtype, recvbuf, recvcnts, rdispls,recvtype, comm,ierror);

• See attached code



MPI ALLTOALLV

rank0 rank1 rank2

Sendcounts(0) 1 4 7 0=sdispls(0)


2 5 8 2


3 6 9 4

3 6 9 5

sendbuf sendbuf

Recvcounts(0) 1 2 3 0=rdispls(0)

Recvcounts(1) 4 2 3 1

Recvcounts(3) 7 5 3 2

recvbuf 5 6 3=rdispls(1)

8 6 4

8 6 5

recvbuf 9 6=rdispls(2)

9 7

9 8



MPI ALLTOALLV

proc

ircnt 0 1 2

0 1 2 3

1 1 2 3

2 1 2 3

proc

irdsp 0 1 2

0 0 0 0

1 1 2 3

2 2 4 6



MPI ALLTOALLVProgram alltoallv

include ‘mpif.h’

integer isend(6), irecv(9)

integer iscnt(0:2), isdsp(0:2), ircnt(0), irdsp(0:2)

data isend/1,2,2,3,3,3/

data iscnt/1,2,3/ isdsp/0,1,3/

call MPI_INIT(ierr)

call MPI_COMM_SIZE(MPI_COMM_WORLD,nprocs, ierr)call MPI_COMM_RANK(MIP_COMM_WORLD,myrank, ierr)do i = 1,6

isend(i) = isend(i) + nprocs*myrankenddo

do i = 0, nprocs – 1ircnt(i) = myrank + 1irdsp(i) = i* (myrank + 1)

enddo

print*, ‘isend=‘, isendcall MP_FLUSH(1)call MPI_ALLTOALLV(isend,iscnt,isdsp,MPI_INTEGER,irecv, ircnt, irdsp,MPI_INTEGER,

MPI_COMM_WORLD, ierr)print*, ‘irecv=‘,irecvcall MPI_FINALIZE(ierr)end



MPI ALLTOALLV

Sample execution of mpialltoallv program:

% bsub –q hpc –m ultra –I –n 3

% 0: isend = 1 2 2 3 3 3

1: isend = 4 5 5 6 6 6

2: isend = 7 8 8 9 9 9

0: irecv = 1 4 7 0 0 0 0 0 0

1: irecv = 2 2 5 5 8 8 0 0 0

2: irecv = 3 3 3 6 6 6 9 9 9



Derived types

• C and Fortran 90 have the ability to define arbitrary data types that encapsulate reals, integers, and characters.

• MPI allows you to define message data types corresponding to your data types

• Can use these data types just as default types



Derived types, Three main classifications:

• Contiguous Vectors: enable you to send contiguous blocks of the same type of data lumped together

• Noncontiguous Vectors: enable you to send noncontiguous blocks of the same type of data lumped together

• Abstract types: enable you to (carefully) send C or Fortran 90 structures, don't send pointers



Derived types, how to use them

• Three step process– Define the type using

• MPI_TYPE_CONTIGUOUS for contiguous vectors

• MPI_TYPE_VECTOR for noncontiguous vectors

• MPI_TYPE_STRUCT for structures

– Commit the type using• MPI_TYPE_COMMIT

– Use in normal communication calls• MPI_Send(buffer, count, MY_TYPE,

destination,tag, MPI_COMM_WORLD, ierr)



MPI_TYPE_CONTIGUOUS• Defines a new data type of length count elements

from your old data type• C

– MPI_TYPE_CONTIGUOUS(int count, old_type, &new_type)

• Fortran– Call MPI_TYPE_CONTIGUOUS(count, old_type,

new_type, ierror)

• Parameters– Old_type: your base type

– New_type: a type count elements of Old_type

• See attached codes



MPI_TYPE_CONTIGUOUSSample program:

program type_contiguousinclude ‘mpif.h’integer ibuf(20)call MPI_INIT(ierr)call MPI_COMM_SIZE(MPI_COMM_WORLD,nprocs,ierr)call MPI_COMM_RANK(MPI_COMM_WORLD,myrank,ierr)if (myrank .eq. 0) then do i = 1,20

ibuf(i) = I enddoendifcall MPI_TYPE_CONTIGUOUS(3,MPI_INTEGER,inewtype, ierr)call MPI_TYPE_COMMIT(inewtype, ierr)call MPI_BCAST(ibuf,3,inewtype,0,MPI_COMM_WORLD, ierr)print*, ‘ibuf=‘,ibufcall MPI_FINALIZE(ierr)end

Sample execution:% bsub –q hpc –m ultra –I in 2 a.out% 0 : ibuf =1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 1: ibuf = 1 2 3 4 5 6 7 8 9 0 0 0 0 0 0 0 0 0 0 0



MPI_TYPE_VECTOR

• Defines a datatype which consists of count blocks each of length blocklength and stride displacement between blocks

• C– MPI_TYPE_VECTOR(count, blocklength, stride,

old_type, *new_type)

• Fortran– Call MPI_TYPE_VECTOR(count, blocklength, stride,

old_type, new_type, ierror)

• See attached codes



module mpi

!DEC$ NOFREEFORM

include "mpif.h"

!DEC$ FREEFORM

end module

!Shows how to use MPI_Type_vector to send noncontiguous blocks of data

!and MPI_Get_count and MPI_Get_elements to see the number of elements sent

program do_vect

use mpi

! include "mpif.h"

integer , parameter :: size=24

integer myid, ierr,numprocs

real*8 svect(0:size),rvect(0:size)

integer i,bonk1,bonk2,numx,stride,extent

integer MY_TYPE

integer status(MPI_STATUS_SIZE)

call MPI_INIT( ierr )

call MPI_COMM_RANK( MPI_COMM_WORLD, myid, ierr )

call MPI_COMM_SIZE( MPI_COMM_WORLD, numprocs, ierr )

stride=5

numx=(size+1)/stride



extent = 1

if(myid == 1)write(*,*)"numx=",numx," extent=",extent," stride=",stride

call MPI_Type_vector(numx,extent,stride,MPI_DOUBLE_PRECISION,MY_TYPE,ier

r)

call MPI_Type_commit(MY_TYPE, ierr )

if(myid == 0)then

do i=0,size

svect(i)=i

enddo

call MPI_Send(svect,1,MY_TYPE,1,100,MPI_COMM_WORLD,ierr)

endif

if(myid == 1)then

do i=0,size

rvect(i)=-1

enddo

call MPI_Recv(rvect,1,MY_TYPE,0,100,MPI_COMM_WORLD,status,ierr)

endif

if(myid == 1)then

call MPI_Get_count(status,MY_TYPE,bonk1, ierr )

call MPI_Get_elements(status,MPI_DOUBLE_PRECISION,bonk2,ierr)



write(*,*)"got ", bonk1," elements of type MY_TYPE"

write(*,*)"which contained ", bonk2," elements of type MPI_DOUBLE_PREC

ISION"

do i=0,size

if(rvect(i) /= -1)write(*,'(i2,f4.0)')i,rvect(i)

enddo

endif

call MPI_Finalize(ierr )

end program

! output

! numx= 5 extent= 1 stride= 5

! got 1 elements of type MY_TYPE

! which contained 5 elements of type MPI_DOUBLE_PRECISION

! 0 0.

! 5 5.

! 10 10.

! 15 15.

! 20 20.



MPI_TYPE_STRUCT

• Defines a MPI datatype which maps to a user defined derived datatype

• C– int MPI_TYPE_STRUCT(count, &array_of_blocklengths,

&array_of_displacement, &array_of_types, &newtype);

• Fortran– Call MPI_TYPE_STRUCT(count, array_of_blocklengths,

array_of_displacement, array_of_types, newtype,ierror)



MPI_TYPE_STRUCT

• Parameters: – [IN count] # of old types in the new type (integer)

– [IN array_of_blocklengths] how many of each type in new structure (integer)

– [IN array_of_types] types in new structure (integer)

– [IN array_of_displacement] offset in bytes for the beginning of each group of types (integer)

– [OUT newtype] new datatype (handle) – Call MPI_TYPE_STRUCT(count, array_of_blocklengths,

array_of_displacement,array_of_types, newtype,ierror)



Derived Data type Example

Consider the data type or structure consisting of 3 MPI_DOUBLE_PRECISION 10 MPI_INTEGER 2 MPI_LOGICAL Creating the MPI data structure matching this C/Fortran structure is a three step process Fill the descriptor arrays: B - blocklengths T - types D - displacements Call MPI_TYPE_STRUCT to create the MPI data structure Commit the new data type using MPI_TYPE_COMMIT



Derived Data type Example

• Consider the data type or structure consisting of– 3 MPI_DOUBLE_PRECISION

– 10 MPI_INTEGER

– 2 MPI_LOGICAL

• To create the MPI data structure matching this C/Fortran structure– Fill the descriptor arrays:

• B - blocklengths

• T - types

• D - displacements

– Call MPI_TYPE_STRUCT



Derived Data type Example (continued)! t contains the types that! make up the structure

t(1)=MPI_DOUBLE_PRECISION t(2)=MPI_INTEGER t(3)=MPI_LOGICAL! b contains the number of each type

b(1)=3;b(2)=10;b(3)=2! d contains the byte offset of! the start of each type

d(1)=0;d(2)=24;d(3)=64 call MPI_TYPE_STRUCT(3,b,d,t, MPI_CHARLES,mpi_err)

MPI_CHARLES is our new data type



MPI_Type_commit

• Before we use the new data type we call MPI_Type_commit

• C– MPI_Type_commit(MPI_CHARLES)

• Fortran– Call MPI_Type_commit(MPI_CHARLES,ierr)



Communicators

• A communicator is a parameter in all MPI message passing routines

• A communicator is a collection of processors that can engage in communication

• MPI_COMM_WORLD is the default communicator that consists of all processors

• MPI allows you to create subsets of communicators



Why Communicators?

• Isolate communication to a small number of processors

• Useful for creating libraries

• Different processors can work on different parts of the problem

• Useful for communicating with "nearest neighbors"



MPI_Comm_split

• Provides a short cut method to create a collection of communicators

• All processors with the "same color" will be in the same communicator

• Index gives rank in new communicator

• Fortran– call MPI_COMM_SPLIT(OLD_COMM, color, index,

NEW_COMM, mpi_err)

• C– MPI_Comm_split(OLD_COMM, color, index, &NEW_COMM)



MPI_Comm_split• Split odd and even processors into 2 communicators

Program comm_splitinclude "mpif.h"Integer color,zero_onecall MPI_INIT( mpi_err )call MPI_COMM_SIZE( MPI_COMM_WORLD, numnodes, mpi_err )call MPI_COMM_RANK( MPI_COMM_WORLD, myid, mpi_err )color=mod(myid,2) !color is either 1 or 0call MPI_COMM_SPLIT(MPI_COMM_WORLD,color,myid,NEW_COMM,mpi_err)call MPI_COMM_RANK( NEW_COMM, new_id, mpi_err )call MPI_COMM_SIZE( NEW_COMM, new_nodes, mpi_err )Zero_one = -1If(new_id==0)Zero_one = colorCall MPI_Bcast(Zero_one,1,MPI_INTEGER,0, NEW_COMM,mpi_err)If(zero_one==0)write(*,*)"part of even processor communicator"If(zero_one==1)write(*,*)"part of odd processor communicator"Write(*,*)"old_id=", myid, "new_id=", new_idCall MPI_FINALIZE(mpi_error)End program



MPI_Comm_split

• Split odd and even processors into 2 communicators0: part of even processor communicator0: old_id= 0 new_id= 0

2: part of even processor communicator2: old_id= 2 new_id= 1

1: part of odd processor communicator1: old_id= 1 new_id= 0

3: part of odd processor communicator3: old_id= 3 new_id= 1



#include "mpi.h"#include <math.h>int main(argc,argv)int argc;char *argv[];{ int myid, numprocs; int color,Zero_one,new_id,new_nodes; MPI_Comm NEW_COMM; MPI_Init(&argc,&argv); MPI_Comm_size(MPI_COMM_WORLD,&numprocs); MPI_Comm_rank(MPI_COMM_WORLD,&myid); color=myid % 2; MPI_Comm_split(MPI_COMM_WORLD,color,myid,&NEW_COMM); MPI_Comm_rank( NEW_COMM, &new_id); MPI_Comm_size( NEW_COMM, &new_nodes); Zero_one = -1; if(new_id==0)Zero_one = color; MPI_Bcast(&Zero_one,1,MPI_INT,0, NEW_COMM); if(Zero_one==0)printf("part of even processor communicator \n"); if(Zero_one==1)printf("part of odd processor communicator \n"); printf("old_id= %d new_id= %d\n", myid, new_id); MPI_Finalize();}

Documents

MPI Part III NPACI Parallel Computing Institute August 19 - 23, 2002