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reduce

The syntax for the reduction methods is

comm.Reduce(sendbuf, recvbuf, op=MPI.SUM, root=0)
comm.Allreduce(sendbuf, recvbuf, op=MPI.SUM)
comm.reduce(sendobj=None, recvobj=None, op=MPI.SUM, root=0)
comm.allreduce(sendobj=None, recvobj=None, op=MPI.SUM)


The followimg example shows the use of Reduce and Allreduce. sendbuf and recvbuf must be buffer like data objects with the same number of elements of the same type in all tasks. The reduction operation is performed elementwise using the corresponding elements in sendbuf in all tasks. The result is stored into the corresponding element of recvbuf in the root task by Reduce and in all tasks by Allreduce. If the op paramter is omitted, the default operation MPI.SUM is used.

reduce_array.py:

a_size = 3
recvdata = numpy.zeros(a_size,dtype=numpy.int)
senddata = (rank+1)*numpy.arange(a_size,dtype=numpy.int)
comm.Reduce(senddata,recvdata,root=0,op=MPI.PROD)
print 'on task',rank,'after Reduce:    data = ',recvdata

comm.Allreduce(senddata,recvdata)
print 'on task',rank,'after Allreduce:    data = ',recvdata


The use of the lower case methods reduce and allreduce operating on generic python data objects is limited, because the reduction operations are undefined for most of the data objects (like lists, tuples etc.).

reduce_scatter

The reduce_scatter functions operate elementwise on size sections of the buffer like data objects sendbuf. The sections must have the equal number of elements in all tasks. The result of the reductions in section i is copied to recvbuf in task i, which must have an appropriate length. The syntax for the reduction methods is

comm.Reduce_scatter_block(sendbuf, recvbuf, op=MPI.SUM)
comm.Reduce_scatter(sendbuf, recvbuf, recvcounts=None, op=MPI.SUM)


In Reduce_scatter_block the number of elements in all sections must be equal and the number of elements in sendbuf must be size times that number. An example code is the following

reduce_scatter_block:

a_size = 3
recvdata = numpy.zeros(a_size,dtype=numpy.int)
senddata = (rank+1)*numpy.arange(size*a_size,dtype=numpy.int)
print 'on task',rank,'senddata  = ',senddata
comm.Reduce_scatter_block(senddata,recvdata,op=MPI.SUM)
print 'on task',rank,'recvdata = ',recvdata


In Reduce_scatter the number of elements in the sections can be different. They must be given in the integer tuple recvcounts. The number of elements in sendbuf must be sum of the numbers of elements in the sections. On task i recvbuf must have the length of section i of sendbuf. The following code gives an example for this.

reduce_scatter:

recv_size = range(1,size+1)
recvdata = numpy.zeros(recv_size[rank],dtype=numpy.int)
send_size = 0
for i in  range(0,size):
   send_size =send_size + recv_size[i]
senddata = (rank+1)*numpy.arange(send_size,dtype=numpy.int)
print 'on task',rank,'senddata  = ',senddata
comm.Reduce_scatter(senddata,recvdata,recv_size,op=MPI.SUM)
print 'on task',rank,'recvdata = ',recvdata


Reduction with MINLOC and MAXLOC

The reduction operations MINLOC and MAXLOC differ from all others: they return two results, the minimum resp. maximum of the values in the different tasks and the rank of a task, which holds the extreme value. mpi4py provides the two operations only for the lower case reduce and allreduce mehods for comparing a single numerical data object in every task. An example is given in

reduce_minloc.py:

inp = numpy.random.rand(size)
senddata = inp[rank]
recvdata=comm.reduce(senddata,None,root=0,op=MPI.MINLOC)
print 'on task',rank,'reduce:    ',senddata,recvdata 

recvdata=comm.allreduce(senddata,None,op=MPI.MINLOC)
print 'on task',rank,'allreduce: ',senddata,recvdata


Code Examples

The python codes for all examples described in this tutorial are available from http://wwwuser.gwdg.de/~ohaan/mpi4py_examples/

Scientific Computing