This is an old revision of the document!

Outdated

This Wiki page is outdated, please go to: https://info.gwdg.de/wiki/doku.php?id=wiki:hpc:mpi4py

Global Data Communication

Data communication between all tasks of a communicator can have two directions:

• collecting data coming from all tasks to one or all tasks

For distributing data mpi4py provides functions for

• broadcasting: `Bcast, bcast`
• scattering from one task: `Scatter, Scatterv, scatter`

For collecting data mpi4py provides functions for

• gathering to one task: `Gather, Gatherv, gather`
• gathering to all tasks: `Allgather, Allgatherv, allgather`

In addition, there are functions, which combine collection and distribution of data:

• `Alltoall, Alltoallv, Alltoallw, alltoall`

The syntax of the broadcast method is

```comm.Bcast(buf, int root=0)
buf = comm.bcast(obj=None, int root=0)```

An example for broadcasting an NumPy array is

bcast_array.py:

```data = numpy.empty(5,dtype=numpy.float64)
if rank == 0:
data = numpy.arange(5,dtype=numpy.float64)
comm.Bcast([data,3,MPI.DOUBLE],root=0)
print 'on task',rank,'after recv:    data = ',data```

An example broadcasting a python data object is

bcast.py:

```rootdata = None
if rank == 0:
rootdata = (1,'a','z',3.14)
data = comm.bcast(rootdata,root=0)
print 'on task',rank,'after bcast:    data = ',data```

A difference between the two broadcast functions is, that in `Bcast` the data to be broadcasted stay in place in the root task, whereas in `bcast`, they are copied to the object returned by the call to bcast.

scatter

Whereas a broadcast operation distributes the same object from the root task to all other tasks, a scattering operation sends a different object from root to every task. The syntax of the methods for scattering is

```comm.Scatter(sendbuf, recvbuf, int root=0)
comm.Scatterv(sendbuf, recvbuf, int root=0)
comm.scatter(sendobj=None, recvobj=None, int root=0)```

In the upper case function `Scatter` the sendbuf and recvbuf arguments must be given in terms of a list (as in the point to point function `Send`):

`buf = [data, data_size, data_type]`

where `data` must be a buffer like object of size `data_size` and of type `data_type`. The size and type descriptions can be omitted, if they are implied by the buffer object, as in the case of a NumPy array. Equally sized sections of the data in sendbuf will be sent from root to the other tasks, the i-th section to task i, where they will be stored in recvbuf. The data_size in sendbuf therefore must be a multiple of `size`, where `size` is the number of tasks in the communicator. The data_size in recvbuf must be at least as large as the size of the data section received.

In the vector variant of this function, `Scatterv`, the size and the location of the sections of senddata to be distributed may be freely chosen. The sendbuf in this case must include a description of the layout of the sections in the form

`sendfbuf = [data, counts, displacements, type]`

where `counts` and `displacements` are integer tuples with as many elements as tasks are in the communicator. `counts[i]` designates the size of the i-th segment, `displacements[i]` the number of the element in `data` used as the first element of the i-th section.

The calls of `Scatter` and `Scatterv` are illustrated in the next example

scatter_array,py:

```a_size = 4
recvdata = numpy.empty(a_size,dtype=numpy.float64)
senddata = None
if rank == 0:
senddata = numpy.arange(size*a_size,dtype=numpy.float64)
comm.Scatter(senddata,recvdata,root=0)
print 'on task',rank,'after Scatter:    data = ',recvdata

recvdata = numpy.empty(a_size,dtype=numpy.float64)
counts = None
dspls = None
if rank == 0:
senddata = numpy.arange(100,dtype=numpy.float64)
counts=(1,2,3)
dspls=(4,3,10)
comm.Scatterv([senddata,counts,dspls,MPI.DOUBLE],recvdata,root=0)
print 'on task',rank,'after Scatterv:    data = ',recvdata```

In the `scatter` function the `sendobj` to be scattered from the root task must be a sequence of exactly `size` objects, where `size` is the number of tasks in the communicator. Each element in this sequence can be a data object of any type, and the i-th object of the sequence will be sent from root to the i-th task and will be received in the i-th tasks as the value returned from `scatter`. This is shown in the following example program

scatter.py:

```rootdata = None
if rank == 0:
rootdata = [1,2,3,(4,5)]
data = comm.scatter(rootdata,root=0)
print 'on task',rank,'after bcast:    data = ',data```

gather

The gather operations collects data from all tasks and delivers this collection to the root task or to all tasks. The syntax of the gathering methods is

```comm.Gather(sendbuf, recvbuf, int root=0)
comm.Gatherv(sendbuf, recvbuf, int root=0)
comm.Allgather(sendbuf, recvbuf)
comm.Allgatherv(sendbuf, recvbuf)
comm.gather(sendobj=None, recvobj=None, int root=0)
comm.allgather(sendobj=None, recvobj=None)```

Compared to the scatter methods, the roles of sendbuf and recvbuf are exchanged for `Gather` and `Gatherv`. In `Gather` sendbuf must contain the same number of elements in all tasks and the recvbuf on root must contain `size` times that number of elements, where `size` is the total number of tasks. For `Gatherv` the integer tuples counts and displacements characterize the layout of recvbuf, as described in the scatter section. Here sendbuf must contain exactly `counts[rank]` elements in task `rank`. The following code demonstrates this.

gather_array.py:

```a_size = 4
recvdata = None
senddata = (rank+1)*numpy.arange(a_size,dtype=numpy.float64)
if rank == 0:
recvdata = numpy.arange(size*a_size,dtype=numpy.float64)
comm.Gather(senddata,recvdata,root=0)
print 'on task',rank,'after Gather:    data = ',recvdata

counts=(2,3,4)
dspls=(0,3,8)
if rank == 0:
recvdata = numpy.empty(12,dtype=numpy.float64)
sendbuf = [senddata,counts[rank]]
recvbuf = [recvdata,counts,dspls,MPI.DOUBLE]
comm.Gatherv(sendbuf,recvbuf,root=0)
print 'on task',rank,'after Gatherv:    data = ',recvdata```

The lower case function `gather` communicates generic python data objects analogous to the `scatter` function. An example is

gather.py:

```senddata = ['rank',rank]
rootdata = comm.gather(senddata,root=0)
print 'on task',rank,'after bcast:    data = ',rootdata```

The “all” variants of the gather methods deliver the collected data not only to the root task, but to all tasks in the communicator. These methods therefore lack the `root` parameter.

alltoall

alltoall operations combine gather and scatter. In a communicator with `size` tasks every task has a sendbuf and a recvbuf with exactly `size` data objects. The alltoall operation takes the i-th object from the sendbuf of task j and copies it into the j-th object of the recvbuf of task i. The operation can be thought of as a transpose of the matrix with tasks as columns and data objects as rows. The syntax of the alltoall methods is

```comm.Alltoall(sendbuf, recvbuf)
comm.Alltoallv(sendbuf, recvbuf)
comm.alltoall(sendobj=None, recvobj=None)```

The data objects in sendbuf and recvbuf have types depending on the method. In `Alltoall` the data objects are equally sized consecutive sections of buffer like objects. In `Alltoallv` they are sections of varying sizes and varying displacements of buffer like objects. The layout must be described the in counts und diplacements tuples, which have to be defined for sendbuf and recvbuf in every tasks in a way consistent with the intended transposition in the (task,object) matrix. Finally in the lower case `alltoall` function the data objects can be of any allowed Python type, provided that the data objects in sendbuf conform to those in recvbuf, which they will replace.

An example for `Alltoall` is

alltoall_array.py:

```a_size = 1
senddata = (rank+1)*numpy.arange(size*a_size,dtype=numpy.float64)
recvdata = numpy.empty(size*a_size,dtype=numpy.float64)
comm.Alltoall(senddata,recvdata)```

Next an example for `alltoall`, to be started with two tasks.

alltoall.py:

```data0 = ['rank',rank]
data1 = [1,'rank',rank]
senddata=(data0,data1)
print 'on task',rank,'    senddata = ',senddata
recvdata = comm.alltoall(senddata)
print 'on task',rank,'    recvdata = ',recvdata```

Global Data Reduction

The data reduction functions of MPI combine data from all tasks with one of a set of predefined operations and deliver the result of this “reduction” to the root tsks or to all tasks. mpi4py provides the following operations for reduction:

```MPI.MIN        minimum
MPI.MAX        maximum
MPI.SUM        sum
MPI.PROD       product
MPI.LAND       logical and
MPI.BAND       bitwise and
MPI.LOR        logical or
MPI.BOR        bitwise or
MPI.LXOR       logical xor
MPI.BXOR       bitwise xor
MPI.MAXLOC     max value and location
MPI.MINLOC     min value and location```

The reduction operations need data of the appropriate type.

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)
comm.Reduce_scatter_block(senddata,recvdata,op=MPI.SUM)

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)
comm.Reduce_scatter(senddata,recvdata,recv_size,op=MPI.SUM)

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)