[MPIWG Fortran] type inference like mpi4py

[Jeff Hammond]

Recently, I have been writing mpi4py and Fortran 2008 MPI code (
https://github.com/ParRes/Kernels/pull/592), which ends up looking quite
similar except for 0-1 base indexing and MPI argument deduction.

Numpy arrays behave a lot like Fortran arrays, including how they store
size information in them.

I wonder if it is reasonable to add this same argument inference to MPI
Fortran.  If I pass an array argument with no type or size information, it
should be inferred.

The first inference is type.  There is no reason to ask users to specify
MPI_DOUBLE_PRECISION when the argument is of type double precision.
Obviously, this only works for built-in types, but as that is the common
case, why not do it?

The second inference is size.  If I pass A(100) to MPI_Bcast, why do I need
to say MPI_Bcast(buf=A,count=100,...)?  The dope vector for A contains the
100 already.

The hard part here seems to be needing 15 dimensions worth of interfaces,
but those are trivial to generate.

Are there any hard problems here that I don't realize?

Thanks,

Jeff

PS code excerpts from the link above.  Named arguments would make Fortran
even more similar.

for phase in range(0,np):
  recv_from = (me + phase ) % np
  send_to = (me - phase + np) % np
  lo = block_order * send_to
  hi = block_order * (send_to+1)

comm.Sendrecv(sendbuf=A[lo:hi,:],dest=send_to,sendtag=phase,recvbuf=T,source=recv_from,recvtag=phase)
  lo = block_order * recv_from
  hi = block_order * (recv_from+1)
  B[lo:hi,:] += T.T

do q=0,np-1

  recv_from = mod( (me + q     ), np)

  send_to   = mod( (me - q + np), np)

  lo = block_order * send_to + 1

  hi = block_order * (send_to+1)

  call MPI_Sendrecv(A(:,lo:hi), block_order*block_order,
MPI_DOUBLE_PRECISION,    &

                    send_to,q,
      &

                    T,block_order*block_order, MPI_DOUBLE_PRECISION,
        &

                    recv_from, q, MPI_COMM_WORLD, MPI_STATUS_IGNORE)

  lo = block_order * recv_from + 1

  hi = block_order * (recv_from+1)

  B(:,lo:hi) = B(:,lo:hi) + transpose(T)

-- 
Jeff Hammond
jeff.science at gmail.com
http://jeffhammond.github.io/
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