[MPIWG Fortran] type inference like mpi4py

[Wesley Bland]

I’d suggest that this might be a good discussion for the Languages WG (mpiwg-languages at lists.mpi-forum.org <mailto:mpiwg-languages at lists.mpi-forum.org>). They’re working on these sorts of issues related to any language (Python, C++, Fortran, etc.).

> On Nov 29, 2021, at 4:51 AM, Jeff Hammond via mpiwg-fortran <mpiwg-fortran at lists.mpi-forum.org> wrote:
> 
> Recently, I have been writing mpi4py and Fortran 2008 MPI code (https://github.com/ParRes/Kernels/pull/592 <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 <mailto:jeff.science at gmail.com>
> http://jeffhammond.github.io/ <http://jeffhammond.github.io/>_______________________________________________
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