Efficiency of Fortran stream access vs. MPI-IO - io

Question

I have a parallel section of the code where I write out n large arrays (representing a numerical mesh) in blocks that are later read in different sized blocks. To do this I used Stream access so each processor writes their block independently, but I've seen inconsistent timings taking from 0.5-4 seconds in this section testing with 2 processor groups.
I am aware you can do something similar with MPI-IO, but I'm not sure what the benefits would be since there is no synchronization necessary. I would like to know if there is a way to either improve performance of my writes, or if there is a reason MPI-IO would be a better choice for this section.
Here is a sample of the code section where I create the files to write norb arrays using two groups (mygroup = 0 or 1]:
do irbsic=1,norb
[various operations]
blocksize=int(nmsh_tot/ngroups)
OPEN(unit=iunit,FILE='ZPOT',STATUS='UNKNOWN',ACCESS='STREAM')
mypos = 1 + (IRBSIC-1)*nmsh_tot*8 ! starting point for writing IRBSIC
mypos = mypos + mygroup*(8*blocksize) ! starting point for mesh group
WRITE(iunit,POS=mypos) POT(1:nmsh)
CLOSE(iunit)
OPEN(unit=iunit,FILE='RHOI',STATUS='UNKNOWN',ACCESS='STREAM')
mypos = 1 + (IRBSIC-1)*nmsh_tot*8 ! starting point for writing IRBSIC
mypos = mypos + mygroup*(8*blocksize) ! starting point for mesh group
WRITE(iunit,POS=mypos) RHOG(1:nmsh,1,1)
CLOSE(iunit)
[various operations]
end do

Answer 1

(As discussed in the comments) I would strongly recommend against using Fortran stream access for this. Standard Fortran I/O is only guaranteed to work if the file is being accessed by a single process, and in my own work I have seen random corruptions of files when multiple processes try to write to them at once, even if the processes are writing to different parts of the file. MPI-I/O, or a library such as HDF5 or NetCDF which uses MPI-I/O is the only sensible way to achieve this. Below is a simple program illustrating the use of mpi_file_write_at_all
ian#eris:~/work/stack$ cat at.f90
Program write_at
Use mpi
Implicit None
Integer, Parameter :: n = 4
Real, Dimension( 1:n ) :: a
Real, Dimension( : ), Allocatable :: all_of_a
Integer :: me, nproc
Integer :: handle
Integer :: i
Integer :: error
! Set up MPI
Call mpi_init( error )
Call mpi_comm_size( mpi_comm_world, nproc, error )
Call mpi_comm_rank( mpi_comm_world, me , error )
! Provide some data
a = [ ( i, i = n * me, n * ( me + 1 ) - 1 ) ]
! Open the file
Call mpi_file_open( mpi_comm_world, 'stuff.dat', &
mpi_mode_create + mpi_mode_wronly, mpi_info_null, handle, error )
! Describe how the processes will view the file - in this case
! simply a stream of mpi_real
Call mpi_file_set_view( handle, 0_mpi_offset_kind, &
mpi_real, mpi_real, 'native', &
mpi_info_null, error )
! Write the data using a collective routine - generally the most efficent
! but as collective all processes within the communicator must call the routine
Call mpi_file_write_at_all( handle, Int( me * n,mpi_offset_kind ) , &
a, Size( a ), mpi_real, mpi_status_ignore, error )
! Close the file
Call mpi_file_close( handle, error )
! Read the file on rank zero using Fortran to check the data
If( me == 0 ) Then
Open( 10, file = 'stuff.dat', access = 'stream' )
Allocate( all_of_a( 1:n * nproc ) )
Read( 10, pos = 1 ) all_of_a
Write( *, * ) all_of_a
End If
! Shut down MPI
Call mpi_finalize( error )
End Program write_at
ian#eris:~/work/stack$ mpif90 --version
GNU Fortran (Ubuntu 7.4.0-1ubuntu1~18.04.1) 7.4.0
Copyright (C) 2017 Free Software Foundation, Inc.
This is free software; see the source for copying conditions. There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
ian#eris:~/work/stack$ mpif90 -Wall -Wextra -fcheck=all -std=f2008 at.f90
ian#eris:~/work/stack$ mpirun -np 2 ./a.out
0.00000000 1.00000000 2.00000000 3.00000000 4.00000000 5.00000000 6.00000000 7.00000000
ian#eris:~/work/stack$ mpirun -np 5 ./a.out
0.00000000 1.00000000 2.00000000 3.00000000 4.00000000 5.00000000 6.00000000 7.00000000 8.00000000 9.00000000 10.0000000 11.0000000 12.0000000 13.0000000 14.0000000 15.0000000 16.0000000 17.0000000 18.0000000 19.0000000
ian#eris:~/work/stack$