Hello,
I am sorry I did not attach any files since it wasn't possible to attach a 64
MB file. I am attaching the program for a smaller run.
Hi. I do this all the time too -- but there was no code in your
message.
The folder ptestrun contains the program to read a binary file "mfdn.smwf001"
and write the same binary file into another file "binopfile". It also uses a
HDF5 file hdfref, to read the lengths being read and written by the
processors. The program does IO in exactly the same way our actual
code does. IO happens as
follows.
1. Proc 0 reads its part of the vector. Then it reads the vector for the
remaining processors and sends(tag=1) it to the respective processors.
2. Proc 0 writes its part of the vector. Then it writes the vector for the
remaining processors that it receives(tag=2) from the respective processors.
This approach of having rank 0 drive the entire I/O process is going
to give you pretty poor performance, right?
Yes it should give poor performance. But this is what I am trying
to improve by doing Parallel IO using PHDF5. This program basically emulates
the IO in the older version which I intend to improve.
You're on the right track, using MPI to carry out some coordination
before performing I/O. Just take that a little further.
parallel HDF5 really needs two things to perform well:
- simultaneous accesses from all processes (via hyperslabs)
- collective I/O
The folder testrun contains the program to write the output file in a HDF5
file. It reads the contents from a HDF5 file "mfdn.smwf001" and writes the
dataset named "EigenVectors__" to a new HDF5 file "hdfopfile". The lengths
written by various procs can stored in the dataset "LENGTHS". The IO happens
as
follows.
1. Get the dataset dims from the datasets named "EigenVectors__"
OK, you can do this with rank 0. I'd suggest rank 0 broadcast the
result to everyone. You could either use MPI_Bcast or MPI_Scatter.
Use MPI_Bcast if each process has to compute their 'offset' into the
dataset as well. Use MPI_Scatter if all they need is a length.
this is a very minor issue. I do this only in the test
program since there is no other way to compute the dimensions. In the main
program, IO is not required to find the dimension. I basically mentioned this
so that it will become easier to understand the code.
2. Create the HDF5 o/p file accordingly.
3. Read the datasets from "mfdn.smwf001" and write it to "hdfopfile"
Once all processes have the lenghts and have computed their offsets,
then you can create hyperslabs to set up a collective read from
mfdn.smwf001 and a collective write to hdfopfile.
this is exactly what I am doing(or rather trying to do).
the reads and writes are parallel and issued by all the processors
simultaneously. Unfortunately in my case independent IO outperforms collective
IO.
can tell if I am making any mistakes that is leading to the slow IO. I can't
send the larger runs as attachments since they are around 64 MB and 2 GB.
Maybe your slow performance is because you are you are using the
parallel HDF5 interface to carry out essentially serial I/O? Do
follow up with your code.
I have attached the code. Please have a look if you can.
Thanks a lot.
Nikhil
Regards,
Nikhil
testrun.zip (170 KB)
ptestrun.zip (1.07 MB)
