Thank you Edgar for the information.
I also tried MPI_File_write_at_all(), but it usually makes the
performance worse.
My program is very simple.
Each MPI process writes a consecutive portion of a file.
No interleaving among the MPI processes.
I think in this case I can use MPI_File_write_at().
I tested the maximum bandwidth of the target devices and they are at least
a few times bigger than what single process can achieve.
I tested it using the same program but open the individual files using
MPI_COMM_SELF.
I tested 32MB chunk, but didn't show noticeable changes. I also tried 512MB
chunk, but no noticeable difference.
(There are performance differences between using 32MB chunk and using 512MB
chunk.
But, they still don't make multiple MPI processes file IO exceeds the
performance of single MPI process file IO)
As for the local disk, at least 2 times faster than single MPI process can
achieve.
As for the ramdisk, at least 5 times faster.
Luster, I know that it is at least 7-8 times or more faster depending on
the configuration.
About caching effect, it would be the case of MPI_File_read().
I can see very high bandwidth of MPI_File_read(), which I believe comes
from caches in RAM.
But as for MPI_File_write, I think it doesn't be affected by caching.
And I create a new file for each test and removes the file at the end of
the testing.
I may make a very simple mistake, but I don't know what it is.
I saw MPI_File I/O could achieve multiple times of speedup over single
process file IO,
when faster file system is used like Lustre from a few reports in the
internet.
I started this experiment because I couldn't get speedup on Lustre file
system.
And then I moved the experiment to ramdisk and local disk, because it can
remove the issue of Lustre configuration.
Any comments are welcome.
David
On Mon, Apr 6, 2020 at 9:03 AM Gabriel, Edgar <egabr...@central.uh.edu>
wrote:
> Hi,
>
>
>
> A couple of comments. First, if you use MPI_File_write_at, this is usually
> not considered collective I/O, even if executed by multiple processes.
> MPI_File_write_at_all would be collective I/O.
>
>
>
> Second, MPI I/O can not do ‘magic’, but is bound by hardware that you are
> providing. If already a single process is able to saturate the bandwidth of
> your file system and hardware, you will not be able to see performance
> improvements from multiple processes (some minor exceptions maybe due to
> caching effects, but that is only for smaller problem sizes, the larger the
> amount of data that you try to write, the lesser the caching effects become
> in file I/O). So the first question that you have to answer, what is the
> sustained bandwidth of your hardware, and are you able to saturate it
> already with a single process. If you are using a single hard drive (or
> even 2 or 3 hard drives in a RAID 0 configuration), this is almost
> certainly the case.
>
>
>
> Lastly, the configuration parameters of your tests also play a major role.
> As a general rule, the larger amounts of data you are able to provide per
> file I/O call, the better the performance will be. 1MB of data per call is
> probably on the smaller side. The ompio implementation of MPI I/O breaks
> large individual I/O operations (e.g. MPI_File_write_at) into chunks of
> 512MB for performance reasons internally. Large collective I/O operations
> (e.g. MPI_File_write_at_all) are broken into chunks of 32 MB. This gives
> you some hints on the quantities of data that you would have to use for
> performance reasons.
>
>
>
> Along the same lines, one final comment. You say you did 1000 writes of
> 1MB each. For a single process that is about 1GB of data. Depending on how
> much main memory your PC has, this amount of data can still be cached in
> modern systems, and you might have an unrealistically high bandwidth value
> for the 1 process case that you are comparing against (it depends a bit on
> what your benchmark does, and whether you force flushing the data to disk
> inside of your measurement loop).
>
>
>
> Hope this gives you some pointers on where to start to look.
>
> Thanks
>
> Edgar
>
>
>
> *From:* users <users-boun...@lists.open-mpi.org> *On Behalf Of *Dong-In
> Kang via users
> *Sent:* Monday, April 6, 2020 7:14 AM
> *To:* users@lists.open-mpi.org
> *Cc:* Dong-In Kang <dik...@gmail.com>
> *Subject:* [OMPI users] Slow collective MPI File IO
>
>
>
> Hi,
>
>
>
> I am running an MPI program where N processes write to a single file on a
> single shared memory machine.
>
> I’m using OpenMPI v.4.0.2.
>
> Each MPI process write a 1MB chunk of data for 1K times sequentially.
>
> There is no overlap in the file between any of the two MPI processes.
>
> I ran the program for -np = {1, 2, 4, 8}.
>
> I am seeing that the speed of the collective write to a file for -np = {2,
> 4, 8} never exceeds the speed of -np = {1}.
>
> I did the experiment with a few different file systems {local disk, ram
> disk, Luster FS}.
>
> For all of them, I see similar results.
>
> The speed of collective write to a single shared file never exceeds the
> speed of single MPI process case.
>
> Any tip or suggestions?
>
>
>
> I used MPI_File_write_at() routine with proper offset for each MPI process.
>
> (I also tried MPI_File_write_at_all() routine, which makes the performance
> worse as np gets bigger.)
>
> Before writing, MPI_Barrrier() is used.
>
> The start time is taken right after MPI_Barrier() using MPI_Timer();
>
> The end time is taken right after another MPI_Barrier().
>
> The speed of the collective write is calculate as
>
> (total data amount written to the file)/(time between the first
> MPI_Barrier() and the second MPI_Barrier());
>
>
>
> Any idea to increase the speed?
>
>
>
> Thanks,
>
> David
>
>
>
--
=========
Jesus is My Lord!
