Hi Eliseo,
This is very useful and interesting data, thanks for providing it.
When the I/O gets gridlocked at the end, are you in a tight loop polling the
file, or do you wait between poll operations? Are you able to share your
programs? In a couple of months, I’ll be working on some improvements to the
performance and memory for getting data from a writer to a reader and it would
be good to have test code like this to work with.
Quincey
> On Oct 20, 2017, at 3:47 PM, Eliseo Gamboa <[email protected]> wrote:
>
> Hi,
>
> I am experiencing some major performance issues in SWMR mode when a reader
> refreshes the metadata in a file.
>
> I’m testing I/O performance on our data systems using SWMR and h5py. I have a
> process writing out random data to a single dataset, which is then read by a
> single reader. The files are striped across a Lustre file system with three
> OSTs.
>
> In a nutshell, the reader process refreshes the metadata, loops through and
> reads the dataset until it runs out of data, then refreshes the metadata
> again, loops through the new data and so on.
>
> Refreshing the metadata with dset.id.refresh() lags both the reader and
> writer processes for several seconds. This gets worse the more data that is
> refreshed (usually several gb at a time).
>
> Please see the attached image for plots of the reading/writing rates to disk.
> The reading in this test case is slightly faster than the writing. Eventually
> the reader catches up to the writer and tries to call dset.id.refresh() on
> each loop iteration. At this point the I/O gets gridlocked and comes to a
> near standstill.
>
> Thanks,
> Eliseo
>
> <PastedGraphic-1.tiff>
>
>
> Thanks,
> Eliseo
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