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Hi Ingo, The file is physically written (or read) as a sequence of bytes, clusters, or sectors (depending on storage device); that is serially. Only function of "parallel-enabled" file API is making an illusion of parallel access. It makes sense only if you don't bother about performance. Much better performance can be achieved when serialization is implemented in the program (executed in parallel) considering internal program logic; then the file is accessed in serial way which is obviously faster. And if you really care about performance use native HDF API: it is much faster. Regards, Sergei -----Original Message----- From: ingo.bethke@xxxxxx [mailto:ingo.bethke@xxxxxx] Sent: 19 September 2011 20:25 To: Shibaev, Sergei Cc: netcdfgroup@xxxxxxxxxxxxxxxx Subject: Re: [netcdfgroup] NetCDF4 Parallel-enabled IO performance issue Hi Sergei, Could you please elaborate on what you wrote? Do you think the problem is a file (or record) locking issue? if yes, would that explain the different result for read and write? I've tried to test parallel-io on our CRAY XT4 but didn't manage to get even close to the serial io performance... Thanks, Ingo Siterer "Shibaev, Sergei" <Sergei.Shibaev@xxxxxxxxxx>: > Hi Wei, > > Your result is quite obvious because the file itself is a serial device, > so "parallel" read/write means serialising of parallel requests. Of > course, it is at least two times slower than serial requests from single > process. > If you can make file access serialising in your program it could be much > faster than common parallel-enabled API. > > Regards, > Sergei Shibaev > > -----Original Message----- > From: netcdfgroup-bounces@xxxxxxxxxxxxxxxx > [mailto:netcdfgroup-bounces@xxxxxxxxxxxxxxxx] On Behalf Of Wei Huang > Sent: 19 September 2011 17:28 > To: netcdfgroup@xxxxxxxxxxxxxxxx > Subject: [netcdfgroup] NetCDF4 Parallel-enabled IO performance issue > > > Hi, netcdfgroup, > > Currently, we are trying to use parallel-enabled NetCDF4. We started > with read/write a 5G file and some computation, we got the following > timing (in wall-clock) on a IBM power machine: > Number of Processors Total(seconds) read(seconds) Write(seconds) > Computation(seconds) > seq 89.137 28.206 > 48.327 11.717 > 1 178.953 44.837 > 121.17 11.644 > 2 167.25 46.571 > 113.343 5.648 > 4 168.138 44.043 > 118.968 2.729 > 8 137.74 25.161 > 108.986 1.064 > 16 113.354 16.359 > 93.253 0.494 > 32 439.481 122.201 > 311.215 0.274 > 64 831.896 277.363 > 588.653 0.203 > > First thing we can see is that when run parallel-enabled code at one > processor, the total wall-clok time doubled. Then we did not see the > scaling when more processors added. > > Anyone wants to share their experience? > > Thanks, > > Wei Huang > huangwei@xxxxxxxx > VETS/CISL > National Center for Atmospheric Research > P.O. Box 3000 (1850 Table Mesa Dr.) > Boulder, CO 80307-3000 USA > (303) 497-8924 > > > > _______________________________________________ > netcdfgroup mailing list > netcdfgroup@xxxxxxxxxxxxxxxx > For list information or to unsubscribe, visit: > http://www.unidata.ucar.edu/mailing_lists/ > > > Click > https://www.mailcontrol.com/sr/qAdBheWrG8zTndxI!oX7UhQ1x5oWB0K1JQiz+EsP7 > a8E+4PlxZ84awGZMwDw3dulXStBmSRlfipTHufDF4Ashw== to report this email as > spam. > > _______________________________________________ > netcdfgroup mailing list > netcdfgroup@xxxxxxxxxxxxxxxx > For list information or to unsubscribe, visit: > http://www.unidata.ucar.edu/mailing_lists/ >
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