I guess you're right, without the info about the machine, this doesn't help much!
This was running on a new machine at SLAC called 'orange-bigmem' -- it's a 32node machine with a ton of memory available to all the processors. I checked memory usage at the end of the run, and after the projection ahd been save out a few times it was around 1.5 gigs per node. I'm threading some outputs of the total memory usage through the projection code, and hopefully that will give us an idea of the peak memory usage.
The file system is lustre, which works well with the preloading of the data, and I ran it a couple times beforehand to make sure that the files were in local cache or whatever.
So the communication was via shared memory, which while still an MPI interface is much closer to ideal. I will be giving it a go on a cluster tomorrow, after I work out some kinks with data storage. I've moved the generation of the binary hierarchies into yt -- so if you don't have one, rather than dumping the hierarchy into the .yt file, it will dump it into the .harrays file. This way if anyone else writes an interface for the binary hierarchy method, we can all share it. (I think it would be a bad idea to have Enzo output a .yt file. ;-) The .yt file will now exist solely to store objects, not any of the hierarchy info.
On Mon, Nov 2, 2009 at 10:13 PM, Sam Skillman 72Nova@gmail.com wrote:
Hi Matt, This is awesome. I don't think anyone can expect much faster for that dataset. I remember running projections just a year or so ago on this data and it taking a whole lot more time (just reading in the data took ages). What machine were you able to do this on? I'm mostly curious about the memory it used, or had available to it. In any case, I'd say this is a pretty big success, and the binary hierarchies are a great idea. Cheers, Sam On Mon, Nov 2, 2009 at 8:47 PM, Matthew Turk email@example.com wrote:
(For all of these performance indicators, I've used the 512^3 L7 amr-everywhere run called the "LightCone." This particular dataset has ~380,000 grids and is a great place to find the )
Last weekend I did a little bit of benchmarking and saw that the parallel projections (and likely several other parallel operations) all sat inside an MPI_Barrier for far too long. I converted (I think!) this process to be an MPI_Alltoallv operation, following on an MPI_Allreduce to get the final array size and the offsets into an ordered array, and I think it is working. I saw pretty good performance improvements, but it's tough to quantify those right now -- for projecting "Ones" (no disk-access) it sped things up by ~15%.
I've also added a new binary hierarchy method to devel enzo, and it provides everything that is necessary for yt to analyze the data. As such, if a %(basename)s.harrays file exists, it will be used, and yt will not need to open the .hierarchy file at all. This sped things up by 100 seconds. I've written a script to create these (http://www.slac.stanford.edu/~mturk/create_harrays.py), but outputting them inline in Enzo is the fastest.
To top this all off, I ran a projection -- start to finish, including all overhead -- on 16 processors. To project the fields "Density" (native), "Temperature" (native) and "VelocityMagnitude" (derived, requires x-, y- and z-velocity) on 16 processors to the finest resolution (adaptive projection -- to L7) takes 140 seconds, or roughly 2:20.
I've looked at the profiling outputs, and it seems to me that there are still some places performance could be squeezed out. That being said, I'm pretty pleased with these results.
These are all in the named branch hierarchy-opt in mercurial. They rely on some rearrangement of the hierarchy parsing and whatnot that has lived in hg for a little while; it will go into the trunk as soon as I get the all clear about moving to a proper stable/less-stable dev environment. I also have some other test suites to run on them, and I want to make sure the memory usage is not excessive.
Matt _______________________________________________ Yt-dev mailing list Ytfirstname.lastname@example.org http://lists.spacepope.org/listinfo.cgi/yt-dev-spacepope.org
-- Samuel W. Skillman DOE Computational Science Graduate Fellow Center for Astrophysics and Space Astronomy University of Colorado at Boulder samuel.skillman[at]colorado.edu
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