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.


On Mon, Nov 2, 2009 at 8:47 PM, Matthew Turk <matthewturk@gmail.com> wrote:
Hi guys,

(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.


Yt-dev mailing list

Samuel W. Skillman
DOE Computational Science Graduate Fellow
Center for Astrophysics and Space Astronomy
University of Colorado at Boulder