Hi Matt & friends,

I tested this on a fairly large nested simulation with about 60k grids using 6 nodes of Janus (dual-hex nodes) and ran from 1 to 64 processors.  I got fairly good scaling and made a quick mercurial repo on bitbucket with everything except the dataset needed to do a similar study. https://bitbucket.org/samskillman/quad-tree-proj-performance

Raw timing:
projects/quad_proj_scale:more perf.dat 
64 2.444e+01
32 4.834e+01
16 7.364e+01
8 1.125e+02
4 1.853e+02
2 3.198e+02
1 6.370e+02

A few notes: 
-- I ran with 64 cores first, then again so that the disks were somewhat warmed up, then only used the second timing of the 64 core run.
-- While I did get full nodes, the machine doesn't have a ton of I/O nodes so in an ideal setting performance may be even better.
-- My guess would be that a lot of this speedup comes from having a parallel filesystem, so you may not get as great of speedups on your laptop.
-- Speedup from 32 to 64 is nearly ideal...this is great.

This looks pretty great to me, and I'd +1 any PR.  


On Thu, May 3, 2012 at 1:42 PM, Matthew Turk <matthewturk@gmail.com> wrote:
Hi all,

I implemented this "quadtree extension" that duplicates the quadtree
on all processors, which may make it nicer to scale projections.
Previously the procedure was:

1) Locally project
2) Merge across procs:
 2a) Serialize quadtree
 2b) Point-to-point communciate
 2c) Deserialize
 2d) Merge local and remote
 2d) Repeat up to 2a
3) Finish

I've added a step 0) which is "initialize entire quadtree", which
means all of step 2 becomes "perform sum of big array on all procs."
This has good and bad elements: we're still doing a lot of heavy
communication across processors, but it will be managed by the MPI
implementation instead of by yt.  Also, we avoid all of the costly
serialize/deserialize procedures.  So for a given dataset, step 0 will
be fixed in cost, but step 1 will be reduced as the number of
processors goes up.  Step 2, which now is a single (or two)
communication steps, will increase in cost with increasing number of

So, it's not clear that this will *actually* be helpful or not.  It
needs testing, and I've pushed it here:

hash 3f39eb7bf468

If anybody out there could test it, I'd be might glad.  This is the
script I've been using:


I'd *greatly* appreciate testing results -- particularly for proc
combos like 1, 2, 4, 8, 16, 32, 64, ... .  On my machine, the results
are somewhat inconclusive.  Keep in mind you'll have to run with the

--config serialize=False

to get real results.  Here's the shell command I used:

( for i in 1 2 3 4 5 6 7 8 9 10 ; do mpirun -np ${i} python2.7 proj.py
--parallel --config serialize=False ; done ) 2>&1 | tee proj_new.log

Comparison against results from the old method would also be super helpful.

The alternate idea that I'd had was a bit different, but harder to
implement, and also with a glaring problem.  The idea would be to
serialize arrays, do the butterfly reduction, but instead of
converting into data objects simply progressively walk hilbert
indices.  Unfortunately this only works for up to 2^32 effective size,
which is not going to work in a lot of cases.

Anyway, if this doesn't work, I'd be eager to hear if anybody has any ideas.  :)

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