[GRASS-dev] Re: parallelizing GRASS modules

Michael Barton michael.barton at asu.edu
Sat Dec 3 17:45:02 EST 2011

On Dec 3, 2011, at 3:38 PM, Hamish wrote:

> Michael wrote:
>> I lost the previous thread but wanted to respond
>> to your question about which modules might
>> benefit from speedup.
>> In our recursive landscape evolution module
>> (r.landscape.evol.py), the two GRASS modules that
>> take the most time are r.watershed, r.stats, and
>> r.walk, especially r.watershed and r.stats since
>> we need to run these every model cycle.
>> The speedup of r.watershed of a few years back
>> made an enormous difference in our model run
>> times. But it is still time consuming on
>> landscapes with large numbers of cells. If
>> parallelization could speed this up, it would be
>> great. I'm not sure that r.stats can be
>> parallelized or not, but speedup would be helpful.
> wrt r.walk: I would think to start with parallelizing r.cost, then porting
> the method over to r.walk. Both modules use the segment library, so coding
> it to process each segment in its own thread seems like the way to do that.
> (and more generally formulate + document a method to parallelize things that use the segment library.
> perhaps a simple proof-of-method module to do that should come first)

I was hoping that this would be the answer. So improvements to the segment library should help both (and also v.surf.bspline and v.surf.rst??)

> wrt r.watershed: I guess we'd want a segment mode like the -m flag, but
> keeping things in RAM instead of using disk swap? Segment mode does make
> use of the segment library..   MarkusM?

Sounds promising.

> wrt r.stats: I suspect it is mostly I/O bound so I don't know how much faster
> it can be made, but I ran it through the valgrind profiler anyway just to see.
> (see the wiki Bugs page for recipe)
> 22% of the time is spent in G_quant_get_cell_value()
> 13% of the time is spent in lib/gis/get_row.c's cell_values_double()
> 10% of the time is spent in r.stats/stats.c's update_cell_stats() updating a
> hash table. may be possible to parallelize that.

But not much gain it seems. 

> if multiple input maps are used perhaps each could be processed in their own
> thread, but I don't think you are doing that with LandDyn.

You're right

> perhaps the way that r.stats is called/used by LandDyn could be refined? ie
> is there a lot of unnecessary calculations going on just to get a simple
> stat out which could more efficiently be answered in another way? (no idea,
> but worth exploring)

No help in that regard. There are other things we're doing like working through long lists that might be speeded up, but these are in Python and Java, not GRASS. So they can't be helped by parallelization. 

This overall sounds encouraging, however.


> Hamish

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