[GRASS-dev] measuring cpu time for each module and estimating modules scalability

Sören Gebbert soerengebbert at googlemail.com
Fri Aug 8 05:29:07 PDT 2014

Hi Jachym,
here some suggestions from me:
* When using PyGRASS: please create a Module() object before the
benchmark run (run_=False) because PyGRASS will call the GRASS module
to analyse its XML output before any processing is done. This may
influence the runtime and therefore the benchmark significantly for
small data.
* Try to make a sync (man sync) at the end of each run, to avoid
caching of output data in the main memory measurement instead of hard
disk IO performance
* Please consider to integrate your benchmark suite into the GRASS
testing framework GSoC project [1] which already includes runtime

Best regards

[1] http://trac.osgeo.org/grass/wiki/GSoC/2014/TestingFrameworkForGRASS

2014-08-08 13:00 GMT+02:00 Jachym Cepicky <jachym.cepicky at gmail.com>:
> Hi all,
> just some intellectual thoughts for the weekend (I'm off for a
> weekend, so no need to rush with the answer). I started to write some
> script  [1], which tries to estimate CPU time, needed for each GRASS
> module (currently, I have about 4-5 raster modules). Target is to
> create suit (rather then simple script), which could estimate how each
> process scales (based on number of features or raster cells, which
> need to be processed) and put GRASS modules into row. Also estimate
> rawly CPU time, which is consumed by each module.
> Any thoughts to this?
> I started with the dataset from Martin [2], but I thing, I'll switch
> over to spearfish, one resolution for now. I did not figure out yet,
> how to implement equivalent of unix time [3] program in Python
> Thanks
> Jachym
> [1] https://github.com/jachym/grass-performace
> [2] http://geo.fsv.cvut.cz/data/grasswikicz/freegeodatacz/aktualni/
> [3] https://github.com/jachym/grass-performace
> --
> Jachym Cepicky
> e-mail: jachym.cepicky gmail com
> URL: http://les-ejk.cz
> GPG: http://les-ejk.cz/pgp/JachymCepicky.pgp
> Give your code freedom with PyWPS - http://pywps.wald.intevation.org
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