[gradsusr] Performance Tips

[Travis Wilson - NOAA Federal]

Hi All,



Attached is a very short ppt on grads performance vs python using grib
files.  In most cases, grads blows python away.  Times are relative to our
machine and consider everything from starting grads/opening the file, to
closing the file.



- In particular we have found that shaded1 is much faster.  Up to 40%
faster on our machines.

- Wesley Ebisuzaki recommended converting the grib files to a lat/lon grid
to eliminate the PDEF entry to significantly speed up the opening time of
high resolution grib files.
http://gradsusr.org/pipermail/gradsusr/2016-January/039339.html

- Again noted by Wesley, grib packing can have an impact on performance
http://gradsusr.org/pipermail/gradsusr/2010-May/027683.html



One thing we show in the ppt is that as the view gets wider (i.e. the
number of points that are plotted increase), the slower grads is relative
to python.  At some point, python will become faster.   Anyways, to battle
with this, regridding (using the re() function) the data within grads
significantly speeds up the plotting time (see last slide) when you have a
lot of points.  As far as I know, you can’t use re() in grads 2.1a3.  You
do have lterp() but a grid is needed.  Is there anything that will allow me
to lterp to my image dimensions?  Say my image dimensions are x800 y600
then lterp would interpolate my high resolution grib file to x800 y600 (or
some multiple of) when a view exceeds 800 points across.  This will
significantly speed up the plotting time when viewing a wide view of a high
resolution grib file while not degrading the image quality by much (again,
see last slide).



Also, if anyone has other performance tips on plotting high resolution grib
files we would love to hear them.



Thanks,

Travis
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