[gradsusr] Performance Tips

[dan martin]

I use the lats4d version that comes with opengrads and it has the netcdf4
option.

On Mon, Feb 15, 2016 at 4:15 AM, Jennifer M Adams <jadams21 at gmu.edu> wrote:

> Does lats4d write out netcdf4?
>
> On Feb 14, 2016, at 3:36 PM, dan martin <sailmaui at gmail.com> wrote:
>
> I am not using the 1k data, but I have the same i/o speed issues with the
> nested nam conus. I've optimized by converting to netcdf. I download the
> grib2 and run g2ctl and gribmap. Then lats4d to convert to netcdf. This
> makes a lat/lon file and de-compresses the grib2. This is substantially
> faster than dealing with grib2 and pdef.
>
>
> Here is a live example:
>
>
> http://www.meteogram.com/servlet/metChart?zoom=20&gs=wrfradar&tau=2&lat=38.83&lon=-77.31&units=e
>
> You can change the chart by adjusting the inputs. The zoom is roughly the
> longitude domain in degrees. As shown the larger the domain the longer it
> takes to load. If you change that to 50 and then to 5 you'll notice the
> speed difference. Overall it is fine for my needs. If I left it grib2 and
> pdef, it takes too long.
>
> Dan
>
>
>
>
> On Tue, Feb 9, 2016 at 12:37 PM, Travis Wilson - NOAA Federal <
> travis.wilson at noaa.gov> wrote:
>
>> 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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>>
>>
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>
> --
> Jennifer Miletta Adams
> Center for Ocean-Land-Atmosphere Studies (COLA)
> George Mason University
>
>
>
>
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