r ? (which of course you would have to be the
ultimate Guru here :-)
Many thanks,
Shaun
-Original Message-
From: Frank Warmerdam [mailto:warmer...@pobox.com]
Sent: Thursday, 19 November 2009 11:20 AM
To: Shaun Kolomeitz
Cc: Seth Price; gdal-dev@lists.osgeo.org
Subject: Re: [gdal-dev]
On Wed, Nov 18, 2009 at 7:19 PM, Frank Warmerdam wrote:
> Shaun Kolomeitz wrote:
>
>> Thanks Seth,
>>
>> It makes sense that the slowest part of the whole equation would be the
>> disk operations, and there must be quite a number of disk reads/writes
>> when processing imagery. Currently we use Ra
Shaun Kolomeitz wrote:
Thanks Seth,
It makes sense that the slowest part of the whole equation would be the
disk operations, and there must be quite a number of disk reads/writes
when processing imagery. Currently we use Raid Arrays that push data
through at a rate of 300MB/s, granted if these w
grid
computing approach then like Condor to break up the processing ?
Cheers,
Shaun
-Original Message-
From: Seth Price [mailto:s...@pricepages.org]
Sent: Thursday, 19 November 2009 8:07 AM
To: Shaun Kolomeitz
Cc: gdal-dev@lists.osgeo.org
Subject: Re: [gdal-dev] CUDA PyCUDA and GDAL
I&#x
I've been intending for a while to work on either CUDA or OpenCL with GDAL
& GRASS. I applied to do this for the Google Summer of Code, but wasn't
accepted this past summer. I'll probably work on it someday just to make
sure my thesis work gets finished within budget.
However, I'm mostly interest
I've heard a lot about the power of NVidia CUDA and am curious about
ways in which we could leverage off this to increase the performance of
1) Image Mosaics 2) Translates and 3) Image Reading/rendering
(especially highly compressed images).
I also see that there is pyCUDA as well. Both of which I
