My lab had a GPU library for Random Forests on Images.
It was pretty fast but probably needs some updates:
https://github.com/deeplearningais/curfil
On 8/8/18 10:01 PM, Tommy Tracy wrote:
Dear Ta Hoang,
Accelerating decision tree ensembles (including Random Forest) is
actually a current area of computer architecture research; in fact it
is a principle component of my dissertation. Like Sebastian Raschka
said, the GPU is not an ideal architecture for decision tree inference
because at its core it is a pointer-chasing algorithm (low computation
per memory access) that shows low memory locality. Scikit-Learn has
done an excellent job with their von Neumann implementation utilizing
things like predication and vectorization. If you're looking to go
beyond what the CPU can give you, I would point you to FPGAs. If
you're interested in discussing this further, let me know.
--
--
Sincerely,
Tommy James Tracy II
Ph.D Candidate
Computer Engineering
UniversityofVirginia
On Wed, Aug 8, 2018 at 8:50 PM, hoang trung Ta <[email protected]
<mailto:[email protected]>> wrote:
Dear all members,
I am using Random forest for classification satellite images. I
have a bunch of images, thus the processing is quite slow. I
searched on the Internet and they said that GPU can accelerate the
process.
I have GPU NDVIA Geforce GTX 1080 Ti installed in the computer
Do you know how to use GPU in Scikit learn, I mean the packages to
use and sample code that used GPU in random forest classification?
Thank you very much
--
*Ta Hoang Trung (Mr)*
*
*
/Master student/
Graduate School of Life and Environmental Sciences
University of Tsukuba, Japan
Mobile: +81 70 3846 2993
Email : [email protected]
<mailto:[email protected]>
[email protected] <mailto:[email protected]>
[email protected] <mailto:[email protected]>
*----
*
/Mapping Technician/
Department of Surveying and Mapping Vietnam
No 2, Dang Thuy Tram street, Hanoi, Viet Nam
Mobile: +84 1255151344
Email : [email protected] <mailto:[email protected]>
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