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
University of Virginia
On Wed, Aug 8, 2018 at 8:50 PM, hoang trung Ta <tahoangtr...@gmail.com>
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 : ta.hoang-trung...@alumni.tsukuba.ac.jp
> tahoangtr...@gmail.com
> s1626...@u.tsukuba.ac.jp
>
> *----*
> *Mapping Technician*
> Department of Surveying and Mapping Vietnam
> No 2, Dang Thuy Tram street, Hanoi, Viet Nam
>
> Mobile: +84 1255151344
> Email : tahoangtr...@gmail.com
>
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