Hi, Andrey This is a very good question and a bit tricky one.
To start with, one can argue that you already can use GPU in Ignite ML. This is because we support BLAS via netlib (IGNITE-5278) and netlib, in turn, can be configured to use NVBLAS, as explained in this library documentation: https://github.com/fommil/netlib-java/wiki/NVBLAS However after you dig deeper you'll see that efficient support for GPU is more complicated, specifically because of overhead involved in data transfers between CPU and GPU memory. And this is much more complicated and effort consuming because we need to design things to avoid redundant data transfers which is likely to involve changes in a lot of ML Grid code. Tentatively we plan to integrate JCuda library which exposes a fairly straightforward API to control CPU-GPU data transfers (via JCuda.cudaMalloc and JCuda.cudaFree). This is sort of design challenge though because we would like to have it transparent for users who don't have CUDA. We're currently working on transition to Dataset API which possibly can make it easier (IGNITE-8059 and other tickets). After this is completed I would like to revisit the matters of GPU integration. Best regards, Yury -- Sent from: http://apache-ignite-developers.2346864.n4.nabble.com/
