In general, large scale machine learning is I/O bound already. There are some things that would not be, but to really feed a GPU reasonably, data almost has to be memory resident.
For more information on CUDA from Java, see (among others) http://www.jcuda.de/ On Sun, Jul 8, 2012 at 4:04 PM, Sean Owen <[email protected]> wrote: > More than that, Mahout is mostly Hadoop-based, which is well up the > stack from Java. No there is nothing CUDA-related in the project. The > closest thing are the pure Java non-Hadoop-based recommender pieces. > But it is still far from CUDA. > > I think CUDA is intriguing since a lot of ML is a bunch of matrix math > and GPUs are very good at vectorized math. I think a first step is to > introduce proper JNI bindings for the big matrix math jobs and see how > much that gains. If it's a lot, then CUDA-izing the JNI pieces is an > interesting next step. > > On Sun, Jul 8, 2012 at 11:41 PM, mohsen jadidi <[email protected]> > wrote: > > Hello , > > > > This is my first post here and I just started reading about Hadoop, > Mahout > > and all. I was wondering if there is any solution to use Mahout on > parallel > > computing on GPU (mainly CUDA) ? I know it's a bit wired question to ask > > because cud a is C base and Mahout is Java base , but I just ask it as > > curiosity! I think it would be a very cool combination to use both > cluster > > and local parallelisation ! > > > > cheers, > > -- > > Mohsen >
