Hi, I did some work six months ago in porting the whitepaper to OpenCL. The WIP code is available here: https://github.com/Jontte/CortiCL
CLA doesn't port to GPU as well as deep neural networks due to some parts of the algorithm needing memory access to the neighbouring columns (neighbourhood inhibition in SP) and the need to maintain variable sized memory buffers (list of proposed changes to a segment), but it all can be worked around. CPU<->GPU memory bandwidth shouldn't be a problem, since the state of the whole network doesn't have to be accessible from the CPU side during operation unless one wants debug diagnostics. In my application I only move the network input/output bit patterns across the boundary each step: https://github.com/Jontte/CortiCL/blob/master/src/cltemporal.cpp#L83 Joonas Haapala 2014-05-05 8:28 GMT+03:00 Sergey Bryukov <[email protected]>: > Hi, is there any progress for CLA on GPU? > > There is a mention of GPU data base. Dont know if it would be useful for > CLA. > > "Known as MapD <http://geops.csail.mit.edu/docs/mapd_overview.pdf>, or > massively parallel database, the new technology achieves big speed gains by > storing the data in the onboard memory of graphics processing units (GPUs) > instead of in central processing units (CPUs), as is conventional." > > > http://www.technologyreview.com/news/520021/graphics-chips-help-process-big-data-sets-in-milliseconds/ > > _______________________________________________ > nupic mailing list > [email protected] > http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org > >
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