Joonas, Really really cool and the name is perfect, well done :)
Ian On Thu, May 8, 2014 at 2:51 PM, Joonas Haapala <[email protected]> wrote: > 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 >> >> > > _______________________________________________ > nupic mailing list > [email protected] > http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org > >
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