There's an example here for addition which will look very similar to multiplication: http://deeplearning.net/software/theano/tutorial/adding.html#adding-two-matrices
On Thursday, October 27, 2016 at 10:42:38 AM UTC-7, Jesse Livezey wrote: > > It will look similar to creating two numpy arrays and multiplying them > elementwise, except it will perform the multiplications in parallel on the > gpu. > > On Thursday, October 27, 2016 at 10:41:33 AM UTC-7, Jesse Livezey wrote: >> >> If you can create a theano vector that has all of the i's and a second >> theano vector that has all of the j's, then you can just do i*j and will >> will perform all of the multiplications in parallel. >> >> On Wednesday, October 26, 2016 at 11:48:06 PM UTC-7, [email protected] >> wrote: >>> >>> I would like to compute the result of i*j for a number of i's and j's, >>> and I would like to do so concurrently. If I use the scan function over my >>> sequence of i's and j's, I will get my desired result, but it will not >>> perform the operations concurrently. If I have 100 cores in my single GPU, >>> I would like there to be 100 asynchronous computations (technically more >>> since each core has multiple threads) of the multiplication and final >>> assignment to one vector that will be returned. This is similar to how >>> multiprocessing works in base python with CPU cores. The Theano tutorial >>> claims that it uses GPU asynchronous capabilities, but I am not sure of >>> that as I have ran scan functions, and they seems to go as fast or slower >>> than the CPU. >>> >>> Should I not use scan? Can this even be done in Theano? Do I have to use >>> PyCUDA? >>> >> -- --- You received this message because you are subscribed to the Google Groups "theano-users" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. For more options, visit https://groups.google.com/d/optout.
