On Fri, 7 Oct 2011 17:03:21 +0200, Nick Rayrider <nick.rayri...@googlemail.com> wrote: > On Fri, Oct 7, 2011 at 4:00 PM, Andreas Kloeckner > <li...@informa.tiker.net> wrote: > > On Fri, 7 Oct 2011 15:38:59 +0200, Nick Rayrider > > <nick.rayri...@googlemail.com> wrote: > >> On Thu, Oct 6, 2011 at 2:47 PM, Andreas Kloeckner > >> <li...@informa.tiker.net> wrote: > >> > On Thu, 6 Oct 2011 12:25:12 +0200, Nick Rayrider > >> > <nick.rayri...@googlemail.com> wrote: > >> >> Hi, > >> >> > >> >> first thanks for this fine piece of software. > >> >> > >> >> Optimizing my kernels, the nvidia's visual profiler recommended, that > >> >> I should use more pinned memory. I read the PyCUDA documentation [1] > >> >> and tried to understand the sparse solve example [3], but I could not > >> >> make out how to turn an existing numpy array into pinned memory. I did > >> >> not find further examples of PageLockedMemoryPool [2]. > >> > > >> > pool = PageLockedMemoryPool() > >> > empty_pinned_array = pool.allocate((300, 300), np.float64) > >> > > >> > Now empty_pinned_array is backed by pinned storage, and when you memcpy > >> > to/from it, it'll go faster. > >> > >> Thanks for the fast answer. I read that you shouldn't use in() as it > >> performs a copy [1], so I tried following version to no avail. > >> What am I missing? Problably something about how numnpy and pycuda > >> handle pointers... > >> > >> from pycuda.tools import PageLockedMemoryPool > >> pool = PageLockedMemoryPool() > >> empty_pinned_array = pool.allocate(data.shape,np.float32) > >> empty_pinned_array = gpuarray.to_gpu(data) > >> my_kernel(empty_pinned_array,... ) > >> > >> [1] http://lists.tiker.net/pipermail/pycuda/2009-August/001784.html > > > > If you'd like to pass a GPUArray to a kernel, you need to pass > > empty_pinned_array.gpudata. > > I think I am totally on the wrong path. I attached what I have so far. > The example compiles and 'calculates' the correct results, but the > visual profiler still says that the "host mem transfer" is still > pageable.
The transfer off the device behind the 'get' is still pageable, the transfer in shouldn't be. Andreas
pgpDi66pmbguu.pgp
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