On Thu, Aug 6, 2009 at 5:10 PM, Sturla Molden <stu...@molden.no> wrote:
> Charles R Harris wrote: > > > I mean, once the computations are moved elsewhere numpy is basically a > > convenient way to address memory. > > That is how I mostly use NumPy, though. Computations I often do in > Fortran 95 or C. > > NumPy arrays on the GPU memory is an easy task. Glad to hear it. So maybe some way to specify and track where the memory is allocated would be helpful. Travis wants to add a dictionary to ndarrays and that might be useful here. But then I would have to > write the computation in OpenCL's dialect of C99? But I'd rather program > everything in Python if I could. Details like GPU and OpenCL should be > hidden away. Nice looking Python with NumPy is much easier to read and > write. That is why I'd like to see a code generator (i.e. JIT compiler) > for NumPy. > Yes, but that is a language/compiler problem. I'm thinking of what tools numpy can offer that would help people experimenting with different approaches to using GPUs. At some point we might want to adopt a working approach but now seems a bit early for that. Chuck
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