Hi, > Indeed. In the future, if OpenCL is the way to go, it may even be > helpful to have Numpy using OpenCL directly, as AMD provides an SDK > for OpenCL, and with Larrabee approaching, Intel will surely provide > one of its own.
I was just in a lecture by one of the Intel people about OpenCL: http://parlab.eecs.berkeley.edu/bootcampagenda http://parlab.eecs.berkeley.edu/sites/all/parlab/files/OpenCL_Mattson.pdf He offered no schedule for an Intel OpenCL implementation, but said that they were committed to it. The lectures in general were effective in pointing out what a time-consuming effort it can be moving algorithms into the the parallel world - including GPUs. The lecture just passed cited the example of a CUDA-based BLAS implementation on the GPU that was slower than the CPU version. Making BLAS go faster required a lot of work to find optimal strategies for blocking, transfer between CPU / GPU shared memory / GPU registers, vector sizes and so on - this on a specific NVIDIA architecture. I can imagine Numpy being useful for scripting in this C-and-assembler-centric world, making it easier to write automated testers, or even generate C code. Is anyone out there working on this kind of stuff? I ask only because there seems to be considerable interest here on the Berkeley campus. Best, Matthew _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion