thank you, that solved all my problems ! ( I mean.... that one problem, which was pervasive in my program ) On Thu, Apr 22, 2010 at 4:25 PM, Andreas Klöckner <lists(a)informa.tiker.net&gt; wrote: > On Donnerstag 22 April 2010, Michael Rule wrote: >> NV Tesla T10, >> I assume its something I am doing wrong... > > If you're using NV hardware, it helps to know the CUDA literature. You > must supply a local_size to partition your work into NV's thread > blocks. If you don't you're only submitting one thread block, which has > hardware size limits. > > Andreas > > PS: Please keep replies on the list. Thanks. > _______________________________________________ PyOpenCL mailing list PyOpenCL(a)host304.hostmonster.com http://host304.hostmonster.com/mailman/listinfo/pyopencl_tiker.net

On Donnerstag 22 April 2010, David Garcia wrote: > The hardware-independent way to solve this is to > call clGetKernelWorkGroupInfo() with an argument > of CL_KERNEL_WORK_GROUP_SIZE to get the maximum number of work-items that > you can enqueue at once. > > See also CL_KERNEL_LOCAL_MEM_SIZE to find out how much local memory will be > available for that kernel. > > Andreas, I assume that this is also exposed through PyOpenCL somehow :) http://documen.tician.de/pyopencl/reference.html#pyopencl.Kernel.get_work... Andreas _______________________________________________ PyOpenCL mailing list PyOpenCL(a)host304.hostmonster.com http://host304.hostmonster.com/mailman/listinfo/pyopencl_tiker.net