Hello, So I've looked at the sparse stuff from Git, and the SparseSolve.py examples, and I can see how to build a sparse matrix and a dense vector, and how to use the CG stuff, solving A * x = b for x. What I can't seem to figure out is how to do a basic sparse-matrix-by-dense-vector multiply, and solve A * x = b for b. I'm assuming the CG method requires this at some point, and I just can't see where it happens? Is get_elwise_kernel doing some sort of magic in order to return a kernel that works on sparse matrices and dense vectors?
How can I multiply a sparse packeted or coordinate-format matrix by a GPUArray vector on the GPU? Thanks, -Adam On Fri, Sep 17, 2010 at 8:24 AM, Andreas Kloeckner <li...@informa.tiker.net> wrote: > On Thu, 16 Sep 2010 15:59:29 -0700, Adam N <interf...@gmail.com> wrote: >> The PyCuda documentation < >> http://documen.tician.de/pycuda/misc.html#version-0-94> lists a sparse >> matrix library, pycuda.sparse, as being in version 0.94. It isn't in 0.94rc, >> and there's no other documentation for it. Does it exist, and, if so, how >> can I use it? > > 0.94rc is (by now) unfortunately very old. I'll try to release 0.94 with > support for 3.2rc during/before(?) GTC. In the meantime, you can get the > sparse matrix support from the git tree. > > HTH, > Andreas > _______________________________________________ PyCUDA mailing list PyCUDA@tiker.net http://lists.tiker.net/listinfo/pycuda
