Hi Ranga, it turns out that someone added Windows install instructions to the PyCUDA wiki a couple days ago:
http://wiki.tiker.net/PyCuda/Installation/Windows (Mac crowd, you're lagging behind... seriously... :P) HTH, Andreas On Dienstag 30 Juni 2009, Derek Anderson wrote: > i'm afraid i'm of no use there. been windows-free for going on 10 years > now. :) > > ananth ranga wrote: > > Oh thats great, thatnks alot. really appreciate it. I am trying to > > install pycuda on windows and kind of struggling with it. could ou > > please run me through it? I have VS 05 and 08 but not 03 , is that > > fine? > > > > On Tue, Jun 30, 2009 at 11:49 AM, Derek Anderson<[email protected]> wrote: > >> well, both matrices have to be squarish. but even for say > >> 100x120*120x100, i would think not. here were my performance numbers > >> when i wrote it: (includes memory transfer times) > >> > >> (4160×4160)*(4160×4160) = 43.0X faster than numpy > >> (4096×4096)*(4096×4096) = 34.0X > >> (3900×3900)*(3900×3900) = 47.3X > >> (2048×2048)*(2048×2048) = 28.2X > >> (1024×1024)*(1024×1024) = 58.8X > >> (512×512)*(512×512) = 24.1X > >> (256×256)*(256×256) = 6.3X > >> (128×128)*(128×128) = 1.1X > >> > >> CPU: Intel(R) Core(TM)2 Duo CPU E8400 @ 3.00GHz stepping 06 > >> GPU: nVidia Corporation GeForce 8800 GT (rev a2) > >> > >> but, you *might* get a modest increase (<5x) if you're keeping the > >> matrices on the card and performing the multiplications many times > >> before you pull it back to main memory. (likely, if you're doing svd :) > >> > >> derek > >> > >> ananth ranga wrote: > >>> Hey mine is also an pretty evenly sized matrix. its (120*100). So you > >>> suggesting that for this evenly sized small matrix i can expect speed > >>> up in SVD calculation? or you mean it should be a larger sized and > >>> even sized matrix to get good speed up? > >>> > >>> On Tue, Jun 30, 2009 at 11:31 AM, Derek Anderson<[email protected]> wrote: > >>>> np. yes, for more evenly sized matrices it's much faster. (for > >>>> >500^2 too) > >>>> btw if just matrix multiplication is what you're looking for, i wrote > >>>> a numpy wrapper for it a while back: > >>>> > >>>> http://kered.org/blog/2009-04-13/easy-python-numpy-cuda-cublas/ > >>>> > >>>> derek > >>>> > >>>> ananth ranga wrote: > >>>>> Thanks derek. I read some paper which suggest a speed up of upto 60 > >>>>> when the matrix size is big and almost even for size less than (500 * > >>>>> 500). > >>>>> > >>>>> On Tue, Jun 30, 2009 at 9:53 AM, Derek Anderson<[email protected]> wrote: > >>>>>> my experience with trying to cuda-ize svd/nmf calculations is that > >>>>>> they're > >>>>>> not really a good fit for cuda. specifically, most of your > >>>>>> expensive operations are matrix multiplications over very long and > >>>>>> narrow matrices. > >>>>>> (mxk or kxn), where m~=n (within an order of mag) but k<<(m|n). > >>>>>> even when > >>>>>> m~=2^16 (the max for cublas matrices) and k<2^8, i was barely > >>>>>> breaking even > >>>>>> with normal cpu-based blas libs. > >>>>>> > >>>>>> derek > >>>>>> > >>>>>> ananth ranga wrote: > >>>>>>> Hello people, > >>>>>>> > >>>>>>> I am Ranga a new member to the group. I have a problem of > >>>>>>> finding svd of a matrix of size 120*100. On a CPU with the VTK > >>>>>>> implemented version its taking about 5 ms for evaluation. So I was > >>>>>>> wondering if a pycuda version of it could give me abetter reult > >>>>>>> regarding the speed. > >>>>>>> > >>>>>>> If any one has a pycuda version of SVD calculation could you please > >>>>>>> help > >>>>>>> me out. > >>>>>>> > >>>>>>> Thanks, > >>>>>>> ranga > >>>>>>> > >>>>>>> _______________________________________________ > >>>>>>> PyCUDA mailing list > >>>>>>> [email protected] > >>>>>>> http://tiker.net/mailman/listinfo/pycuda_tiker.net > > _______________________________________________ > PyCUDA mailing list > [email protected] > http://tiker.net/mailman/listinfo/pycuda_tiker.net
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