I've updated the mandelbrot.py demo: http://wiki.tiker.net/PyCuda/Examples/Mandelbrot which runs against the latest git, cheers for accepting the patches.
There's a (slightly linkbaity!) post on my blog with timing info for a 9800GT and GTX 480: "22,937* faster Python math using pyCUDA" http://ianozsvald.com/2010/07/14/22937-faster-python-math-using-pycuda/ One question that's raised from my post - how come double precision CPU math is faster than single precision CPU math? I hadn't expected that result to drop out of the test and it has been a while since I did any good speed tests on CPUs. Is it generally the case that double precision math (both in C and Python?) on x86 is faster than single precision now? i. On 9 July 2010 22:11, Andreas Kloeckner <[email protected]> wrote: > On Tue, 29 Jun 2010 16:44:18 +0100, Ian Ozsvald <[email protected]> wrote: >> Andreas, I'm attaching two patches. >> >> 0001 removes the #warning lines in cuda.hpp that make msvc (2008 on WinxP) >> fail. >> >> 0002 adds GPUArray comparisons for == != < > <= >= > > Merged, thanks! > >> Assuming you're cool with the patches I can contribute an updated >> Mandelbrot.py where a reasonable speed-up is obtained using pure >> Python/GPUArray(numpy-like) operators rather than having to implement >> a pure .cu solution. This GPUArray solution sits between a numpy (CPU) >> speed-up and the pure .cu code version. It'll make for a good demo for >> pure-Python folk (like my boss). > > Sure, I'd be interested in seeing that. > > Andreas > > -- Ian Ozsvald (A.I. researcher, screencaster) [email protected] http://IanOzsvald.com http://MorConsulting.com/ http://blog.AICookbook.com/ http://TheScreencastingHandbook.com http://FivePoundApp.com/ http://twitter.com/IanOzsvald _______________________________________________ PyCUDA mailing list [email protected] http://lists.tiker.net/listinfo/pycuda
