Just saw this blog post come by and thought it was relevant to the discussion here:<br><br><a href="http://ianozsvald.com/2010/07/14/22937-faster-python-math-using-pycuda/">http://ianozsvald.com/2010/07/14/22937-faster-python-math-using-pycuda/</a><br>
<br>Steve<br><br><div class="gmail_quote">On Wed, Jul 14, 2010 at 9:34 AM, Andy Wiggin <span dir="ltr"><<a href="mailto:andywiggin@gmail.com">andywiggin@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
<div class="im">On Tue, Jul 13, 2010 at 6:14 PM, Seth Friedman <<a href="mailto:sfseth@gmail.com">sfseth@gmail.com</a>> wrote:<br>
> This is really neat. I've got a CUDA-capable nvidia card I'm thinking I<br>
> want to write a python program to answer the following question: what/where<br>
> is the effect of the GIL when programming 240/480/720 1.3ghz cores? It<br>
> seems like this question must have been conquered to some degree, at<br>
> least. Is this simply a non issue because there isn't any python<br>
> "interpreting" happening in parallel, that it's translated in serial<br>
> (assuming single-threaded/single-process python doing the farming to the<br>
> GPUs) ..?<br>
><br>
> seth<br>
><br>
<br>
</div>Seth,<br>
it doesn't look to me like any python code is actually executing on<br>
the GPU. The "kernels" of code running on the GPU appear to still be<br>
written in C (notice the C-format string literals when creating<br>
SourceModule objects in the examples; that's what runs on the GPU). So<br>
unfortunately I don't think you can do the experiment you're<br>
describing.<br>
Regards,<br>
<font color="#888888">Andy<br>
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