That was it! I changed my code to carry the context etc around in an object to be reused each time... not it's much better. No need to inspect my crazy code...
I do see that my cpu core with this process is at 100% while this code is running. Does wait() do something that takes cpu cycles? I had hoped that with the GPU busy computing, I'd have most of my cpu left for other stuff. thanks! -steve On Jan 27, 2012, at 9:05 AM, Andreas Kloeckner <[email protected]> wrote: > On Fri, 27 Jan 2012 08:59:04 -0700, Steve Spicklemire <[email protected]> wrote: >> Two more quick points... >> >> If I let the code keep running on the ION2 system I get this: >> >> <http://www.spvi.com/files/bccd-out-9.txt> >> >> And... if I set the environment variable to show compiler output on the ION2 >> system.. I see this. >> >> <http://www.spvi.com/files/bccd-compiler-output-9.txt> >> >> I'm struggling to interpret what that all means. ;-) >> >> Any hints appreciated. > > I'll look through your code/results once I have more time, but these two > things I can answer off the bat: > >> BTW... is there a 'release' memory method needed when using pyopencl? > > No, *if* you're ok with waiting for the point in time when the Python GC > decides it's time to get rid of the host-side object managing the CL > allocation. If you're very close to resource limits, you can use > > http://documen.tician.de/pyopencl/runtime.html#pyopencl.MemoryObject.release > >> Do I need to create my context/queue only once and pass it around to >> be reused all the time? > > Definitely. Context creation (especially on Nv) is expensive, *and* > there's a fairly low limit on the number of concurrent contexts. > > Andreas > _______________________________________________ PyOpenCL mailing list [email protected] http://lists.tiker.net/listinfo/pyopencl
