Re: [PyCUDA] How to manually free GPUarray to avoid leak?
On Sonntag 25 April 2010, gerald wrong wrote: Can I manually free GPUarray instances? In addition to Bogdan's comments (which are more likely to help you with what you're seeing): If you must free the memory by hand, you can use ary.gpudata.free() to do so. HTH, Andreas signature.asc Description: This is a digitally signed message part. ___ PyCUDA mailing list pyc...@host304.hostmonster.com http://host304.hostmonster.com/mailman/listinfo/pycuda_tiker.net
Re: [PyCUDA] How to manually free GPUarray to avoid leak?
I have a question about the .gpudata contract that I couldn't figure out by experimentation. If I construct gpuarrays with a call like GPUArray(...,gpudata=xxx) is it sufficient that xxx.___int___() and xxx.free() be defined for things to work out correctly? That's what I gleaned from the documentation, but it didn't work when I tried it with a Python class that had those two methods. (I wanted to allocate memory on the device some other way.) ^L On Wed, May 5, 2010 at 9:25 PM, Andreas Klöckner li...@informa.tiker.netwrote: On Sonntag 25 April 2010, gerald wrong wrote: Can I manually free GPUarray instances? In addition to Bogdan's comments (which are more likely to help you with what you're seeing): If you must free the memory by hand, you can use ary.gpudata.free() to do so. HTH, Andreas ___ PyCUDA mailing list pyc...@host304.hostmonster.com http://host304.hostmonster.com/mailman/listinfo/pycuda_tiker.net ___ PyCUDA mailing list pyc...@host304.hostmonster.com http://host304.hostmonster.com/mailman/listinfo/pycuda_tiker.net
[PyCUDA] How to manually free GPUarray to avoid leak?
I think I may be running into a memory leak using GPUarray. I have a function using GPUarrays that is working stable on single calls. If I loop this function within python from another script like this: for i in xrange(m): do_some_gpuarray_stuff() I can watch the memory pointers of the gpuarrays increase until I get a launch error... presumably due to lack of memory. ie I need gpu mem to free upon exit of do_some_gpuarray_stuff(), so I can repeat same gpu calculation many times on new data sets. Can I manually free GPUarray instances? If not, can I somehow manually remove all PyCUDA stuff from memory? like... for i in xrange(m): do_some_gpuarray_stuff() de_init_pycuda_mem I could not find this in the docs, and I understand everything is supposed to be automagically handled by PyCUDA, but manually freeing will be an easy confirmation/workaround for my problem. I know this can be done with pycuda.driver completely manually, but gpu_array is already working nicely and cleanly except for this leak. Any input from the experts would be much appreciated. Thanks much :) Garrett Wright ___ PyCUDA mailing list pyc...@host304.hostmonster.com http://host304.hostmonster.com/mailman/listinfo/pycuda_tiker.net
Re: [PyCUDA] How to manually free GPUarray to avoid leak?
Hi Gerald, I can watch the memory pointers of the gpuarrays increase until I get a launch error... presumably due to lack of memory. Are you sure that failure is caused by the lack of memory? I think, this would rather result in an error during memory allocation, not during kernel execution. Can I manually free GPUarray instances? If not, can I somehow manually remove all PyCUDA stuff from memory? Python deinitialises objects as soon as the reference count for them becomes zero. If you need to do it explicitly, I think just del gpuarray_obj will be enough. At least, it worked for me. Best regards, Bogdan On Mon, Apr 26, 2010 at 2:53 AM, gerald wrong psillymathh...@gmail.com wrote: I think I may be running into a memory leak using GPUarray. I have a function using GPUarrays that is working stable on single calls. If I loop this function within python from another script like this: for i in xrange(m): do_some_gpuarray_stuff() I can watch the memory pointers of the gpuarrays increase until I get a launch error... presumably due to lack of memory. ie I need gpu mem to free upon exit of do_some_gpuarray_stuff(), so I can repeat same gpu calculation many times on new data sets. Can I manually free GPUarray instances? If not, can I somehow manually remove all PyCUDA stuff from memory? like... for i in xrange(m): do_some_gpuarray_stuff() de_init_pycuda_mem I could not find this in the docs, and I understand everything is supposed to be automagically handled by PyCUDA, but manually freeing will be an easy confirmation/workaround for my problem. I know this can be done with pycuda.driver completely manually, but gpu_array is already working nicely and cleanly except for this leak. Any input from the experts would be much appreciated. Thanks much :) Garrett Wright ___ PyCUDA mailing list pyc...@host304.hostmonster.com http://host304.hostmonster.com/mailman/listinfo/pycuda_tiker.net ___ PyCUDA mailing list pyc...@host304.hostmonster.com http://host304.hostmonster.com/mailman/listinfo/pycuda_tiker.net
