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
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