CuPy random - how to generate new random set in same memory?
As user2357112 suggests, cupy.random.random() does not appear to support “re-randomizing“ an existing ndarray, even though cuRand does. Writing C to modify an existing cupy array somewhat defeats the point of using python / cupy in the first place.
Curiously, having an array about 1/3rd the size of available memory, while increasing the number of loops, is faster in total execution time (versus larger arrays/fewer loops).  I was not able to determine when cupy (or python or cuda?) does garbage collection on the disused array, but it seems to happen asynchronously.
If GPU garbage collection uses cuda cores (I presume it does?), it does not appear to materially effect my code execution time. Nvidia-smi reports “P2” GPU usage when my code calculations are running, suggesting there are still cores available for cupy / cuda to free memory outside of my code?
I don’t like answering my own question... just sharing what I found in case it helps someone else
I am generating a large array of random numbers, totaling more than half the available memory on a GPU. I am doing this in a loop.
When I call cupy.random the second time (or third time...), assigning to the same variable name, it does not free the memory for the first array. It tries to allocate more memory, which causes an out of memory error.
Explicitly freeing the memory before generating a new random array is very slow, and seems inefficient.
Is there a way to generate a new set of numbers, but in the same memory space?
Edit: cupy.random.shuffle() is letting me work around the problem, but I wonder if there is a better way?
Edit 2: on further review, shuffle() does not address the problem, and appears to need even more memory than allocating a second block (before freeing the first) of memory...  I am back to restricting ndarray size to less than half the remaining memory, so two ndarrays can be allocated alternately