BUG: Numba using a lot of GPU memory

Numba Development
1

Hi everyone. Probably I found a bug in Numba. I created a reproducable demo. It should allocate only 256KB of memory but allocates more than 2GB. Can someone take a look?

import numba as nb
from numba import cuda
from time import sleep

######################

@cuda.jit("void(int32[:])", device=True)
def GPU_device_function(arr):
    return

@cuda.jit("void()")
def GPU_entry_point():
    # When this if is removed, it works normally
    if cuda.grid(1):
        return

    # Should use only 256 KB of memory.
    arr = cuda.local.array(shape=65536, dtype=nb.int32)

    # When this assigment is removed, it works normally
    arr[0] = 0

    # When this call is removed, it works normally
    GPU_device_function(arr)

######################

if __name__ == '__main__':
    print(cuda.select_device(0))
    print("LOADED")

    GPU_entry_point[1, 1]()  # Run  once
    cuda.synchronize()

    print("DONE")
    sleep(3)  # Wait, so the memory spike will show up in Task manager before deallocation
    print("END")
2

Thanks for the report! I can reproduce the behaviour, except with a Quadro RTX 8000 it uses 18GB of RAM! :slight_smile:

I’ve written it up on the issue tracker, and will report further progress there: https://github.com/numba/numba/issues/6352