Testing the excecution with and with out GPU and CUDA in Jetson TX2

Hello everyone,
i’m trying to investigate the difference in Performance between running a code with CUDA on GPU and with out it. So I wrote the following program:

from numba import jit, cuda 
import numpy as np 
# to measure exec time 
from timeit import default_timer as timer    
# normal function to run on cpu 
def func(a):                                 
    for i in range(10000000): 
        a[i]+= 1      
# function optimized to run on gpu  
@jit(target ="cuda")                          
def func2(a): 
    for i in range(10000000): 
        a[i]+= 1
if __name__=="__main__": 
    n = 10000000                            
    a = np.ones(n, dtype = np.float64) 
    b = np.ones(n, dtype = np.float32) 
    start = timer() 
    print("without GPU:", timer()-start)     
    start = timer() 
    print("with GPU:", timer()-start)

func(a) seems to works properly but func2(a) which Python mit CUDA doesn’t work and return the program the following error:

without GPU: 7.720662347999678
/usr/local/lib/python3.6/dist-packages/numba/cuda/decorators.py:116: UserWarning: autojit is deprecated and will be removed in a future release. Use jit instead.
warn(‘autojit is deprecated and will be removed in a future release. Use jit instead.’)
Traceback (most recent call last):
File “GPU vs CPU.py”, line 26, in
File “/usr/local/lib/python3.6/dist-packages/numba/cuda/dispatcher.py”, line 42, in call
return self.compiled(*args, **kws)
File “/usr/local/lib/python3.6/dist-packages/numba/cuda/dispatcher.py”, line 38, in compiled
self._compiled = autojit(self.py_func, **self.targetoptions)
File “/usr/local/lib/python3.6/dist-packages/numba/cuda/decorators.py”, line 117, in autojit
return jit(*args, **kwargs)
File “/usr/local/lib/python3.6/dist-packages/numba/cuda/decorators.py”, line 56, in jit
raise NotImplementedError(“bounds checking is not supported for CUDA”)
NotImplementedError: bounds checking is not supported for CUDA

I’m not able to interpret the error so I would be thankful if you could help.

Thanks in advance



Let me give it a try.
I’m installing numba right now.



Sorry for the late.

We can reproduce this issue on our environment.
It looks like that the bounds checking is disable in CUDA by this issue:

It’s recommended to check with the numba team to see this issue is fixed or not first.

okay thanks