python gpu programming example code

import numpy as np

from timeit import default_timer as timer

from numba import vectorize

# This should be a substantially high value. On my test machine, this took

# 33 seconds to run via the CPU and just over 3 seconds on the GPU.

NUM_ELEMENTS = 100000000

# This is the CPU version.

def vector_add_cpu(a, b):

c = np.zeros(NUM_ELEMENTS, dtype=np.float32)

for i in range(NUM_ELEMENTS):

c[i] = a[i] + b[i]

return c

# This is the GPU version. Note the @vectorize decorator. This tells

# numba to turn this into a GPU vectorized function.

@vectorize(["float32(float32, float32)"], target='cuda')

def vector_add_gpu(a, b):

return a + b;

def main():

a_source = np.ones(NUM_ELEMENTS, dtype=np.float32)

b_source = np.ones(NUM_ELEMENTS, dtype=np.float32)

# Time the CPU function

start = timer()

vector_add_cpu(a_source, b_source)

vector_add_cpu_time = timer() - start

# Time the GPU function

start = timer()

vector_add_gpu(a_source, b_source)

vector_add_gpu_time = timer() - start

# Report times

print("CPU function took %f seconds." % vector_add_cpu_time)

print("GPU function took %f seconds." % vector_add_gpu_time)

return 0

if __name__ == "__main__":

main()

gpu-example.py

link : https://linuxhint.com/gpu-programming-python/

result