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A helper package to easily time Numba CUDA GPU events.
As this package uses Numba, refer to the Numba compatibility guide.
Using Pip: pip3 install numba_timer
.
import math
import numpy as np
from numba import cuda
from numba_timer import cuda_timer
@cuda.jit
def increment_a_2D_array(an_array):
x, y = cuda.grid(2)
if x < an_array.shape[0] and y < an_array.shape[1]:
an_array[x, y] += 1
an_array = np.zeros((2, 100))
threadsperblock = (16, 16)
blockspergrid_x = math.ceil(an_array.shape[0] / threadsperblock[0])
blockspergrid_y = math.ceil(an_array.shape[1] / threadsperblock[1])
blockspergrid = (blockspergrid_x, blockspergrid_y)
timer = cuda_timer.Timer()
timer.start()
increment_a_2D_array[blockspergrid, threadsperblock](an_array)
timer.stop()
print(f'Elapsed time for run 1: {timer.elapsed()} ms')
timer.start()
increment_a_2D_array[blockspergrid, threadsperblock](an_array)
timer.stop()
print(f'Elapsed time for run 2: {timer.elapsed()} ms')
Numba specific code is borrowed from the Numba documentation.
FAQs
A helper package to easily time Numba CUDA GPU events
We found that numba-timer demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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