koyomitan3
commented
3 months ago You can use a some snippet from the examples (from the README.md in case) the official repo https://github.com/HigherOrderCO/Bend?tab=readme-ov-file#getting-started
def Sum(start, target):
if start == target:
return start
else:
return start + Sum(start + 1, target)
def main():
return Sum(1, 1_000_000)Run the code above with bend run-cu file.py
And then you can compare that to a similar example in python using numba (or pycuda and JAX works as well). I am not sure however if python cuda is fair comparison, but you it's uh... something? I hope @VictorTaelin does not punish me on this because I have no idea how many threads per block and blocks per grid bend uses. So adjust whatever below to make it a fair comparison
import numpy as np
from numba import cuda
import time
@cuda.jit
def parallel_sum(arr, result):
idx = cuda.grid(1)
if idx < arr.size:
cuda.atomic.add(result, 0, arr[idx])
def main():
N = 1_000_000
arr = np.arange(1, N + 1, dtype=np.int32)
result = np.zeros(1, dtype=np.int32)
threads_per_block = 256
blocks_per_grid = (arr.size + (threads_per_block - 1)) // threads_per_block
start_time = time.time()
parallel_sum[blocks_per_grid, threads_per_block](arr, result)
cuda.synchronize()
end_time = time.time()
print(f"Sum result: {result[0]}")
print(f"Time taken: {end_time - start_time} seconds")
if __name__ == "__main__":
main()
deadsoul44
developedby
PredragN
Hello, thanks for the great work.
Speed-up between CPU and GPU is obviously great but what about speed difference between CUDA and Bend for the same algorithm? A comparison using a tensor operation like a neural network library can be great.