如何使用Dask在GPU上运行python代码? [英] How to use Dask to run python code on the GPU?
问题描述
我有一些使用Numba cuda.jit的代码，以便我可以在gpu上运行，如果可能的话，我想在其上分层.
I have some code that uses Numba cuda.jit in order for me to run on the gpu, and I would like to layer dask on top of it if possible.
示例代码
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from numba import cuda, njit
import numpy as np
from dask.distributed import Client, LocalCluster
@cuda.jit()
def addingNumbersCUDA (big_array, big_array2, save_array):
i = cuda.grid(1)
if i < big_array.shape[0]:
for j in range (big_array.shape[1]):
save_array[i][j] = big_array[i][j] * big_array2[i][j]
if __name__ == "__main__":
cluster = LocalCluster()
client = Client(cluster)
big_array = np.random.random_sample((100, 3000))
big_array2 = np.random.random_sample((100, 3000))
save_array = np.zeros(shape=(100, 3000))
arraysize = 100
threadsperblock = 64
blockspergrid = (arraysize + (threadsperblock - 1))
d_big_array = cuda.to_device(big_array)
d_big_array2 = cuda.to_device(big_array2)
d_save_array = cuda.to_device(save_array)
addingNumbersCUDA[blockspergrid, threadsperblock](d_big_array, d_big_array2, d_save_array)
save_array = d_save_array.copy_to_host()
如果我的函数addingNumbersCUDA不使用任何CUDA，我只需将client.submit放在我的函数前面(以及在集合之后)，它将起作用.但是，由于我使用的是CUDA，因此无法将提交放在函数的前面.简短的文档说，您可以针对gpu，但是尚不清楚如何在实践中进行实际设置.我该如何设置我的函数以在目标gpu和cuda.jit上使用dask?
If my function addingNumbersCUDA didn't use any CUDA I would just put client.submit in front of my function (along with gather after) and it would work. But, since I'm using CUDA putting submit in front of the function doesn't work. The dask documentation says that you can target the gpu, but it's unclear as to how to actually set it up in practice. How would I set up my function to use dask with the gpu targeted and with cuda.jit if possible?
