我如何使用Dask对NumPy数组切片执行并行操作？

我有一个大小为n_slice x 2048 x 3的坐标数组,其中n_slice数以万计.我想分别对每个2048 x 3切片应用以下操作

import numpy as np
from scipy.spatial.distance import pdist

# load coor from a binary xyz file, dcd format

n_slice, n_coor, _ = coor.shape
r = np.arange(n_coor)
dist = np.zeros([n_slice, n_coor, n_coor])

# this loop is what I want to parallelize, each slice is completely independent
for i in xrange(n_slice): 
    dist[i, r[:, None] < r] = pdist(coor[i])

我尝试通过使coor为dask.array来使用Dask,

import dask.array as da
dcoor = da.from_array(coor, chunks=(1, 2048, 3))

但是简单地用dcoor替换coor不会暴露出并行性.我可以看到设置并行线程以在每个片上运行,但是如何利用Dask处理并行性？

这是使用parallel.futures的并行实现

import concurrent.futures
import multiprocessing

n_cpu = multiprocessing.cpu_count()

def get_dist(coor, dist, r):
    dist[r[:, None] < r] = pdist(coor)

# load coor from a binary xyz file, dcd format

n_slice, n_coor, _ = coor.shape
r = np.arange(n_coor)
dist = np.zeros([n_slice, n_coor, n_coor])

with concurrent.futures.ThreadPoolExecutor(max_workers=n_cpu) as executor:
    for i in xrange(n_slice):
        executor.submit(get_dist, cool[i], dist[i], r)

由于没有块间计算,因此此问题可能不太适合Dask.

解决方法:

map_blocks

map_blocks方法可能会有所帮助：

dcoor.map_blocks(pdist)

数组不均匀

看起来您在做一些花哨的切片,以将特定值插入输出数组的特定位置.使用dask.arrays可能很尴尬.相反,我建议制作一个产生numpy数组的函数

def myfunc(chunk):
    values = pdist(chunk[0, :, :])
    output = np.zeroes((2048, 2048))
    r = np.arange(2048)
    output[r[:, None] < r] = values
    return output

dcoor.map_blocks(myfunc)

延迟

最坏的情况下,您可以随时使用dask.delayed

from dask import delayed, compute
coor2 = delayed(coor)
slices = [coor2[i] for i in range(coor.shape[0])]
slices2 = [delayed(pdist)(slice) for slice in slices]
results = compute(*slices2)

标签：dask,parallel-processing,arrays,python,numpy
来源： https://codeday.me/bug/20191026/1937778.html