我如何使用Dask在NumPy數組的片上執行並行操作？

Question

我有一個大小為n_slice x 2048 x 3的numpy坐標數組，其中n_slice是數萬個。 我想分別在每個2048 x 3切片上應用以下操作

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])

我試着用DASK通過使coor一個dask.array ，

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

但是簡單地用dcoor替換coor不會暴露並行性。 我可以看到設置並行線程為每個切片運行但是如何利用Dask來處理並行性？

這是使用concurrent.futures的並行實現

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)

這個問題可能不適合Dask，因為沒有塊間計算。

Answer 1

map_blocks

map_blocks方法可能會有所幫助：

dcoor.map_blocks(pdist)

不均勻的陣列

看起來你正在做一些花哨的切片，將特定值插入到輸出數組的特定位置。 這可能與dask.arrays有些尷尬。 相反，我建議制作一個產生numpy數組的函數

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)

delayed

在最糟糕的情況下，您始終可以使用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)