I have a numpy array of coordinates of size n_slice x 2048 x 3, where n_slice is in the tens of thousands. I want to apply the following operation on each 2048 x 3 slice separately
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])
I tried using Dask by making coor
a dask.array
,
import dask.array as da
dcoor = da.from_array(coor, chunks=(1, 2048, 3))
but simply replacing coor
by dcoor
will not expose the parallelism. I could see setting up parallel threads to run for each slice but how do I leverage Dask to handle the parallelism?
Here is the parallel implementation using concurrent.futures
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)
It is possible this problem is not well suited to Dask since there are no inter-chunk computations.
map_blocks
The map_blocks method may be helpful:
dcoor.map_blocks(pdist)
It looks like you're doing a bit of fancy slicing to insert particular values into particular locations of an output array. This will probably be awkward to do with dask.arrays. Instead, I recommend making a function that produces a numpy array
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
Worst case scenario you can always use 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)
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