问题描述
为了改进我的有一个重循环的代码,我需要加快速度.如何为这样的代码实现多处理?(a 是典型的大小 2 和 l 到 10)
To improve my code which has one heavy loop I need a speed up. How can I implement multiprocessing for a code like this? (a is typical of size 2 and l up to 10)
for x1 in range(a**l):
for x2 in range(a**l):
for x3 in range(a**l):
output[x1,x2,x3] = HeavyComputationThatIsThreadSafe1(x1,x2,x3)
推荐答案
如果 HeavyComputationThatIsThreadSafe1
函数只使用数组而不使用 python 对象,我会使用 并发期货(或 python2 backport) ThreadPoolExecutor
以及 Numba (或 cython) 发布 GIL.否则使用 ProcessPoolExecutor
.
If the HeavyComputationThatIsThreadSafe1
function only uses arrays and not python objects, I would using a concurrent futures (or the python2 backport) ThreadPoolExecutor
along with Numba (or cython) with the GIL released. Otherwise use a ProcessPoolExecutor
.
见:
http://numba.pydata.org/numba-doc/latest/user/examples.html#multi-threading
您希望在最外层循环的级别并行化计算,然后从每个线程/进程产生的块中填充 output
.这假设这样做的成本比计算便宜得多,应该是这种情况.
You'd want to parallelize the calculation at the level of the outermost loop and and then fill output
from the chunks resulting from each thread/process. This assumes the cost of doing so is much cheaper than the computation, which should be the case.
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