embarrassingly-parallel

How to parallelize this piece of code?

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问题 I've been browsing for some time but couldn't find any constructive answer that I could comprehend. How should I paralellize the following code: import random import math import numpy as np import sys import multiprocessing boot = 20#number of iterations to be performed def myscript(iteration_number): #stuff that the code actually does def main(unused_command_line_args): for i in xrange(boot): myscript(i) return 0 if __name__ == '__main__': sys.exit(main(sys.argv)) or where can I read about

Parallelize pandas apply

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问题 New to pandas, I already want to parallelize a row-wise apply operation. So far I found Parallelize apply after pandas groupby However, that only seems to work for grouped data frames. My use case is different: I have a list of holidays and for my current row/date want to find the no-of-days before and after this day to the next holiday. This is the function I call via apply: def get_nearest_holiday(x, pivot): nearestHoliday = min(x, key=lambda x: abs(x- pivot)) difference = abs(nearesHoliday

OpenMP with matrices and vectors

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问题 What is the best way to utilize OpenMP with a matrix-vector product? Would the for directive suffice (if so, where should I place it? I assume outer loop would be more efficient) or would I need schedule, etc..? Also, how would I take advantage different algorithms to attempt this m-v product most efficiently? Thanks 回答1: The first step you should take is the obvious one, wrap the outermost loop in a parallel for directive. As you assume. It's always worth experimenting a bit to get some

Parallelize pandas apply

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New to pandas, I already want to parallelize a row-wise apply operation. So far I found Parallelize apply after pandas groupby However, that only seems to work for grouped data frames. My use case is different: I have a list of holidays and for my current row/date want to find the no-of-days before and after this day to the next holiday. This is the function I call via apply: def get_nearest_holiday(x, pivot): nearestHoliday = min(x, key=lambda x: abs(x- pivot)) difference = abs(nearesHoliday - pivot) return difference / np.timedelta64(1, 'D') How can I speed it up? edit I experimented a bit

Solving embarassingly parallel problems using Python multiprocessing

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问题 How does one use multiprocessing to tackle embarrassingly parallel problems? Embarassingly parallel problems typically consist of three basic parts: Read input data (from a file, database, tcp connection, etc.). Run calculations on the input data, where each calculation is independent of any other calculation . Write results of calculations (to a file, database, tcp connection, etc.). We can parallelize the program in two dimensions: Part 2 can run on multiple cores, since each calculation is