A package for easy task parallelization across CPU threads
mpyll is a package for easy task parallelization across CPU threads.
pip install mpyll
mpyll logic is as follows:
- Identify the data on which to parallelize computation. The data should be stored in a list.
- Define the task: a python function that takes as input a list of data elements and performs the desired task. This is the parallelized task; instances of this function are to be running in CPU threads.
- Define an eventual post processing function that takes as input a list of data and returns the final result, if any.
Let's take as an example the estimation of Pi through Monte Carlo:
import numpy as np from mpyll import parallelize # First, we define the data on which we would like to parallelize computation. r = 1. m = 10 ** 6 X = np.random.uniform(-r, r, size = m) Y = np.random.uniform(-r, r, size = m) data = [(X[i], Y[i]) for i in range(m)] # Second, we define the task to be parallelized. # It takes as input the data (a list) as well as other arguments, if any, # and it returns a result. If it is a procedure, then it does not return. def count_in_circle_points(data, r, m): a = np.array(data) # matrix, each row contains a point coordinates d = np.sqrt(np.sum(a ** 2, axis = 1)) # distance to the origin in_circle = d <= r # an array, True if distance <= radius, False otherwise return np.sum(in_circle) # Finally, we define a post processor. def estimate_pi(data, m): pi_estimation = 4 * np.sum(data) / m return pi_estimation pi_estimation = parallelize(task = count_in_circle_points, data = data, data_shuffle = False, post_processor = estimate_pi, n_jobs = -1, # task arguments count_in_circle_points_r = r, count_in_circle_points_m = m, # post processor arguments estimate_pi_m = m)
parallelize(task, data, shuffle_data = False, post_processor = None, n_jobs = -1, *args, **kwargs) Parallelize a task that returns a value Parameters ---------- task: function The task to be parallelized. data: list The data on which the parallelization is performed. shuffle_data: boolean shuffle data before processing. Sometimes the data are not identically distributed, which could cause some threads to be overloaded compared to others. post_processor: function A function that runs after all threads terminate. n_jobs: int The number of threads to be used. Specify -1 to use all CPU threads. Other Parameters ---------------- Other parameters could be passed to `task` and `post_processor`. The argument name should start with the name of the task or the post processor, followed by an underscore and the name of the argument. Returns ------- If a post processor is specified, then this function returns what is returned by the post processor, otherwise, it returns a list of the objects returned by each thread.
GNU General Public License v3
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