parallelism-amdahl

I'm wondering, which is better to use with GridSearchCV( ..., n_jobs = ... ) to pick the best parameter set for a model, n_jobs = -1 or n_jobs with a big number, like n_jobs = 30 ? Based on Sklearn documentation: n_jobs = -1 means that the computation will be dispatched on all the CPUs of the computer. On my PC I have an Intel i3 CPU, which has 2 cores and 4 threads, so does that mean if I set n_jobs = -1 , implicitly it will be equal to n_jobs = 2 ? user3666197 ... does that mean if I set n_jobs = -1 , implicitly it will be equal to n_jobs = 2 ? This one is easy : python ( scipy / joblib

I am attempting to implement multiprocessing in Python (Windows Server 2012) and am having trouble achieving the degree of performance improvement that I expect. In particular, for a set of tasks which are almost entirely independent, I would expect a linear improvement with additional cores . I understand that--especially on Windows--there is overhead involved in opening new processes [1] , and that many quirks of the underlying code can get in the way of a clean trend. But in theory the trend should ultimately still be close to linear for a fully parallelized task [2] ; or perhaps logistic

I want to use more processors to run my code to minimize the running time only. Though I have tried to do it but failed to get the desired result. My code is a very big one that's why I'm giving here a very small and simple code (though it does not need parallel job to run this code) just to know how can I do parallel job in python. Any comments/ suggestions will be highly appreciated. import numpy as np import matplotlib.pyplot as plt from scipy.integrate import odeint def solveit(n,y0): def exam(y, x): theta, omega = y dydx = [omega, - (2.0/x)*omega - theta**n] return dydx x = np.linspace(0

import multiprocessing as mp import numpy as np pool = mp.Pool( processes = 4 ) inp = np.linspace( 0.01, 1.99, 100 ) result = pool.map_async( func, inp ) #Line1 ( func is some Python function which acts on input ) output = result.get() #Line2 So, I was trying to parallelize some code in Python, using a .map_async() method on a multiprocessing.Pool() instance. I noticed that while Line1 takes around a thousandth of a second, Line2 takes about .3 seconds. Is there a better way to do this or a way to get around the bottleneck caused by Line2 , or am I doing something wrong here? ( I am rather new

I was trying out the Python multiprocessing module. In the code below the serial execution time 0.09 seconds and the parallel execution time is 0.2 seconds. As I am getting no speedup, I think I might be going wrong somewhere import multiprocessing as mp from random import uniform, randrange import time # m = mp.Manager() out_queue = mp.Queue() def flop_no(rand_nos, a, b): cals = [] for r in rand_nos: cals.append(r + a * b) return cals def flop(val, a, b, out_queue): cals = [] for v in val: cals.append(v + a * b) # print cals out_queue.put(cals) # print "Exec over" def concurrency(): # out