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Python High Performance, Second Edition

You're reading from   Python High Performance, Second Edition Build high-performing, concurrent, and distributed applications

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Product type Paperback
Published in May 2017
Publisher Packt
ISBN-13 9781787282896
Length 270 pages
Edition 2nd Edition
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Author (1):
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Dr. Gabriele Lanaro Dr. Gabriele Lanaro
Author Profile Icon Dr. Gabriele Lanaro
Dr. Gabriele Lanaro
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Table of Contents (10) Chapters Close

Preface Benchmarking and Profiling FREE CHAPTER Pure Python Optimizations Fast Array Operations with NumPy and Pandas C Performance with Cython Exploring Compilers Implementing Concurrency Parallel Processing Distributed Processing Designing for High Performance

Rewriting the particle simulator in NumPy

In this section, we will optimize our particle simulator by rewriting some parts of it in NumPy. We found, from the profiling we did in Chapter 1, Benchmarking and Profiling, that the slowest part of our program is the following loop contained in the ParticleSimulator.evolve method:

    for i in range(nsteps): 
for p in self.particles:

norm = (p.x**2 + p.y**2)**0.5
v_x = (-p.y)/norm
v_y = p.x/norm

d_x = timestep * p.ang_vel * v_x
d_y = timestep * p.ang_vel * v_y

p.x += d_x
p.y += d_y

You may have noticed that the body of the loop acts solely on the current particle. If we had an array containing the particle positions and angular speed, we could rewrite the loop using a broadcasted operation. In contrast, the loop's steps depend on the previous step and cannot be parallelized in this way.

It is natural then...

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