By Gabriele Lanaro

Determine the bottlenecks on your functions and remedy them utilizing the simplest profiling techniques
Write effective numerical code in NumPy and Cython
Adapt your courses to run on a number of processors with parallel programming

Table of Contents
Preface
Chapter 1: Benchmarking and Profiling
Chapter 2: speedy Array Operations with NumPy
Chapter three: C functionality with Cython
Chapter four: Parallel Processing
Index

Preface

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bankruptcy 1: Benchmarking and Profiling
Designing your application
Writing exams and benchmarks
Timing your benchmark
discovering bottlenecks with cProfile
Profile line by way of line with line_profiler
Optimizing our code
The dis module
Profiling reminiscence utilization with memory_profiler
functionality tuning guidance for natural Python code
Summary

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bankruptcy 2: speedy Array Operations with NumPy
Getting began with NumPy
growing arrays
gaining access to arrays
Broadcasting
Mathematical operations
Calculating the Norm
Rewriting the particle simulator in NumPy
achieving optimum functionality with numexpr
Summary

Up

bankruptcy three: C functionality with Cython
Compiling Cython extensions
including static types
Variables
Functions
Classes
Sharing declarations
operating with arrays
C arrays and pointers
NumPy arrays
Typed memoryviews
Particle simulator in Cython
Profiling Cython
Summary

Up

bankruptcy four: Parallel Processing
advent to parallel programming
The multiprocessing module
the method and Pool classes
Monte Carlo approximation of pi
Synchronization and locks
IPython parallel
Direct interface
Task-based interface
Parallel Cython with OpenMP
Summary

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Extra resources for Python High Performance Programming

Example text

In this chapter, we will learn how to create and manipulate NumPy arrays. We will also explore the NumPy broadcasting feature to rewrite complex mathematical expressions in an efficient and succinct manner. In the last few years a number of packages were developed to further increase the speed of NumPy. We will explore one of these packages, numexpr, that optimizes array expressions and takes advantage of multi-core architectures. ndarray. NumPy arrays are a collection of elements of the same data type; this fundamental restriction allows NumPy to pack the data in an efficient way.

Most of the NumPy mathematical functions are also available in numexpr; however, there is a limitation. The reduction operations—the ones which reduce an axis, such as sum—have to happen last. So, we have to first calculate the sum, step out of numexpr, and calculate the square root in another expression. evaluate('sqrt(d_ij)') The numexpr compiler will optimize memory usage by avoiding the storage of intermediate results and by taking advantage of multiple processors. py file you will find two functions that implement the two versions of the distance matrix calculation: distance_matrix_numpy and distance_matrix_numexpr.

Array([[0, 1, 2], [3, 4, 5]]) In [7]: print(a) Out[7]: [[0 1 2] [3 4 5]] The array created in this way has two dimensions—axes in NumPy's jargon. Such an array is like a table that contains two rows and three columns. shape Out[7]: (2, 3) [ 32 ] Chapter 2 Arrays can also be reshaped, only as long as the product of the shape dimensions is equal to the total number of elements in the array. For example, we can reshape an array containing 16 elements in the following ways: (2, 8), (4, 4), or (2, 2, 4).

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