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
Chapter 1: Benchmarking and Profiling
Chapter 2: speedy Array Operations with NumPy
Chapter three: C functionality with Cython
Chapter four: Parallel Processing
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
bankruptcy 2: speedy Array Operations with NumPy
Getting began with NumPy
gaining access to arrays
Calculating the Norm
Rewriting the particle simulator in NumPy
achieving optimum functionality with numexpr
bankruptcy three: C functionality with Cython
Compiling Cython extensions
including static types
operating with arrays
C arrays and pointers
Particle simulator in Cython
bankruptcy four: Parallel Processing
advent to parallel programming
The multiprocessing module
the method and Pool classes
Monte Carlo approximation of pi
Synchronization and locks
Parallel Cython with OpenMP
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• Simplify layout trend implementation utilizing the facility of Python
• every one development is followed with a real-world instance demonstrating its key features
• this can be an easy-to-follow consultant targeting the sensible facets of Python layout patterns
Who This ebook Is For
This ebook is for Python programmers with an intermediate history and an curiosity in layout styles carried out in idiomatic Python. Programmers of alternative languages who're attracted to Python may also take advantage of this booklet, however it will be higher in the event that they first learn a few introductory fabrics that designate how issues are performed in Python.
What you are going to Learn
• discover manufacturing facility approach and summary manufacturing unit for item creation
• Clone items utilizing the Prototype pattern
• Make incompatible interfaces suitable utilizing the Adapter pattern
• safe an interface utilizing the Proxy pattern
• decide on an set of rules dynamically utilizing the tactic pattern
• expand an item with out subclassing utilizing the Decorator pattern
• maintain the common sense decoupled from the UI utilizing the MVC pattern
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Extra resources for Python High Performance Programming
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 : print(a) Out: [[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: (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).