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Python High Performance Programming, by Gabriele Lanaro
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If you have at least a basic knowledge of Python, this book will help you maximize the performance of your code and applications. Easy to follow tutorials will make you a hard act to follow in Python programming.
Overview
- Identify the bottlenecks in your applications and solve them using the best profiling techniques
- Write efficient numerical code in NumPy and Cython
- Adapt your programs to run on multiple processors with parallel programming
In Detail
Python is a programming language with a vibrant community known for its simplicity, code readability, and expressiveness. The massive selection of third party libraries make it suitable for a wide range of applications. This also allows programmers to express concepts in fewer lines of code than would be possible in similar languages. The availability of high quality numerically-focused tools has made Python an excellent choice for high performance computing. The speed of applications comes down to how well the code is written. Poorly written code means poorly performing applications, which means unsatisfied customers.
This book is an example-oriented guide to the techniques used to dramatically improve the performance of your Python programs. It will teach optimization techniques by using pure python tricks, high performance libraries, and the python-C integration. The book will also include a section on how to write and run parallel code.
This book will teach you how to take any program and make it run much faster. You will learn state-of the art techniques by applying them to practical examples. This book will also guide you through different profiling tools which will help you identify performance issues in your program. You will learn how to speed up your numerical code using NumPy and Cython. The book will also introduce you to parallel programming so you can take advantage of modern multi-core processors.
This is the perfect guide to help you achieve the best possible performance in your Python applications.
What you will learn from this book
- Assess the performance of your programs using benchmarks
- Spot the bottlenecks in your code using the Python profiling tools
- Speed up your code by replacing Python loops with NumPy
- Boost NumPy performance using the numexpr compiler
- Use Cython to reach performance on par with the C language
- Write code for multiple processors
- Profile, optimize, and rewrite an application from start to finish
Approach
An exciting, easy-to-follow guide illustrating the techniques to boost the performance of Python code, and their applications with plenty of hands-on examples.
Who this book is written for
If you are a programmer who likes the power and simplicity of Python and would like to use this language for performance-critical applications, this book is ideal for you. All that is required is a basic knowledge of the Python programming language. The book will cover basic and advanced topics so will be great for you whether you are a new or a seasoned Python developer.
- Sales Rank: #1658010 in Books
- Published on: 2013-12-23
- Released on: 2013-12-23
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .25" w x 7.50" l, .44 pounds
- Binding: Paperback
- 108 pages
About the Author
Gabriele Lanaro
Gabriele Lanaro is a PhD student in Chemistry at the University of British Columbia, in the field of Molecular Simulation. He writes high performance Python code to analyze chemical systems in large-scale simulations. He is the creator of Chemlab—a high performance visualization software in Python—and emacs-for-python—a collection of emacs extensions that facilitate working with Python code in the emacs text editor. This book builds on his experience in writing scientific Python code for his research and personal projects.
Most helpful customer reviews
4 of 4 people found the following review helpful.
A good, short, and cheap introduction to speeding up Python code
By Hadayat Seddiqi
This book is fantastic for what it's for, and hopefully I can describe it well enough so that no one is surprised that I rated this 5 stars. FYI, I bought the kindle version and had no problems.
This book is extremely short. I was able to read it while flying over just a couple of states, but I've been using Python, Numpy, and Cython for a while now. I got this book to try and pick up on anything I didn't know. I found some things I wasn't familiar with (e.g. ipython parallel, multiprocessing, numexpr, and some profiling tools) and thought this book explained them well.
Essentially, there are a few simple examples that the author uses to show how to exploit some numerical and parallel processing libraries to get massive speedups in your Python code. These speedups are quite impressive, but that's mostly because Python is rather inefficient at most things in favor of flexibility. The key here is to shoehorn Python code into C code, whether that's through a library full of someone else's C code, or your own C extensions. The book shows that this process can actually be really, really simple in terms of code. In my opinion I think understanding how or why you should attempt certain optimizations is the hard part. I'm not sure a book can give you that as it's something you sort of pick up with practice, but this book gives you a good introduction to the tools for getting you there.
At this price point, if you are struggling with slow Python code and want to learn how to speed it up, buying the Kindle version at $10 will be worth your money. But do have a look at the Cython documentation as well, it is also pretty good. There are a couple of papers entitled "Fast numerical computations with Cython" by Seljebotn, and "Cython tutorial" by Behnel, Bradshaw, and Seljebotn that also serve as useful extensions to the documentation and this book for doing Cython work. See also the recent Cython book by Kurt Smith, which goes over similar topics but is a little longer (N.B., I haven't read through that one in its entirety).
5 of 7 people found the following review helpful.
This is what you need to know....
By Thomas B
All languages have an optimal way that they can be written to get the most performance out of them. Once you understand those techniques, you are on your way to creating more and more efficient solutions. This book is great for the novice to intermediate python developer and serves as a guide to writing better solutions.
0 of 1 people found the following review helpful.
Three Stars
By Moshe G.
In general terms, the book is OK but it could have a more in-depth treatment of the subject.
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