Book Image

Haskell High Performance Programming

By : Samuli Thomasson
Book Image

Haskell High Performance Programming

By: Samuli Thomasson

Overview of this book

Haskell, with its power to optimize the code and its high performance, is a natural candidate for high performance programming. It is especially well suited to stacking abstractions high with a relatively low performance cost. This book addresses the challenges of writing efficient code with lazy evaluation and techniques often used to optimize the performance of Haskell programs. We open with an in-depth look at the evaluation of Haskell expressions and discuss optimization and benchmarking. You will learn to use parallelism and we'll explore the concept of streaming. We’ll demonstrate the benefits of running multithreaded and concurrent applications. Next we’ll guide you through various profiling tools that will help you identify performance issues in your program. We’ll end our journey by looking at GPGPU, Cloud and Functional Reactive Programming in Haskell. At the very end there is a catalogue of robust library recommendations with code samples. By the end of the book, you will be able to boost the performance of any app and prepare it to stand up to real-world punishment.
Table of Contents (21 chapters)
Haskell High Performance Programming
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Chapter 5. Parallelize for Performance

Nowadays, as single processor cores are not getting much faster, CPU manufacturers instead keep increasing the number of cores in processors, implying that high-performance programs must accordingly exploit more and more parallelism to keep up with this breadth-wise hardware development.

Turns out, one of Haskell's strongest aspects, referential transparency, is very valuable for parallelization. Automatically knowing that some distinct expressions won't interact with each other means they are safe to execute simultaneously. Note that parallelism is very different from concurrency, which usually refers to interacting processes (they aren't necessarily executed in parallel).

In this chapter, we will cover what the Haskell ecosystem currently has to offer for parallelism: a powerful parallel runtime system, fairly high-level abstractions for parallel evaluation, data parallel programming, and diagnostic tools for parallel programs. The learning objectives...