Haskell High Performance Programming

Boost the performance of your Haskell applications using optimization, concurrency, and parallel programmingAbout This Book Explore the benefits of lazy evaluation, compiler features, and tools and libraries designed for high performance Write fast programs at extremely high levels of abstraction Work through practical examples that will help you address the challenges of writing efficient codeWho This Book Is For To get the most out of this book, you need to have a working knowledge of reading and writing basic Haskell. No knowledge of performance, optimization, or concurrency is required. What You Will Learn Program idiomatic Haskell that's also surprisingly efficient Improve performance of your code with data parallelism, inlining, and strictness annotations Profile your programs to identify space leaks and missed opportunities for optimization Find out how to choose the most efficient data and control structures Optimize the Glasgow Haskell Compiler and runtime system for specific programs See how to smoothly drop to lower abstractions wherever necessary Execute programming for the GPU with Accelerate Implement programming to easily scale to the cloud with Cloud HaskellIn Detail 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. Style and approach This easy-to-follow guide teaches new practices and techniques to optimize your code, and then moves towards more advanced ways to effectively write efficient Haskell code. Small and simple practical examples will help you test the concepts yourself, and you will be able to easily adapt them for any application.

Cover; Copyright; Credits; About the Author; About the Reviewer; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Identifying Bottlenecks; Meeting lazy evaluation; Writing sum correctly; Weak head normal form; Folding correctly; Memoization and CAFs; Constant applicative form; Recursion and accumulators; The worker/wrapper idiom; Guarded recursion; Accumulator parameters; Inspecting time and space usage; Increasing sharing and minimizing allocation; Compiler code optimizations; Inlining and stream fusion; Polymorphism performance; Partial functions; Summary.

Chapter 2: Choosing the Correct Data StructuresAnnotating strictness and unpacking datatype fields; Unbox with UNPACK; Using anonymous tuples; Performance of GADTs and branching; Handling numerical data; Handling binary and textual data; Representing bit arrays; Handling bytes and blobs of bytes; Working with characters and strings; Using the text library; Builders for iterative construction; Builders for strings; Handling sequential data; Using difference lists; Difference list performance; Difference list with the writer monad; Using zippers; Accessing both ends fast with Seq.

Handling tabular dataUsing the vector …