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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Boost the performance of your Haskell applications using optimization, concurrency, and parallel programming
About 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 code
Who 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 Haskell
In 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.
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Seitenzahl: 563
Veröffentlichungsjahr: 2016
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Table of Contents
Haskell High Performance Programming
Haskell High Performance Programming
Copyright © 2016 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: September 2016
Production reference: 1190916
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham B3 2PB, UK.
ISBN 978-1-78646-421-7
www.packtpub.com
Credits
Author
Samuli Thomasson
Reviewer
Aaron Stevens
Commissioning Editor
Kunal Parikh
Acquisition Editor
Sonali Vernekar
Content Development Editor
Priyanka Mehta
Technical Editor
Ravikiran Pise
Copy Editor
Safis Editing
Project Coordinator
Izzat Contractor
Proofreader
Safis Editing
Indexer
Tejal Daruwale Soni
Graphics
Abhinash Sahu
Production Coordinator
Melwyn Dsa
Cover Work
Melwyn Dsa
About the Author
Samuli Thomasson is a long-time functional programming enthusiast from Finland who has used Haskell extensively, both as a pastime and commercially, for over four years. He enjoys working with great tools that help in getting things done nice and fast.
His current job at RELEX Solutions consists of providing technical solutions to a variety of practical problems. Besides functional programming, Samuli is interested in distributed systems, which he also studies at the University of Helsinki.
I am grateful to my awesome friends, who have stuck around and provided their support during the writing process, and my family for always being there and their understanding.
About the Reviewer
Aaron Stevens is a scientific software engineer with Molex LLC in Little Rock, Arkansas, where he combines his passion for programming with his education in electrical systems engineering to develop innovative techniques to characterize high-speed electronics in the lab and in production. He specializes in signal processing, statistical process-control methods, and application construction in Python and C#, and he enjoys discovering new methods to explore complex data sets through rich visualizations.
Away from the office, Aaron enjoys practicing with a variety of programming languages, studying linguistics, cooking, and spending time with his family. He received his BS in mathematics and BS in electrical systems engineering from the University of Arkansas in Little Rock.
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Preface
Haskell is an elegant language. It allows us to express in code exactly what we mean, in a clean and compact style. The nice features, including referential transparency and call-by-need evaluation, not only help the programmer be more efficient, but also help Haskell compilers to optimize programs in ways that are otherwise plain impossible. For example, the garbage collector of GHC is notoriously fast, not least thanks to its ability to exploit the immutability of Haskell values.
Unfortunately, high expressivity is a double-edged sword. Reasoning the exact order of evaluation in Haskell programs is, in general, not an easy task. A lack of understanding of the lazy call-by-need evaluation in Haskell will for sure lead the programmer to introduce space leaks sooner or later. A productive Haskell programmer not only has to know how to read and write the language, which is a hard enough skill to achieve in itself, they also need to understand a new evaluation schema and some related details. Of course, in order to not make things too easy, just knowing the language well will not get you very far. In addition, one has to be familiar with at least a few common libraries and, of course, the application domain itself.
This book will give you working knowledge of high-performance Haskell programming, including parallelism and concurrency. In this book, we will cover the language, GHC, and the common libraries of Haskell.
What this book covers
Chapter 1, Identifying Bottlenecks, introduces you to basic techniques for optimal evaluation and avoiding space leaks.
Chapter 2, Choose the Correct Data Structures, works with and optimizes both immutable and mutable data structures.
Chapter 3, Profile and Benchmark to Your Heart's Content, profiles Haskell programs using GHC and benchmarking using Criterion.
Chapter 4, The Devil's in the Detail, explains the small details that affect performance in Haskell programs, including code sharing, specializing, and simplifier rules.
Chapter 5, Parallelize for Performance, exploits parallelism in Haskell programs using the RePa library for data parallelism.
Chapter 6, I/O and Streaming, talks about the pros and cons of lazy and strict I/O in Haskell and explores the concept of streaming.
Chapter 7, Concurrency Performance, explores the different aspects of concurrent programming, such as shared variables, exception handling, and software-transactional memory.
Chapter 8, Tweaking the Compiler and Runtime System, chooses the optimal compiler and runtime parameters for Haskell programs compiled with GHC.
Chapter 9, GHC Internals and Code Optimizations, delves deeper into the compilation pipeline, and understands the intermediate representations of GHC.
Chapter 10, Foreign Function Interface, calls safely to and from C in Haskell using GHC and its FFI support.
Chapter 11, Programming for the GPU with Accelerate, uses the Accelerate library to program backend-agnostic GPU programs and executes on CUDA-enabled systems.
Chapter 12, Scaling to the Cloud with Cloud Haskell, uses the Cloud Haskell ecosystem to build distributed systems with Haskell.
Chapter 13, Functional Reactive Programming, introduces three Haskell FRP libraries, including Elerea, Yampa, and Reactive-banana.
Chapter 14, Library Recommendations, talks about a catalogue of robust Haskell libraries, accompanied with overviews and examples.
What you need for this book
To run most examples in this book, all you need is a working, relatively recent, installation of GHC and some Haskell libraries. Examples are built for nix-like systems, although they are easily adapted for a Windows machine.
The recommended minimum version for GHC is 7.6. The Haskell libraries needed are introduced in the chapters in which they are used. In Chapter 4, The Devil's in the Detail, we use the Haskell Stack tool to perform some tasks, but it isn't strictly required, although it is recommended to install Stack.
In Chapter 11, Programming for the GPU Using Accelerate, executing the CUDA versions of examples requires a CUDA-enabled system and the installation of the CUDA platform.
Who 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.
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Summary
In this chapter, we learned how lazy evaluation works, what weak head normal form is, and how to control it by increasing strictness with different methods. We considered the peculiarities of right-fold, left-fold, and strict left-fold, and in which situations one fold strategy works better than another. We introduced the concept of CAF along with memoization techniques, utilized the worker/wrapper pattern, and used guarded recursion to write clean and efficient recursive programs.
We used the :sprint command in GHCi to inspect unevaluated thunks and the Runtime System option -s to inspect the heap usage and GC activity of compiled programs. We took a look at inlining, stream fusion, and the performance costs of partial functions and polymorphism.
In the next chapter, we will take a look at other basic data and control structures, such as different array structures and some monads. But first, we will learn about the performance semantics of Haskell data types and related common optimization techniques.
