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

The tiny discrete-time Elerea


In order to not feel overwhelmed, we'll begin with one of the simplest formulations of FRP, Elerea. Elerea is a very minimalist implementation, which restricts itself to discrete-time semantics and sampling. There are no events and everything is computed on demand only. Furthermore, the API consists of high-level constructs so that it's exceedingly difficult to shoot yourself in the foot with this library.

Time-varying values of type a are represented as Signal a in Elerea:

data Signal a
-- instances incl. Monad, Eq

Signals can be thought of as functions Nat → a, though obviously they are represented differently.

Signals are made inside signal generators, SignalGen a:

data SignalGen a
-- instances incl. Monad, MonadFix, MonadIO

Signal generators have a MonadFix instance, which will later allow us to build mutually recursive signals.

The minimal API provides just a few signal building blocks. Excluding some trivial extensions, the core combinators are as follows:

delay...