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

Events and signal functions with Yampa


Yampa is an FRP framework that supports both continuous and discrete time. In Yampa, the most important concept is the signal function (SF). Signal functions are first-class transformations on signals, that is, time-dependent values:

data SF a b  -- think: Signal a → Signal b

-- instance Arrow, ArrowLoop, Category

Signal functions can be created and manipulated via the Arrow interface. For instance, a pure transformation (a → b) is turned into a signal function simply with arr from the Arrow class. Here's a signal function which squares values passed through it:

square :: SF Double Double
square = arr (^2)

The embed utility function can be used to test signal functions:

embed square (1, [(0, Just 2), (1, Just 3)])
[1.0,4.0,9.0]

The type signature of embed looks like this:

embed :: SF a b -> (a, [(DTime, Maybe a)]) -> [b]

The first argument is the signal function to sample from. The second is a tuple that consists of the initial input to the signal...