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

Reactive-banana – Safe and simple semantics


The third and final approach to FRP we'll take a look at is Reactive-banana. Like Yampa, we have both continuous- and discrete-time semantics at our disposal. In Reactive-banana terminology, events describe discrete time, while behaviors describe continuous time. Behaviors in Reactive-banana are like signals in Elerea. Historically, the names event and behavior were the first to occur:

data Event a    -- instance Functor
data Behavior a -- instance Functor, Applicative

Note that behavior is not Monad, unlike Elerea's signal, which did have a Monad instance. Instead, Reactive-banana supports moments as reactive computation contexts. There is a pure Moment monad, impure MomentIO monad and MonadMoment class that is used to overload moment context:

data Moment a     -- instance Monad, MonadFix, MonadMoment
data MomentIO a   –- instance ..., MonadIO

class Monad m => MonadMoment m where
    liftMoment :: Moment a -> m a

instance MonadMoment Moment...