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 Par monad and schedules


The parallel package restricts us to expressing our computations as lazy data structures. Moreover, such computations must always be pure, so no parallel IO is possible. Sometimes this isn't feasible and we would like to express more control. Somewhat inherently, more control implies less expressiveness. This trade-off is made in the monad-par package.

The core interface in Control.Monad.Par consists of:

data Par a  -- instance Monad
runPar :: Par a → a
fork :: Par () → Par ()

The monad-par library defines its own context for computations, namely Par. The second important operation, next to executing the computation via runPar, is fork, which forks a computation so it happens in parallel.

Communication between computations in Par happens via IVar:

data IVar a
new :: Par (IVar a)
get :: IVar a → Par a
put :: NFData a => IVar a → a → Par ()

To run two computations, c1 and c2, in parallel and return their results in a tuple, we would write:

-- file: ivar-testing.hs
import...