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

More Accelerate concepts


So far, we have considered accelerated arrays and expressions. These are the primitives that Accelerate builds upon. On top, we have a bunch of functional machinery to help us express ourselves in accelerated computations: zips and unzips, reductions, permutations, stencils, and so forth. The complete API is documented in the accelerate package. In this section, we consider using some of the most useful parts of this machinery.

Working with tuples

GPUs don't allow array nesting or tuples as elements of an array. Nested arrays can be somewhat mimicked with higher-dimensional arrays. And it might not come as a surprise that Accelerate supports tuples as elements of an array. Internally, arrays with tupled elements are represented as tuples of arrays, but this is strictly an implementation detail. For the programmer, it really looks like we are working with tupled elements, which is sometimes very convenient.

With zip and unzip, we can (de)construct tupled elements within...