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

Summary


Now we have crammed in how parallelism is done in Haskell and an overview of the threaded runtime. We parallelized pure and lazy data structures with strategies and Eval (the parallel package). For more control and parallelism in IO, we had to resort to schedules and Par (the monad-par package). We dived into data-parallel programming with Repa and even wrote a string recognition program with it.

We learned to use the event log and ThreadScope to diagnose the parallel performance of Haskell programs. Things to keep in mind when parallelizing programs are: use good granularity, not too much overhead but not too much sequential processing either; compile with flags optimized for parallelism, especially with Repa; and profile and diagnose before applying transformations at the code level.

In the next chapter, we will look at stream processing in Haskell: I/O, networking, and streaming libraries such as conduits and pipes. Lazy I/O, combined with interacting with networks, produces nightmarish...