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Haskell High Performance Programming

Haskell High Performance Programming

By : Thomasson
3 (2)
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Haskell High Performance Programming

Haskell High Performance Programming

3 (2)
By: 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 (16 chapters)
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15
Index

Summary


In this chapter, we have discussed the easiest way to increase GHC Haskell's performance: tweaking compiler and Runtime System flags. Enabling optimizations, compiling via LLVM, and enabling LLVM optimizations is a quick route to a usually very respectable performance. Although most of the time GHC's sophisticated, heuristic optimizations produce faster code, this is not always the case. Some optimizations produce slow and even incorrect code under certain situations. Unsafe functions in particular interact badly with many optimizations. Furthermore, eager inlining may produce very big binaries.

We discussed features in the Runtime System and how to enable and configure them. Light-weight (green) threads were cheap, scheduled by RTS, and enabled easy concurrent evaluation via sparks, but were limited with regard to foreign system calls. The parallel and generational garbage collector also had multiple tunable parameters to experiment with.

In the next chapter, we will learn to read...

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