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

Primitive GHC-specific features


All strictly GHC-specific functionality is contained in GHC.* module. The GHC.Exts module is of particular interest. The GHC.Prim module (re-exported by GHC.Exts) exports core primitives in GHC.

For a while now, GHC has shipped with primitives for SIMD processor instructions. These are available when compiling via the LLVM backend (-fllvm).

SIMD stands for Single Instruction, Multiple Data. It basically means performing the same operation on a whole vector of machine numbers at the cost of performing that operation on just one number. SIMD vector types can be found in the GHC.Prim module. The specialized vectors are named like Int8X16#, which stands for an Int8 vector of length 16. DoubleX8# stands for a vector of eight double precision values:

data Int8X16#
data DoubleX8#

These types are primitive and there are no exposed constructors.

To create vectors that can be used with SIMD instructions, we have two basic options. The first one is to use one of the broadcast...