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Haskell High Performance Programming

Chapter 2. Choosing the Correct Data Structures

Perhaps the next most important topic in Haskell performance after lazy evaluation is data structures. I say the next most important because although data structures form a wider area than lazy evaluation, the unique performance aspects of lazy evaluation should deserve more attention. Still, structuring data efficiently is a must for performance, and in Haskell this often requires taking laziness into account, too.

Haskell gives the programmer lots of variety and clutches to structuring data, ranging from low-level primitives to ingenious, purely functional data structures. The traditional (re-)implementation costs associated with quick'n'dirty versus highly optimized solutions are really low in Haskell, and therefore there are even fewer reasons for complex premature optimizations in Haskell than in many other languages.

This chapter will help you to understand the performance semantics of Haskell values in general and...

Handling numerical data

Like all general-purpose programming languages, Haskell too has a few different number types. Unlike other languages, the number types in Haskell are organized into a hierarchy via type classes. This gives us two things:

Check sat compiletime we aren't doing anything insane with numbers
The ability to write polymorphic functions in the number type with enhanced type safety

An example of an insane thing would be dividing an integer by another integer, expecting an integer as a result. And because every integral type is an instance of the Integral class, we can easily write a factorial function that doesn't care what the underlying type is (as long as it represents an integer):

factorial :: Integral a => a -> a
factorial n = product [1..n]

The following table lists basic numeric types in Haskell:

Type	Size
`Int`	Signed integers, machine-dependent
`Word`	Unsigned integers, machine-dependent
`Double`	Double-precision floating point, machine-dependent
`Float...`

Handling numerical data

Check sat compiletime we aren't doing anything insane with numbers
The ability to write polymorphic functions in the number type with enhanced type safety

factorial :: Integral a => a -> a
factorial n = product [1..n]

The following table lists basic numeric types in Haskell:

Type	Size
`Int`	Signed integers, machine-dependent
`Word`	Unsigned integers, machine-dependent
`Double`	Double-precision floating point, machine-dependent
`Float`	Single...

Key benefits

Explore the benefits of lazy evaluation, compiler features, and tools and libraries designed for high performance

Write fast programs at extremely high levels of abstraction

Work through practical examples that will help you address the challenges of writing efficient code

Description

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.

What you will learn

Program idiomatic Haskell that s also surprisingly efficient

Improve performance of your code with data parallelism, inlining, and strictness annotations

Profile your programs to identify space leaks and missed opportunities for optimization

Find out how to choose the most efficient data and control structures

Optimize the Glasgow Haskell Compiler and runtime system for specific programs

See how to smoothly drop to lower abstractions wherever necessary

Execute programming for the GPU with Accelerate

Implement programming to easily scale to the cloud with Cloud Haskell

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Oswald Michael Nov 19, 2019

This book is ok to get an overview over performance in Haskell. On certain topics it does not delve very deep (which I would have wished for). But to get a bit of feel about what matters for performance in Haskell, it is really ok.Some Haskell source had a bad formatting inside (spaces got removed, so identifiers are not right), but with a little understanding it is clear what is meant.

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Kevin S. Van Horn Feb 26, 2020

The book is full of typos and inconsistencies that suggest the author never did a final review of the manuscript. New concepts (e.g. guarded recursion) are often ambiguously or incompletely defined, or defined only by example. Very sloppy writing.

Haskell High Performance Programming: Write Haskell programs that are robust and fast enough to stand up to the needs of today

What do you get with Print?

Haskell High Performance Programming

Chapter 2. Choosing the Correct Data Structures

Annotating strictness and unpacking datatype fields

Handling numerical data

Handling binary and textual data

Handling sequential data

Handling tabular data

Annotating strictness and unpacking datatype fields

Handling numerical data

Handling binary and textual data

Handling sequential data

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Haskell High Performance Programming: Write Haskell programs that are robust and fast enough to stand up to the needs of today

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What you will learn

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