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Home > Programming > High Performance Programming > Haskell High Performance Programming
Haskell High Performance Programming
Haskell High Performance Programming

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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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...

Annotating strictness and unpacking datatype fields

Recall that in the previous chapter, we used seq to force strict evaluation. With the BangPatterns extension, we can force functions arguments. Strict arguments are evaluated WHNF just before entering the function body:

{-# LANGUAGE BangPatterns #-}

f !s (x:xs) = f (s + 1) xs
f !s      _ = s

Using bangs for annotating strictness in fact predates the BangPatterns extension (and the older compiler flag -fbang-patterns in GHC 6.x). With just plain Haskell98, we are allowed to use bangs to make datatype fields strict:

> data T = T !Int

A bang in front of a field ensures that whenever the outer constructor (T above) is in WHNF, the inner field is as well in WHNF. We can check this:

> T undefined `seq` ()
*** Exception: Prelude.undefined

There are no restrictions to which fields can be strict, be it recursive or polymorphic fields, although it rarely makes sense to make recursive fields strict. Consider the fully strict linked list:

data List...

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 binary and textual data

The smallest piece of data is a bit (0 or 1), which is isomorphic to Bool (True or False). When you need just one bit, a Bool should be your choice. If you need a few bits, then a tuple of Bools will fit the purpose when performance is not critical. A [Bool] is sometimes convenient, but should only be chosen for convenience in some situations.

For high-performance binary data, you could define your own data type with strict Bool fields. But this has an important caveat, namely that Bool is not a primitive but an algebraic data type:

data Bool = False | True

The consequence is that you cannot unpack a Bool similar to how you could an Int or Double. In Haskell, Bool values will always be represented by pointers. Fortunately for many bit-fiddling applications, you can define a data type like this:

data BitStruct = BitStore !Bool !Bool !Bool

This will get respectable performance. However, if you need a whole array of bits it quickly becomes inconvenient to define...

Handling sequential data

The standard list,[], is the most used data structure for sequential data. It has reasonable performance, but when processing multiple small values, say Chars, the overhead of a linked list might be too much. Often, the convenient nature of [] is convincing enough.

The wide range of list functions in Data.List are hand-optimized and many are subject to fusion. List fusion, as it is currently implemented using the foldr/build fusion transformation, is subtly different from stream fusion employed in ByteString and Text (concatMap is a bit problematic with traditional stream fusion). Still, the end result is pretty much the same; in a long pipeline of list functions, intermediate lists will usually not be constructed.

Say we want a pipeline that first increases every element by one, calculates intermediate sums of all elements up to current element, and finally sums all elements. From the previous chapter, we have learned to write optimally strict recursive functions...

Handling tabular data

If you need O(1) general indexing, a table-like data structure is virtually your only option. The Haskell report specifies the array package, which provides tables indexed by anything with an instance for a Ix typeclass.

Immutable arrays come in two flavors (we'll discuss mutable arrays later):

  • Data.Array.Array: Immutable arrays of boxed values
  • Data.Array.Unboxed.UArray: Immutable arrays of unboxed values

A common use case for Immutable arrays is memoization. For example, a table of Fibonacci numbers could be constructed as follows:

-- file: fib-array-mem.hs
import Data.Array

fib :: Int -> Array Int Integer
fib n = arr where
  arr = listArray (1,n) $ 1 : 1 : [ arr!(i-2) + arr!(i-1)| i <- [3..n] ]

We can also index by a tuple, which gives the array extra dimensions. The symmetric Pascal matrix will serve as an example:

pascal :: Int -> Array (Int, Int) Integer
pascal n = arr where
  arr = array ((1,1),(n,n)) $
    [ ((i,1),1) | i <- [1..n] ] ++
    [ ((1...

Annotating strictness and unpacking datatype fields

Recall that in the previous chapter, we used seq to force strict evaluation. With the BangPatterns extension, we can force functions arguments. Strict arguments are evaluated WHNF just before entering the function body:

{-# LANGUAGE BangPatterns #-}

f !s (x:xs) = f (s + 1) xs
f !s      _ = s

Using bangs for annotating strictness in fact predates the BangPatterns extension (and the older compiler flag -fbang-patterns in GHC 6.x). With just plain Haskell98, we are allowed to use bangs to make datatype fields strict:

> data T = T !Int

A bang in front of a field ensures that whenever the outer constructor (T above) is in WHNF, the inner field is as well in WHNF. We can check this:

> T undefined `seq` ()
*** Exception: Prelude.undefined

There are no restrictions to which fields can be strict, be it recursive or polymorphic fields, although it rarely makes sense to make recursive fields strict. Consider the fully strict linked list:

data List...

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

Single...

Handling binary and textual data

The smallest piece of data is a bit (0 or 1), which is isomorphic to Bool (True or False). When you need just one bit, a Bool should be your choice. If you need a few bits, then a tuple of Bools will fit the purpose when performance is not critical. A [Bool] is sometimes convenient, but should only be chosen for convenience in some situations.

For high-performance binary data, you could define your own data type with strict Bool fields. But this has an important caveat, namely that Bool is not a primitive but an algebraic data type:

data Bool = False | True

The consequence is that you cannot unpack a Bool similar to how you could an Int or Double. In Haskell, Bool values will always be represented by pointers. Fortunately for many bit-fiddling applications, you can define a data type like this:

data BitStruct = BitStore !Bool !Bool !Bool

This will get respectable performance. However, if you need a whole array of bits it quickly becomes inconvenient to define...

Handling sequential data

The standard list,[], is the most used data structure for sequential data. It has reasonable performance, but when processing multiple small values, say Chars, the overhead of a linked list might be too much. Often, the convenient nature of [] is convincing enough.

The wide range of list functions in Data.List are hand-optimized and many are subject to fusion. List fusion, as it is currently implemented using the foldr/build fusion transformation, is subtly different from stream fusion employed in ByteString and Text (concatMap is a bit problematic with traditional stream fusion). Still, the end result is pretty much the same; in a long pipeline of list functions, intermediate lists will usually not be constructed.

Say we want a pipeline that first increases every element by one, calculates intermediate sums of all elements up to current element, and finally sums all elements. From the previous chapter, we have learned to write optimally strict recursive functions...

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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.

Who is this book for?

To get the most out of this book, you need to have a working knowledge of reading and writing basic Haskell. No knowledge of performance, optimization, or concurrency is required.

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

Product Details

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : Sep 26, 2016
Length: 408 pages
Edition : 1st
Language : English
ISBN-13 : 9781786466914
Vendor :
The Glasgow Haskell Team
Category :
Programming
Languages :
Haskell
Concepts :
High Performance Programming
Tools :
GHC

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Product Details

Publication date : Sep 26, 2016
Length: 408 pages
Edition : 1st
Language : English
ISBN-13 : 9781786466914
Vendor :
The Glasgow Haskell Team
Category :
Programming
Languages :
Haskell
Concepts :
High Performance Programming
Tools :
GHC

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Table of Contents

15 Chapters
1. Identifying Bottlenecks Chevron down icon Chevron up icon
1. Identifying Bottlenecks
Meeting lazy evaluation
Memoization and CAFs
Recursion and accumulators
Inspecting time and space usage
Compiler code optimizations
Summary
2. Choosing the Correct Data Structures Chevron down icon Chevron up icon
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
Handling sparse data
Ephemeral data structures
Working with monads and monad stacks
Summary
3. Profile and Benchmark to Your Heart's Content Chevron down icon Chevron up icon
3. Profile and Benchmark to Your Heart's Content
Profiling time and allocations
Heap profiling
Benchmarking using the criterion library
Profile and monitor in real time
Summary
4. The Devil's in the Detail Chevron down icon Chevron up icon
4. The Devil's in the Detail
The anatomy of a Haskell project
Erroring and handling exceptions
Writing tests for Haskell
Trivia at term-level
Trivia at type-level
Useful GHC extensions
Summary
5. Parallelize for Performance Chevron down icon Chevron up icon
5. Parallelize for Performance
Primitive parallelism and the Runtime System
The Eval monad and strategies
The Par monad and schedules
Diagnosing parallelism – ThreadScope
Data parallel programming – Repa
Summary
6. I/O and Streaming Chevron down icon Chevron up icon
6. I/O and Streaming
Reading, writing, and handling resources
Streaming with side-effects
Logging in Haskell
Summary
7. Concurrency and Performance Chevron down icon Chevron up icon
7. Concurrency and Performance
Threads and concurrency primitives
Software Transactional Memory
Runtime System and threads
Asynchronous processing
Lifting up from I/O
Summary
8. Tweaking the Compiler and Runtime System (GHC) Chevron down icon Chevron up icon
8. Tweaking the Compiler and Runtime System (GHC)
Using GHC like a pro
Tuning GHC's Runtime System
Summary of useful GHC options
Summary of useful RTS options
Summary
9. GHC Internals and Code Generation Chevron down icon Chevron up icon
9. GHC Internals and Code Generation
Interpreting GHC's internal representations
Primitive GHC-specific features
Datatype generic programming
Generating Haskell with Haskell
Summary
10. Foreign Function Interface Chevron down icon Chevron up icon
10. Foreign Function Interface
From Haskell to C and C to Haskell
Data marshal and stable pointers
Summary
11. Programming for the GPU with Accelerate Chevron down icon Chevron up icon
11. Programming for the GPU with Accelerate
Writing Accelerate programs
Running with the CUDA backend
More Accelerate concepts
Summary
12. Scaling to the Cloud with Cloud Haskell Chevron down icon Chevron up icon
12. Scaling to the Cloud with Cloud Haskell
Processes and message-passing
Handling failure
Nodes and networking
Summary
13. Functional Reactive Programming Chevron down icon Chevron up icon
13. Functional Reactive Programming
The tiny discrete-time Elerea
Events and signal functions with Yampa
Reactive-banana – Safe and simple semantics
Combining events and behaviors
Summary
14. Library Recommendations Chevron down icon Chevron up icon
14. Library Recommendations
Representing data
Functional graphs
Numeric data for special use
Encoding and serialization
Persistent storage, SQL, and NoSQL
Networking and HTTP
Cryptography
Web technologies
Parsing and pretty-printing
Pretty-printing and text formatting
Control and utility libraries
Working with monads and transformers
Handling exceptions
Random number generators
Parallel and concurrent programming
Functional Reactive Programming
Mathematics, statistics, and science
Tools for research and sketching
The HaskellR project
Creating charts and diagrams
Scripting and CLI applications
Testing and benchmarking
Summary
Index Chevron down icon Chevron up icon
Index

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Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
(2 Ratings)
5 star 0%
4 star 50%
3 star 0%
2 star 50%
1 star 0%
Oswald Michael Nov 19, 2019
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
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.
Amazon Verified review Amazon
Kevin S. Van Horn Feb 26, 2020
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
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.
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About the author

Profile icon Thomasson
Thomasson
Samuli Thomasson is a long-time functional programming enthusiast from Finland who has used Haskell extensively, both as a pastime and commercially, for over four years. He enjoys working with great tools that help in getting things done nice and fast. His current job at RELEX Solutions consists of providing technical solutions to a variety of practical problems. Besides functional programming, Samuli is interested in distributed systems, which he also studies at the University of Helsinki.
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  • To view your account details or to download a new copy of the book go to www.packtpub.com/account
  • To contact us directly if a problem is not resolved, use www.packtpub.com/contact-us
What eBook formats do Packt support? Chevron down icon Chevron up icon

Our eBooks are currently available in a variety of formats such as PDF and ePubs. In the future, this may well change with trends and development in technology, but please note that our PDFs are not Adobe eBook Reader format, which has greater restrictions on security.

You will need to use Adobe Reader v9 or later in order to read Packt's PDF eBooks.

What are the benefits of eBooks? Chevron down icon Chevron up icon
  • You can get the information you need immediately
  • You can easily take them with you on a laptop
  • You can download them an unlimited number of times
  • You can print them out
  • They are copy-paste enabled
  • They are searchable
  • There is no password protection
  • They are lower price than print
  • They save resources and space
What is an eBook? Chevron down icon Chevron up icon

Packt eBooks are a complete electronic version of the print edition, available in PDF and ePub formats. Every piece of content down to the page numbering is the same. Because we save the costs of printing and shipping the book to you, we are able to offer eBooks at a lower cost than print editions.

When you have purchased an eBook, simply login to your account and click on the link in Your Download Area. We recommend you saving the file to your hard drive before opening it.

For optimal viewing of our eBooks, we recommend you download and install the free Adobe Reader version 9.