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  • Book Overview & Buying Haskell High Performance Programming
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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
Buy this Book

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

Working with monads and monad stacks

Monads are very useful abstractions, and like any sufficiently complex abstraction, many monads too incur some overhead. Two notable exceptions are IO and ST, which are eliminated during compilation. A single simple monad such as Reader or Writer has very minimal overhead, but monad stacks can incur unfortunate slowdowns. In most cases, the convenient nature of programming in a monad stack far outweighs the small overhead, because cost centers are rarely located in monad operations (excluding IO and ST).

Note

If you have an expensive subroutine in a State monad, it might be possible to convert it to ST for a big speedup. However, State is more expressive than ST so conversion is not always feasible.

The list monad and its transformer

The monad instance of lists admits attractive backtracking. For example, consider special Pythagorean triplets from Project Euler problem 9: find three natural numbers a < b < c such that a^2 + b^2 = c^2 and a + b + c ...

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