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  • 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

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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Preface
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Identifying Bottlenecks
1. Identifying Bottlenecks
Meeting lazy evaluation
Memoization and CAFs
Recursion and accumulators
Inspecting time and space usage
Compiler code optimizations
Summary
2
2. Choosing the Correct Data Structures
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
3. Profile and Benchmark to Your Heart's Content
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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
4. The Devil's in the Detail
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
5. Parallelize for Performance
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
6. I/O and Streaming
6. I/O and Streaming
Reading, writing, and handling resources
Streaming with side-effects
Logging in Haskell
Summary
7
7. Concurrency and Performance
7. Concurrency and Performance
Threads and concurrency primitives
Software Transactional Memory
Runtime System and threads
Asynchronous processing
Lifting up from I/O
Summary
8
8. Tweaking the Compiler and Runtime System (GHC)
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
9. GHC Internals and Code Generation
9. GHC Internals and Code Generation
Interpreting GHC's internal representations
Primitive GHC-specific features
Datatype generic programming
Generating Haskell with Haskell
Summary
10
10. Foreign Function Interface
10. Foreign Function Interface
From Haskell to C and C to Haskell
Data marshal and stable pointers
Summary
11
11. Programming for the GPU with Accelerate
11. Programming for the GPU with Accelerate
Writing Accelerate programs
Running with the CUDA backend
More Accelerate concepts
Summary
12
12. Scaling to the Cloud with Cloud Haskell
12. Scaling to the Cloud with Cloud Haskell
Processes and message-passing
Handling failure
Nodes and networking
Summary
13
13. Functional Reactive Programming
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
14. Library Recommendations
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
15
Index
Index

Benchmarking using the criterion library

Profiling aside, benchmarking the time a calculation takes to perform is a direct indicator of real-world performance. Benchmarking Haskell applications is pretty much dominated by the criterion library. There is a system called nofib, which is used to benchmark GHC itself, but for applications criterion is superior. Criterion even produces interactive web pages describing the results of benchmarks, which is a nice feature.

This text is written for criterion-1.1.1.0. Obviously, the criterion package needs to be installed:

cabal install criterion       # or: stack install criterion

A criterion benchmark suite is created as a normal Haskell program. An example is this:

–– file: benchmark.hs

import Criterion.Main
import Data.List (foldl')

main = defaultMain [
  bgroup "sum" [ bench "sum"    $ whnf sum            [1..1000000]
    , bench "foldr"  $ whnf (foldr (+) 0)  [1..1000000]
    , bench "foldl...

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