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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 (16 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
Free Chapter
1
1. Identifying Bottlenecks
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1. 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
2. Choosing the Correct Data Structures
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2. 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
3. Profile and Benchmark to Your Heart's Content
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3. 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
4. The Devil's in the Detail
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4. 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
5. Parallelize for Performance
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5. 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
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6. I/O and Streaming
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6. 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
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7. Concurrency and Performance
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7. 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
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8. Tweaking the Compiler and Runtime System (GHC)
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8. 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
9. GHC Internals and Code Generation
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9. 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
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10. Foreign Function Interface
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10. 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
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11. Programming for the GPU with Accelerate
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11. 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
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12. Scaling to the Cloud with Cloud Haskell
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12. 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
13. Functional Reactive Programming
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13. 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
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14. Library Recommendations
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14. 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
15
Index
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Index

Handling failure

In a distributed system, it's customary to fail fast and let other processes deal with failure. This principle is encouraged in Erlang, from which Cloud Haskell is modelled. We should be prepared for an arbitrary process crashing, with its parent or monitoring process handling the failure (or propagating it further to a parent's parent).

There are two tactics to be noted about process failure in Cloud Haskell: linking and monitoring. The difference is that a linked process propagates exceptions to its parent, while an exception in a monitored process results in the monitoring process receiving a ProcessMonitorNotification message.

Firing up monitors

The basic monitoring API is the following:

monitor     :: ProcessId  → Process MonitorRef
monitorNode :: NodeId     → Process MonitorRef
monitorPort :: SendPort a → Process MonitorRef

unmonitor   :: MonitorRef → Process ()

withMonitor :: ProcessId  → Process a → Process a

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