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Haskell High Performance Programming

Haskell High Performance Programming

By : Thomasson
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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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15
Index

Asynchronous processing


Executing asynchronously involves forking a computation to execute in another thread and right after continuing to do other things. In interactive applications, it is often useful to execute things in the background, in order not to block the user interface for too long. Usually we want to use the results from the asynchronous worker thread once it has finished. Sometimes we wish to cancel a long-running asynchronous computation, in order not to leave unwanted jobs lying around.

Although it is totally possible to create asynchronous jobs with waits and cancels using MVar and perhaps STM, the Async API in the async package provides much nicer solutions.

But first, to be convinced there's nothing magical in the Async API abstraction, we'll build something with just MVar and STM. So, forking multiple asynchronous threads and waiting for their results is trivial with a few instances of MVar: just create an MVar for every worker and make the workers put their results into...

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