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Haskell High Performance Programming

You're reading from   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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Product type Paperback
Published in Sep 2016
Publisher Packt
ISBN-13 9781786464217
Length 408 pages
Edition 1st Edition
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Author (1):
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Samuli Thomasson Samuli Thomasson
Author Profile Icon Samuli Thomasson
Samuli Thomasson
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Table of Contents (16) Chapters Close

Preface 1. Identifying Bottlenecks 2. Choosing the Correct Data Structures FREE CHAPTER 3. Profile and Benchmark to Your Heart's Content 4. The Devil's in the Detail 5. Parallelize for Performance 6. I/O and Streaming 7. Concurrency and Performance 8. Tweaking the Compiler and Runtime System (GHC) 9. GHC Internals and Code Generation 10. Foreign Function Interface 11. Programming for the GPU with Accelerate 12. Scaling to the Cloud with Cloud Haskell 13. Functional Reactive Programming 14. Library Recommendations 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

We can...

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