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Parallelism

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== Parallel Programming in GHC ==
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Parallelism is about speeding up a program by using multiple processors.
   
This page contains notes and information about how to use parallelism in GHC to speed up pure functions in your program.
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In Haskell we provide two ways to achieve parallelism:
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* Pure parallelism, which can be used to speed up pure (non-IO) parts of the program.
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* Concurrency, which can be used for parallelising IO.
   
You may be interested in [[GHC/Concurrency|concurrency]] instead, which would allow you to manage simultaneous IO actions.
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Pure Parallelism (Control.Parallel): Speeding up a pure computation using multiple processors. Pure parallelism has these advantages:
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* Guaranteed deterministic (same result every time)
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* no [[race conditions]] or [[deadlocks]]
   
GHC provides multi-scale support for parallel programming, from very fine-grained, small "sparks", to coarse-grained explicit threads and locks (using concurrency), along with other models of parallel programming.
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[[Concurrency]] (Control.Concurrent): Multiple threads of control that execute "at the same time".
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* Threads are in the IO monad
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* IO operations from multiple threads are interleaved non-deterministically
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* communication between threads must be explicitly programmed
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* Threads may execute on multiple processors simultaneously
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* Dangers: [[race conditions]] and [[deadlocks]]
   
* See "Real World Haskell" [http://book.realworldhaskell.org/read/concurrent-and-multicore-programming.html chapter 24], for an introduction to the most common forms of concurrent and parallel programming in GHC.
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Rule of thumb: use Pure Parallelism if you can, Concurrency otherwise.
* A [http://donsbot.wordpress.com/2009/09/03/parallel-programming-in-haskell-a-reading-list/ reading list for parallelism in Haskell].
 
* The [http://stackoverflow.com/questions/3063652/whats-the-status-of-multicore-programming-in-haskell status of parallel and concurrent programming] in Haskell.
 
 
The parallel programming models in GHC can be divided into the following forms:
 
   
* Very fine grained: parallel sparks and futures, as described in the paper "[http://www.haskell.org/~simonmar/bib/multicore-ghc-09_abstract.html Runtime Support for Multicore Haskell]"
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== Starting points ==
* Nested data parallelism: a parallel programming model based on bulk data parallelism, in the form of the [http://www.haskell.org/haskellwiki/GHC/Data_Parallel_Haskell DPH] and [http://hackage.haskell.org/package/repa Repa] libraries for transparently parallel arrays.
 
* Intel [http://software.intel.com/en-us/blogs/2010/05/27/announcing-intel-concurrent-collections-for-haskell-01/ Concurrent Collections for Haskell]: a graph-oriented parallel programming model.
 
   
The most important (as of 2010) to get to know are implicit parallelism via sparks. If you're interested in scientific programming specifically, you may also be interested in current research on nested data parallelism in Haskell.
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* '''Control.Parallel'''. The first thing to start with parallel programming in Haskell is the use of par/pseq from the parallel library. Try the Real World Haskell [http://book.realworldhaskell.org/read/concurrent-and-multicore-programming.html chapter on parallelism and concurrency]. The parallelism-specific parts are in the second half of the chapter.
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* If you need more control, try Strategies or perhaps the Par monad
   
=== Starting points ===
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== Multicore GHC ==
   
* '''Control.Parallel'''. The first thing to start with parallel programming in Haskell is the use of par/pseq from the parallel library.
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{{GHC/Multicore}}
   
* '''Nested Data Parallelism'''. For an approach to exploiting the implicit parallelism in array programs for multiprocessors, see [[GHC/Data Parallel Haskell|Data Parallel Haskell]] (work in progress).
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== Alternative approaches ==
   
=== Multicore GHC ===
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* Nested data parallelism: a parallel programming model based on bulk data parallelism, in the form of the [http://www.haskell.org/haskellwiki/GHC/Data_Parallel_Haskell DPH] and [http://hackage.haskell.org/package/repa Repa] libraries for transparently parallel arrays.
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* Intel [http://software.intel.com/en-us/blogs/2010/05/27/announcing-intel-concurrent-collections-for-haskell-01/ Concurrent Collections for Haskell]: a graph-oriented parallel programming model.
{{GHC/Multicore}}
 
   
=== Related work ===
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== See also ==
   
* The Sun project to improve http://ghcsparc.blogspot.com/ GHC performance on Sparc]
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* The [[Parallel|parallelism and concurrency portal]]
* A [http://www.well-typed.com/blog/38 Microsoft project to improve industrial applications of GHC parallelism].
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* Parallel [[Parallel/Reading|reading list]]
* [http://www.haskell.org/~simonmar/bib/bib.html Simon Marlow's publications on parallelism and GHC]
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* [[Parallel/Research|Ongoing research in Parallel Haskell]]
* [http://www.macs.hw.ac.uk/~dsg/gph/ Glasgow Parallel Haskell]
 
* [http://www.macs.hw.ac.uk/~dsg/gdh/ Glasgow Distributed Haskell]
 
* http://www-i2.informatik.rwth-aachen.de/~stolz/dhs/
 
* http://www.informatik.uni-kiel.de/~fhu/PUBLICATIONS/1999/ifl.html
 
* [http://www.mathematik.uni-marburg.de/~eden Eden]
 

Revision as of 14:25, 20 April 2011

Parallelism is about speeding up a program by using multiple processors.

In Haskell we provide two ways to achieve parallelism:

  • Pure parallelism, which can be used to speed up pure (non-IO) parts of the program.
  • Concurrency, which can be used for parallelising IO.

Pure Parallelism (Control.Parallel): Speeding up a pure computation using multiple processors. Pure parallelism has these advantages:

Concurrency (Control.Concurrent): Multiple threads of control that execute "at the same time".

  • Threads are in the IO monad
  • IO operations from multiple threads are interleaved non-deterministically
  • communication between threads must be explicitly programmed
  • Threads may execute on multiple processors simultaneously
  • Dangers: race conditions and deadlocks

Rule of thumb: use Pure Parallelism if you can, Concurrency otherwise.

Contents

1 Starting points

  • Control.Parallel. The first thing to start with parallel programming in Haskell is the use of par/pseq from the parallel library. Try the Real World Haskell chapter on parallelism and concurrency. The parallelism-specific parts are in the second half of the chapter.
  • If you need more control, try Strategies or perhaps the Par monad

2 Multicore GHC

Since 2004, GHC supports running programs in parallel on an SMP or multi-core machine. How to do it:

  • Compile your program using the -threaded switch.
  • Run the program with +RTS -N2 to use 2 threads, for example (RTS stands for runtime system; see the GHC users' guide). You should use a -N value equal to the number of CPU cores on your machine (not including Hyper-threading cores). As of GHC v6.12, you can leave off the number of cores and all available cores will be used (you still need to pass -N however, like so: +RTS -N).
  • Concurrent threads (forkIO) will run in parallel, and you can also use the par combinator and Strategies from the Control.Parallel.Strategies module to create parallelism.
  • Use +RTS -sstderr for timing stats.
  • To debug parallel program performance, use ThreadScope.

3 Alternative approaches

  • Nested data parallelism: a parallel programming model based on bulk data parallelism, in the form of the DPH and Repa libraries for transparently parallel arrays.
  • Intel Concurrent Collections for Haskell: a graph-oriented parallel programming model.

4 See also