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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 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.

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

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.

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.
  • monad-par and LVish provide Par monads that can structure parallel computations over "monotonic" data structures, which in turn can be used from within purely functional programs.
  • [OLD] Intel Concurrent Collections for Haskell: a graph-oriented parallel programming model.

See also