m (→Parallel and Concurrent Programming in GHC)
m (swap order of pure parallelism and concurrency, put pure first)
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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.
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.