Revision as of 13:09, 24 December 2014 by Henk-Jan van Tuyl (talk | contribs) (Added links to Wikipedia for race conditions and deadlocks)
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:
- Guaranteed deterministic (same result every time)
- no race conditions or deadlocks
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.
- 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
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.
- 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.