5.4. Using Concurrent Haskell¶
GHC supports Concurrent Haskell by default, without requiring a special option or libraries compiled in a certain way. To get access to the support libraries for Concurrent Haskell, just import Control.Concurrent. More information on Concurrent Haskell is provided in the documentation for that module.
If a thread makes a foreign call (and the call is not marked
unsafe), then other Haskell threads in the program will continue to run while the foreign call is in progress. Additionally,
foreign exported Haskell functions may be called from multiple OS threads simultaneously. See Multi-threading and the FFI.
The following RTS option(s) affect the behaviour of Concurrent Haskell programs:
Sets the context switch interval to ⟨s⟩ seconds. A context switch will occur at the next heap block allocation after the timer expires (a heap block allocation occurs every 4k of allocation). With
-C, context switches will occur as often as possible (at every heap block allocation).
5.5. Using SMP parallelism¶
GHC supports running Haskell programs in parallel on an SMP (symmetric multiprocessor).
There’s a fine distinction between concurrency and parallelism: parallelism is all about making your program run faster by making use of multiple processors simultaneously. Concurrency, on the other hand, is a means of abstraction: it is a convenient way to structure a program that must respond to multiple asynchronous events.
However, the two terms are certainly related. By making use of multiple CPUs it is possible to run concurrent threads in parallel, and this is exactly what GHC’s SMP parallelism support does. But it is also possible to obtain performance improvements with parallelism on programs that do not use concurrency. This section describes how to use GHC to compile and run parallel programs, in Parallel and Concurrent we describe the language features that affect parallelism.
5.5.1. Compile-time options for SMP parallelism¶
Blackholing is the act of marking a thunk (lazy computation) as being under evaluation. It is useful for three reasons: firstly it lets us detect certain kinds of infinite loop (the
NonTerminationexception), secondly it avoids certain kinds of space leak, and thirdly it avoids repeating a computation in a parallel program, because we can tell when a computation is already in progress.
-feager-blackholingcauses each thunk to be blackholed as soon as evaluation begins. The default is “lazy blackholing”, whereby thunks are only marked as being under evaluation when a thread is paused for some reason. Lazy blackholing is typically more efficient (by 1-2% or so), because most thunks don’t need to be blackholed. However, eager blackholing can avoid more repeated computation in a parallel program, and this often turns out to be important for parallelism.
We recommend compiling any code that is intended to be run in parallel with the
5.5.2. RTS options for SMP parallelism¶
Use ⟨x⟩ simultaneous threads when running the program.
The runtime manages a set of virtual processors, which we call capabilities, the number of which is determined by the
-Noption. Each capability can run one Haskell thread at a time, so the number of capabilities is equal to the number of Haskell threads that can run physically in parallel. A capability is animated by one or more OS threads; the runtime manages a pool of OS threads for each capability, so that if a Haskell thread makes a foreign call (see Multi-threading and the FFI) another OS thread can take over that capability.
Normally ⟨x⟩ should be chosen to match the number of CPU cores on the machine 1. For example, on a dual-core machine we would probably use
+RTS -N2 -RTS.
Omitting ⟨x⟩, i.e.
+RTS -N -RTS, lets the runtime choose the value of ⟨x⟩ itself based on how many processors are in your machine.
+RTS -maxN3 -RTS, the runtime will choose at most (x), also limited by the number of processors on the system. Omitting (x) is an error, if you need a default use option
Be careful when using all the processors in your machine: if some of your processors are in use by other programs, this can actually harm performance rather than improve it. Asking GHC to create more capabilities than you have physical threads is almost always a bad idea.
-Nalso has the effect of enabling the parallel garbage collector (see RTS options to control the garbage collector).
The current value of the
-Noption is available to the Haskell program via
Control.Concurrent.getNumCapabilities, and it may be changed while the program is running by calling
The following options affect the way the runtime schedules threads on CPUs:
Use the OS’s affinity facilities to try to pin OS threads to CPU cores.
When this option is enabled, the OS threads for a capability \(i\) are bound to the CPU core \(i\) using the API provided by the OS for setting thread affinity. e.g. on Linux GHC uses
Depending on your workload and the other activity on the machine, this may or may not result in a performance improvement. We recommend trying it out and measuring the difference.
Disable automatic migration for load balancing. Normally the runtime will automatically try to schedule threads across the available CPUs to make use of idle CPUs; this option disables that behaviour. Note that migration only applies to threads; sparks created by
parare load-balanced separately by work-stealing.
This option is probably only of use for concurrent programs that explicitly schedule threads onto CPUs with Control.Concurrent.forkOn.
5.5.3. Hints for using SMP parallelism¶
-s [⟨file⟩] RTS option when running the program to see
timing stats, which will help to tell you whether your program got faster by
using more CPUs or not. If the user time is greater than the elapsed time, then
the program used more than one CPU. You should also run the program without
-N ⟨x⟩ for comparison.
The output of
+RTS -s tells you how many “sparks” were created and
executed during the run of the program (see RTS options to control the garbage collector),
which will give you an idea how well your
par annotations are
GHC’s parallelism support has improved in 6.12.1 as a result of much experimentation and tuning in the runtime system. We’d still be interested to hear how well it works for you, and we’re also interested in collecting parallel programs to add to our benchmarking suite.
Whether hyperthreading cores should be counted or not is an open question; please feel free to experiment and let us know what results you find.