Haskell for multicores
This site attempts to document all our available information on exploiting such hardware with Haskell.
Throughout, we focus on exploiting shared-memory SMP systems, with aim of lowering absolute wall clock times. The machines we target are typical 2x to 32x desktop multicore machine, on which vanilla GHC will run.
- 1 Introduction
- 2 Thread primitives
- 3 Synchronisation with locks
- 4 Message passing channels
- 5 Lock-free synchronisation
- 6 Asynchronous messages
- 7 Parallelism strategies
- 8 Data parallel arrays
- 9 Foreign languages calls and concurrency
- 10 Profiling and measurement
To get an idea of what we aim to do -- reduce running times by exploiting more cores -- here's a naive "hello, world" of parallel programs: parallel, naive fib. It simply tells us whether or not the SMP runtime is working:
import Control.Parallel import Control.Monad import Text.Printf cutoff = 35 fib' :: Int -> Integer fib' 0 = 0 fib' 1 = 1 fib' n = fib' (n-1) + fib' (n-2) fib :: Int -> Integer fib n | n < cutoff = fib' n | otherwise = r `par` (l `pseq` l + r) where l = fib (n-1) r = fib (n-2) main = forM_ [0..45] $ \i -> printf "n=%d => %d\n" i (fib i)
We compile it with the `-threaded` flag:
$ ghc -O2 -threaded --make fib.hs [1 of 1] Compiling Main ( fib.hs, fib.o ) Linking fib ...
And run it with:
where 'x' is the number of cores you have (or a slightly higher value). Here, on a quad core linux system:
./fib +RTS -N4 76.81s user 0.75s system 351% cpu 22.059 total
So we were able to use 3.5/4 of the available cpu time. And this is typical, most problems aren't easily scalable, and we must trade off work on more cores, for more overhead with communication.
- GHC's multiprocessor guide
- runtime options to enable SMP parallelism
- API documentation for paralell strategies
- Real World Haskell: Concurrent and Parallel Programming
- Blog posts about parallelism
TODO - finish
For explicit concurrency and/or parallelism, Haskell implementations have a light-weight thread system that schedules logical threads on the available operating system threads. These light and cheap threads can be created with forkIO. Full OS threads will not be discussed here beyond saying they pose a significantly higher overhead, but you create them using forkOS if truly needed.
forkIO :: IO () -> IO ThreadId
Lets take a simple Haskell application that hashes two files and prints the result:
import Data.Digest.Pure.MD5 (md5) import qualified Data.ByteString.Lazy as L import System.Environment (getArgs) main = do [fileA, fileB] <- getArgs hashAndPrint fileA hashAndPrint fileB hashAndPrint f = L.readFile f >>= return . md5 >>= \h -> putStrLn (f ++ ": " ++ show h)
This is a straight forward solution that hashs the files one at a time printing the resulting hash to the screen. What if we wanted to use more than one processor to hash the files in parallel?
One solution is to start a new thread, hash in parallel, and print the answers as they are computed:
import Control.Concurrent (forkIO) import Data.Digest.Pure.MD5 (md5) import qualified Data.ByteString.Lazy as L import System.Environment (getArgs) main = do [fileA,fileB] <- getArgs forkIO $ hashAndPrint fileA hashAndPrint fileB hashAndPrint f = L.readFile f >>= return . md5 >>= \h -> putStrLn (f ++ ": " ++ show h)
Now we have a rough program with reasonable great performance boost, which is expected given the trivially parallel computation.
But wait! You say there is a bug? Two, actually. One is that if the main thread is finished hashing fileB first, the program will exit before the child thread is done with fileA. The second is a potential for garbled output due to two threads writing to stdout. Both these problems can be solved using some inter-thread communication - we'll pick this example up in the MVar section.
- A concurrent port scanner
- Research papers on concurrency in Haskell
- [http://haskell.org/haskellwiki/Research_papers/Parallelism_and_concurrency#Parallel_Haskell Research papes on parallel Haskell
Synchronisation with locks
Message passing channels
- Async exceptions
- Parallel, pure strategies
Data parallel arrays
Foreign languages calls and concurrency
Non-blocking foreign calls in concurrent threads.
Profiling and measurement