Explicit Parallelism via Thread Pools

I’m happy to say that this post is being finished up from my new home in Portland, Oregon! After many years of searching around I’ve finally decided to study at Portland State. Its probably optimisitic of me to think I’ll have more time as a graduate student, but I hope this is the beginning of much more involvement for me in the Haskell community.

Thread Pools from the Control-Engine Package

Control.Engine was recently released on hackage, providing a simple way to instantiate worker threads to split-up the processing of streaming data.  Its was originally developed as a spin-off library from my DHT and I’ve since generalized it to cover numerous cases.

Trivial Thread Pools

The trivial module Control.ThreadPool can cover static examples such as a recent question asked on the haskell-cafe:

I have a function that does some IO (takes a file path, read the file, parse, and return some data), and I would like to parallelize it, so that multiple files can be parsed in parallel.

‘Control.ThreadPool’ gives us an easy answer (but not as slick as the map reduce answer the person was asking for on cafe). First we have our typical fluff.

import Control.ThreadPool (threadPoolIO)
import System.IO (openFile, IOMode(..))
import System.Environment (getArgs)
import Control.Concurrent.Chan
import Control.Monad (forM_)
import qualified Data.ByteString.Lazy.Char8 as L

main = do
    as <- getArgs

As you can see below, we simply say how many threads we want in our thread pool and what action (or pure computation, using ‘threadPool’) we wish to perform. After that its just channels – send input in and read results out!

	(input,output) <- threadPoolIO nrCPU op
	mapM_ (writeChan input) as   -- input stream
	forM_ [1..length as] (\_ -> readChan output >>= print)
  where
  nrCPU = 4
  op f = do
	h <- openFile f ReadMode
	c <- L.hGetContents h
	let !x = length . L.words $ c
	hClose h
	return (f,x)

And while this does nothing to demonstrate paralellism, it does work:

[tom@Mavlo Test]$ ghc -O2 parLines.hs --make -threaded -fforce-recomp
[1 of 1] Compiling Main             ( web.hs, web.o )
Linking web ...
[tom@Mavlo Test]$ find ~/dev/Pastry -name *lhs | xargs ./parLines +RTS -N4 -RTS
("/home/tom/dev/Pastry/Network/Pastry/Module/Joiner.lhs",107)
("/home/tom/dev/Pastry/Network/Pastry/Config.lhs",14)
("/home/tom/dev/Pastry/Network/Pastry/Data/LeafSet.lhs",120)
("/home/tom/dev/Pastry/Network/Pastry/Data/Router.lhs",87)
("/home/tom/dev/Pastry/Network/Pastry/Data/RouteTable.lhs",75)
("/home/tom/dev/Pastry/Network/Pastry/Data/Address.lhs",152)

Control Engine Setup

The thread pools are simple, but what if you need more flexibility or power? What happens if you want to have an up-to-date state shared amoung the threads, or there’s a non-paralizable cheap computation you need to perform before the main operation? The answer is to use Control.Engine instead of Control.ThreadPool. The engine provides managed state, numerous hook location, and an abilty to inject information to mid-engine locations.

Injectors

The inject* calls can bypass the input hooks (injectPreMutator) or bypass everything besides the output hooks (injectPostMutator) – thus creating a ‘result’ that had no corrosponding ‘job’.

Hooks

Hooks are capable of modifying or filtering jobs or results. All hooks are of type state -> a -> IO (Maybe a); its important to note the type can not change and if a hook returns Nothing then the job or result stops there.

Hooks can either be input, pre-mutate, post-mutate, or output. Input and output hooks are ran in series on all jobs or results respectivly; this is intended for low computation tasks that shouldn’t be done in parallel later. Pre and post mutate hooks happen on the (parallel) worker threads before an after the main task, called the mutator.

Mutator

The engine consists of N mutator threads, which is the only operation capable of transforming the jobs into a different (result) type.

State Management

Control.Engine was built with the idea that jobs and state reads were frequent while alterations to the state were rare. A design decision was made to use STM to resolve all contention on state alterations and have a manager watch the TVar for change then bundle those changes in a quicker to read fashion (MVar) for the input, output, and worker threads.

The state provided to the hooks and mutator is always consistent but not guarenteed up-to-date. When modifications to the state occur a transactional variable is modified, which wakes the stateManager; in turn, the state manager updates the state MVar which is read by each thread before processing the next job. In the future IORefs might be used instead of the MVar – all contention is handled by the STM and the only writer for the MVar (future IORef) should be the State Manager.

Web Crawling

Now lets figure out how to help users who need more flexibility using Control.Engine instead of Control.ThreadPool.

MyCatVerbs, from #haskell, suggested a web crawler that uses URls as the job and the mutator (worker) can add all the links of the current page as new jobs while ignoring any URL that was already visited.  Lets start!

The imports aren’t too surprising – tagsoup, concurrent, bloomfilter and Control-Engine are the packages I draw on.

module Main where

import Control.Concurrent (forkIO, threadDelay)
import Control.Concurrent.Chan
import Control.Monad (forever, when)
import Control.Engine			-- Control-Engine
import Control.Exception as X
import Data.BloomFilter 		-- bloomfilter
import Data.BloomFilter.Hash		-- bloomfilter
import Data.BloomFilter.Easy		-- bloomfilter
import Data.IORef
import System.Environment (getArgs)
import Text.HTML.Download		-- tagsoup
import Text.HTML.TagSoup		-- tagsoup

type URL = String

data Job = GetURL URL | ParseHTML URL String deriving (Eq, Ord, Show)

main = do
	(nrCPU:url:_) <- getArgs

The library tries to remain flexible which makes you do a little more work but don’t let that scare you! It needs an IO action to get tasks and an IO action that delivers the results. Most people will probably just want a channel, but sockets or files would do just as well.

	input <- newChan
	output <- newChan

Starting the engine is a one line affair. You provide the number of threads, input, output, a mutator function and initial state. In return you are provided with an ‘Engine’ with which you can modify the hooks and use the injection points.

For this web crawler my ‘state’ is just a bloom filter of all visited URLs so I’ll keep that in the one hook its needed and declare the engine-wide state as a null – (). For the chosen task the mutator needs a way to add more jobs (more URLs) so as pages are parsed any new URLs can be queued for future crawling; this is handled via partial application of mutator funciton.

	eng <- initEngine (read nrCPU) (readChan input) (writeChan output) (mutator (writeChan input)) ()

As mentioned, I’ll use a bloom filter to keep the web crawler from re-visiting the same site many times. This should happen exactly once for each URL and is fairly fast so I’ll insert it as an ‘Input Hook’ which means a single thread will process all jobs before they get parsed out to the parallel thread pool.

	let initialBF = fromListB (cheapHashes nrHashes) nrBits []
            (nrBits, nrHashes) = suggestSizing 100000 (10 ** (-6))
	bf <- newIORef (initialBF,0)
	let bfHook = Hk (uniqueURL bf) 1 "Ensure URLs have not already been crawled"
	addInputHook eng bfHook

Finishing up main, we print all results then provide an initial URL. Notice we run forever – there’s no clean shutdown in this toy example.

	forkIO $ forever $ printResult output
	writeChan input (GetURL url)
	neverStop eng
  where
  neverStop eng = forever $ threadDelay maxBound

And from here on we’re just providing the worker routine that will run across all the threads and we’ll define the input hook. TagSoup performs all the hard work of downloading the page and parsing HTML. Just pull out the <a href=”…”> tags to add the new URLs as jobs before returning any results. In this example I decided to avoid any sort of error checking (ex: making sure this is an HTML document) and simply returning the number of words as a result.

mutator :: (Job -> IO ()) -> st -> Job -> IO (Maybe (URL,Int))
mutator addJob _ (GetURL url) = forkIO (do
	e <- X.try (openURL url) :: IO (Either X.SomeException String)
	case e of
		Right dat -> addJob (ParseHTML url dat)
		_ -> return () )
	>> return Nothing
mutator addJob _ (ParseHTML url dat) = do
	let !urls = getURLs dat
	    !len = length urls
	    fixed = map (\u -> if take 4 u /= "http" then url ++ '/' : u else u) urls
	mapM_ (addJob . GetURL) fixed
	return $ Just (url,len)
  where
  getURLs :: String -> [URL]
  getURLs s = 
	let tags = parseTags s
	in map snd (concatMap hrefs tags)
  hrefs :: Tag -> [(String,String)]
  hrefs (TagOpen "a" xs) = filter ( (== "href") . fst) xs
  hrefs _ = []

printResult :: (Show result) => Chan result -> IO ()
printResult c = readChan c >>= print

Filtering out non-unique URLs is just the bloom filter in action.

uniqueURL :: IORef (Bloom URL, Int) -> st -> Job -> IO (Maybe Job)
uniqueURL _ _ j@(ParseHTML _ _) = return $ Just j
uniqueURL bf _ j@(GetURL url) =  do
	(b,i) <- readIORef bf
	if elemB url b
		then putStrLn ("Reject: " ++ url) >> return Nothing
		else do writeIORef bf (insertB url b, i + 1)
			when (i `rem` 100 == 0) (print i)
			return $ Just j

Performance

P.S. No serious performance measurements have been made beyond extremely expensive (and trivially parallel) problems, so those don’t count.

14Mar2009 EDIT: Said something about Control.Engine before showing the diagram to make reading smoother.
EDIT2: ThreadPool example shown was a version using MD5, not length – oops! Fixed now.

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