It is easy to implement co-routines in Haskell… but only if you know how. No fewer than three people asked me to blog about it, so here’s a quick guide to rolling your own co-routines. To understand this blog, you will need to have a basic understanding of monad transformers.
There are co-routine packages on Hackage, but I have not had much luck with them. The point here, really, is to show you how it all works.
What’s a co-routine?
A co-routine (called a ‘generator’ in Python) is where you create two interleaved flows of control on a single thread. Unlike threads, co-routines switch co-operatively using a ‘yield’ operation. (This is quite a good trick in GUI programming for implementing complex workflow that spans multiple GUI events, since most GUI libraries require everything to be on one thread.)
The example I’m presenting here works in this way: A caller executes a CoroutineT monad transformer, which adds the ‘yield’ operation to the underlying monad (which can be anything). From the caller’s point of view, the ‘yield’ looks like the monad has returned, but with a continuation. In the callee, ‘yield’ appears to block until the caller executes the continuation. In addition, we add the ability to pass a value in both directions.
So we’ve inverted the flow of control in the callee. Continuation passing style (CPS) can also do this, but co-routines are better than CPS because 1. it’s a bit neater, and 2. it allows for recursion.
One application of co-routines is to separate I/O from logic. By way of example I am going to implement an expert system for identifying fruit. I try not to use contrived examples, and as you can see, this time I have completely failed.
In this example, the CoroutineT sits on top of the identity monad, so it’s pure, but the approach works just the same on top of IO or anything else. This example is not deeply nested, but this approach happily supports any level of recursion or nesting.
So here’s our expert system logic. We’ll define CoroutineT shortly. You can read ‘yield’ as ‘askUser’:
type Question = String data Answer = Y | N deriving Eq
type Expert a = CoroutineT Answer Question Identity a
data Fruit = Apple | Kiwifruit | Banana | Orange | Lemon deriving Show
identifyFruit :: Expert Fruit identifyFruit = do yellow <- yield "Is it yellow?" if yellow == Y then do long <- yield "Is it long?" if long == Y then return Banana else return Lemon else do orange <- yield "Is it orange?" if orange == Y then return Orange else do fuzzy <- yield "Is it fuzzy?" if fuzzy == Y then return Kiwifruit else return Apple
Our ‘Expert’ type above…
…specifies the type we are sending into our co-routine (Answer) and the type we are getting out of it (Question) as viewed from the caller.
Now we just need a main program to drive it. Because the I/O is separated out, we can later replace this with a nice touch-screen GUI for the seriously fruit-impaired.
main :: IO () main = do putStrLn $ "Expert system for identifying fruit" run identifyFruit where run :: Expert Fruit -> IO () run exp = handle $ runIdentity $ runCoroutineT exp
handle (Yield q cont) = do putStrLn q l <- getLine case map toLower l of "y" -> run $ cont Y "yes" -> run $ cont Y "n" -> run $ cont N "no" -> run $ cont N _ -> putStrLn "Please answer 'yes' or 'no'" >> handle (Yield q cont)
handle (Result fruit) = do putStrLn $ "The fruit you have is: "++show fruit
When we run our co-routine, it returns with one of these two events:
So how does CoroutineT work?
We’ll start with the types:
data Result i o m a = Yield o (i -> CoroutineT i o m a) | Result a
-- | Co-routine monad transformer -- -- * i = input value returned by yield -- -- * o = output value, passed to yield -- -- * m = next monad in stack -- -- * a = monad return value data CoroutineT i o m a = CoroutineT { runCoroutineT :: m (Result i o m a) }
Hopefully that’s pretty straightforward. ‘yield’ is defined like this:
-- | Suspend processing, returning a @o@ value and a continuation to the caller yield :: Monad m => o -> CoroutineT i o m i yield o = CoroutineT $ return $ Yield o (\i -> CoroutineT $ return $ Result i)
The key point here is that the continuation does nothing except return the value, which is what we want it to do when we run a monad that contains only a yield.
Most of the magic is in the definition of >>=, thus:
instance Monad m => Monad (CoroutineT i o m) where return a = CoroutineT $ return $ Result a f >>= g = CoroutineT $ do res1 <- runCoroutineT f case res1 of Yield o c -> return $ Yield o (\i -> c i >>= g) Result a -> runCoroutineT (g a) -- Pass fail to next monad in the stack fail err = CoroutineT $ fail err
A typical monad would normally execute f then pass its result to g and execute that, and this is in fact exactly what we do in the Result case. Ho hum.
But there’s no law that says you have to execute g. This is Haskell so we can do whatever we like. g is just a plain old closure representing the continuation.
So what we do in the Yield case is take the continuation that executing f gave us, and bind that to the continuation g, then bail out of the monad (in the same way ErrorT does when it gets an error), handing our constructed continuation to the caller. So we end up with a closure that represents the entire execution state of the monad, and it doesn’t matter how deeply nested we are. It just puts the continuation together in the right way as we unravel everything on our way back to the caller.
Here’s the code in downloadable form:
This code is released in the public domain.
Stephen Blackheath, Manawatu, New Zealand
So, the first half day of AusHac2010 was yesterday. We had about 12 people turn up, which isn’t too bad for a Friday.
Erik de Castro Lopo did a lot of work on Ben Lippmeier’s DDC compiler for his Disciple language.
There was some initial work on the Accelerate library for accelerated array computations in Haskell, using various backends. Most of the current work is aiming at making the CUDA backend usable, after which more backends will likely be added, such as an LLVM backend, and possibly an OpenCL backend as well.
Due to the restricted time yesterday, not all that much work was started, but day 2 (see my next post!) has been much more productive.
Uhh, hello. Welcome to my first blog post ever – and thanks Axman6 for letting me be a “guest blogger”.
It’s rather unfashionable on #haskell, but I like XML. So, 18 months ago, I took over the hexpat package from Evan Martin. It was going to be a small project – a simple XML parser binding to Expat. The fastest Haskell XML parser alive. Or so I thought.
It’s become a passion, a way of life. It’s XML parsing in Haskell the way I think it should be done. The best as well as the fastest. (I like to think big.)
I’ve finally finished adding all the features that I and a number of contributors wanted, and I would now like to announce that hexpat is going beta. I want to make this package really, really good, so please help by testing and critiquing. I want to stabilize hexpat, but hexpat-iteratee will be unstable for a while yet.
The future is chunky
The cherry on top of the hexpat galaxy is the still experimental hexpat-iteratee based on Oleg Kiselyov’s iteratee, which is a bit of a hot ticket these days. It provides lazy XML parsing without the practical issues and philosophical dodginess inherent in Haskell’s lazy I/O through functions like hGetContents.
hexpat-iteratee allows for effectful XML processing done in a functional way, and the magic behind this is Yair Chuchem’s humbly named List package. It is “merely” a generalization of lists, and I think it deserves to be a common piece of infrastructure.
The example project is a chunked XML-over-TCP movie database lookup server. Every home should have one. So, let’s start like all good blogs do, with imports:
{-# LANGUAGE OverloadedStrings #-} import Control.Concurrent import Control.Exception import Control.Monad import Control.Monad.IO.Class import Control.Monad.ListT import qualified Data.ByteString as B import qualified Data.ByteString.Unsafe as B (unsafeUseAsCStringLen) import Data.Iteratee import Data.Iteratee.IO.Fd import Data.Iteratee.WrappedByteString import Data.List.Class as List import Data.Maybe import Data.Text (Text) import qualified Data.Text as T import Network import System.IO import System.Posix.IO (handleToFd, fdWriteBuf, closeFd) import System.Posix.Types (Fd) import Text.XML.Expat.Chunked import qualified Text.XML.Expat.Chunked as Tree import Text.XML.Expat.Format import Foreign.Ptr
main :: IO () main = do let port = 6333 putStrLn $ "listening on port "++show port ls <- listenOn $ PortNumber port forever $ do (h, _, _) <- accept ls forkIO $ handleToFd h >>= \fd -> do iter <- parse defaultParserOptions (session (fdPutStrBS fd)) result <- enumFd fd iter >>= run print result `finally` closeFd fd fdPutStrBS :: Fd -> B.ByteString -> IO () fdPutStrBS fd bs = B.unsafeUseAsCStringLen bs $ \(buf, len) -> writeFully (castPtr buf) (fromIntegral len) where writeFully _ len | len == 0 = return () writeFully buf len = do written <- fdWriteBuf fd buf len if written < 0 then fail "write failed" else writeFully (buf `plusPtr` fromIntegral written) (len - written)
fdPutStrBS :: Fd -> B.ByteString -> IO () fdPutStrBS fd bs = B.unsafeUseAsCStringLen bs $ \(buf, len) -> writeFully (castPtr buf) (fromIntegral len) where writeFully _ len | len == 0 = return () writeFully buf len = do written <- fdWriteBuf fd buf len if written < 0 then fail "write failed" else writeFully (buf `plusPtr` fromIntegral written) (len - written)
The handler is a co-routine. When it runs out of input data, it gets suspended, and control returns to enumFd.
session :: (B.ByteString -> IO ()) -- ^ Write output data to socket -> ListOf (UNode IO Text) -- ^ Input XML document -> XMLT IO () session writeOut inputXML = do let outputXML = formatG $ indent 2 $ Element "server" [] (processRoot inputXML) execute $ liftIO . writeOut =<< outputXML return ()
execute here makes all the IO actually happen. It iterates over a List of monadic actions and sequences them. This translates into a sequence of writes of data blocks to the socket. The elements in the list are monadic, so execute also must execute those in order to extract each output ByteString.
In this way, even though processRoot is pure at the top level, it can contain effectful computations.
processRoot :: ListOf (UNode IO Text) -> ListOf (UNode IO Text) processRoot root = do Element _ _ children <- genericTake 1 root child <- children extractElements child where extractElements :: UNode IO Text -> ListOf (UNode IO Text) extractElements elt | isElement elt = processCommand elt `cons` mzero extractElements _ = mzero
The root of the input document is actually given as a List containing one item – the top-level XML tag. The reason why we do this is so that we have to ask for it to be pulled. If it were just passed as a UNode IO Text type, we would have to calculate it before the handler was called, and the handler wouldn’t get a chance to do output before it requests input.
The function is implemented using List’s Monad instance, which behaves exactly like a list monad. The reason for genericTake 1 root is so we stop processing after the root node and don’t wait for a node that will never come. I need to fix this in hexpat-iteratee.
`cons` is the generalized list cons operator like : and `mzero` corresponds to [].
processCommand :: UNode IO Text -> UNode IO Text processCommand elt@(Element "title" _ _) = Element "title" [] $ joinL $ do txt <- textContentM elt return $ search txt processCommand (Element cmd _ _) = Element "unknown" [("command", cmd)] mzero
joinL is a bit of List magic that lets you drop down into the underlying monad, which in this case is XMLT IO a. joinL’s type is :: ItemM l (l a) -> l a where ItemM l is a type function giving the list’s monad. So, the stuff after joinL resolves to a type of :: XMLT IO (ListOf (UNode IO Text)).
search :: Text -> ListOf (UNode IO Text) search key = joinL $ do iter <- liftIO $ parse defaultParserOptions $ \root -> do let l = do elt@(Element _ _ children) <- genericTake 1 root movie <- List.filter isElement children return movie execute l return l eMovies <- liftIO $ fileDriver iter "movies.xml" case eMovies of Left err -> fail $ "failed to read 'movies.xml': "++show err Right movies -> return $ List.filter matches movies where matches elt = key `T.isInfixOf` fromMaybe "" (getAttribute elt "title")
So, I build and run the server, and here is the result, using Unix’s nc command as my client. I typed this:
<a> <title>of the</title>
<?xml version="1.0" encoding="UTF-8"?> <server> <title> <movie id="dvzrwfvryd" disc="41" title="War of the Worlds (2005)" director="Steven Spielberg" genre="Sci Fi Thriller" rating="6" description="Tom Cruise alert" imdbID="tt0407304"/> <movie id="xxvjgxpokp" disc="44" title="Shaun of the Dead" director="Edgar Wright" genre="Comedy Horror" rating="8" description="British send-up zombie movie" imdbID="tt0365748"/> <movie id="duvcjsygqi" disc="104" title="March of the Penguins (La Marche de l'empereur)" director="Luc Jacquet" genre="Documentary" description="" imdbID="tt0428803"/> <movie id="dawcezoiro" disc="109" title="Pirates of the Caribbean: Dead Man's Chest" director="Gore Verbinski" genre="Action/Comedy" rating="7" description="" imdbID="tt0383574"/> </title>
And the session can process more commands interactively.
And pickled
I should also mention my related hexpat-pickle package which is a shameless rip-off of the picklers from Uwe Schmidt’s excellent hxt package. I find it a very practical and quick way to bang out XML picklers. (It doesn’t work with hexpat-iteratee yet.)
Bye bye
Here’s the code in downloadable form. Make sure you use the monads-fd and transformers packages instead of mtl. Also hexpat-iteratee and text.
I hope you found this interesting. I hope the XML haters of #haskell will be miraculously transformed into XML tolerators, and I hope you’ll help me improve hexpat. – Stephen Blackheath, Manawatu, New Zealand
Over the last week or so, Ivan Miljenovic and I have been busy organising AusHac2010. We’ve made a lot of progress, and are announcing the dates as the 16th-18th of July. If you’d like to come along and work on projects like:
then please put your name down on the sign up page.
This should be a great opportunity for Aussie (and non aussie!) haskell hackers to come and meet all those people you know from Planet Haskell and #haskell, and give something back to the community, while having a great time.
Hope to see you there, – Alex Mason
In my previous post about why I love the cereal package, I went through the development of a bencoding parser and encoder. Brian was kind enough to point out some of the flaws I’d made in this code (which I should add had been caused from me not actually checking the spec while writing the code, obviously a bad idea), and from these comments, I think I’ve managed to fix most of the problems:
Hi, thanks for writing this stuff. I think it could be pretty cool, but it could benefit from more precise reading and implementation of the spec. For example, bencoded integers can be negative. Also, my alarms go off whenever I see ‘read’. In ‘getBString’, you pass ‘read count’ to ‘getByteString’, which expects Int. But check, e.g., ‘read (show $ 2^64-1) :: Int’ in ghci. So if the torrent data is malformed, you could end up passing a negative length to ‘getByteString’. Maybe it knows how to deal with that, but it’s not something you should rely on. You also have to decide what to do about dictionaries you read whose keys aren’t in order, etc. Basically, please be more precise, especially if you put this on Hackage. This stuff is supposed to be industrial strength. Thanks.
The first problem, not handling negative integers was pretty trivial to fix, all I needed to do was check to see if there was a ‘-’ char out the front, and if not, just get all the digits, and then read them:
-- | Parses a BInt getBInt :: Get BCode getBInt = BInt . read <$> getWrapped 'i' 'e' intP where intP = ((:) <$> char '-' <*> getDigits) <|> getDigits
Brian also pointed out something I also wasn’t particularly happy with, the use of read to read in an Int64. This should under normal circumstances be more than large enough to read any bytestring that should be in any bencoded data (.torrent files are usually less than 1-200KB), so we should never have run into a problem here, but it’s still good to make sure we can be ‘industrial strength’:
read
-- | Parses a BString getBString :: Get BCode getBString = do count <- getDigits BString <$> ( char ':' *> getStr (read count :: Integer)) where maxInt = fromIntegral (maxBound :: Int) :: Integer getStr n | n >= 0 = B.concat <$> (sequence $ getStr' n) | otherwise = fail $ "read a negative length string, length: " ++ show n getStr' n | n > maxInt = getByteString maxBound : getStr' (n-maxInt) | otherwise = [getByteString . fromIntegral $ n]
Here you can see we’re now using an Integer as the read value, and taking chunks of maxBound :: Int bytes, until there are less than that many bytes left to fetch.
Integer
maxBound :: Int
I’ve decided to ignore the problem with dictionaries with out of order elements, I can see this being something others may have overlooked in their implementations, and it’s entirely possible that other implementations do not put the keys in the right order. Our implementation does, but can easily handle malformed implementations. I see this is a bonus, and I hope others do too (I feel the code is more robust, and that’s always good).
I hope this has made some difference to the code, and what people think of it.
Until next time,
– Axman
Cereal, as you may know from my previous posts is a library for parsing binary data from strict ByteStrings. It is very similar to the binary package, but importantly, provides both an Alternative instance, and an Either String a return type for the decode function, which tells you where the parse failed.
Either String a
I’ve been playing around with cereal lately in jlouis’ haskell-torrent project, rewriting the various binary parsing and producing parts of the program (the torrent file parser, and the wire protocol parser). I though it would be nice to share some of the code used for these, to demonstrate how easy cereal makes it to do such things.
To begin with, I’ll show you the part that decodes and encodes torrent files (if needed in the future). Torrent files are encoded using a very simple encoding, known as bencoding, which consists of four major primitives: Integral numbers, Strings of bytes, Arrays of bencoded objects, and Dictionaries of String, bencoded object pairs. This is very nicely represented using this datatype:
-- | BCode represents the structure of a bencoded file data BCode = BInt Integer -- ^ An integer | BString B.ByteString -- ^ A string of bytes | BArray [BCode] -- ^ An array | BDict (M.Map B.ByteString BCode) -- ^ A key, value map deriving Show
the specification for bencoded data goes something like this:
Integers are encoded as the ASCII character for ‘i’ as a byte, followed by the digits of the integral value, terminated by the ASCII byte for ‘e’. Eg: the number ‘42’ would be encoded as “i42e” Strings are encoded as the digits of their length, followed by a colon (‘:’), then the bytes of the string. these strings are really just byte sequences, and probably shouldn’t be treated as having an encoding (as jlouis and I found out when I tried to test the current code on GHC 6.12.1, with the BString type using Strings, instead of ByteStrings, and finding out that the simple test contained byte sequences that could not be represented as Strings). Eg: the string “hello” would become “5:hello”, “hello world” would become “11:hello world” Arrays are encoded as ASCII ‘l’ (for list I believe), followed by any number of bencoded objects, terminated by an ASCII ‘e’. (This is where using binary became difficult, as you had to explicitly check whether you had reached the terminating ‘e’ using lookAhead when parsing before attempting to parse another bencoded object, du the the lack of actual failure handling) Eg: ["Hello", 123] would become “l5:helloi123ee”. Notice how we’ve used the previous definitions for integral numbers, and strings. Dictionaries are encoded as an ASCII ‘d’, followed by the String, object pairs, followed by an ASCII ‘e’. Eg: fromList [("test",123),("arr",[1,2,"hello"])] would become “d4:testi123e3:arrli1ei2e5:helloee”. It looks a bit of a mess, but it is quite efficient.
Integers are encoded as the ASCII character for ‘i’ as a byte, followed by the digits of the integral value, terminated by the ASCII byte for ‘e’.
Eg: the number ‘42’ would be encoded as “i42e”
Strings are encoded as the digits of their length, followed by a colon (‘:’), then the bytes of the string. these strings are really just byte sequences, and probably shouldn’t be treated as having an encoding (as jlouis and I found out when I tried to test the current code on GHC 6.12.1, with the BString type using Strings, instead of ByteStrings, and finding out that the simple test contained byte sequences that could not be represented as Strings).
Eg: the string “hello” would become “5:hello”, “hello world” would become “11:hello world”
Arrays are encoded as ASCII ‘l’ (for list I believe), followed by any number of bencoded objects, terminated by an ASCII ‘e’. (This is where using binary became difficult, as you had to explicitly check whether you had reached the terminating ‘e’ using lookAhead when parsing before attempting to parse another bencoded object, du the the lack of actual failure handling)
lookAhead
Eg: ["Hello", 123] would become “l5:helloi123ee”. Notice how we’ve used the previous definitions for integral numbers, and strings.
Dictionaries are encoded as an ASCII ‘d’, followed by the String, object pairs, followed by an ASCII ‘e’.
Eg: fromList [("test",123),("arr",[1,2,"hello"])] would become “d4:testi123e3:arrli1ei2e5:helloee”.
It looks a bit of a mess, but it is quite efficient.
When writing my Serialize instance (Cereal’s version of the Binary class) for the BCode type, I decided it would be much easier to write the put methods first. This turned out to be rather straight forward, once I’d written a few helper functions.
toW8 :: Char -> Word8 toW8 = fromIntegral . ord fromW8 :: Word8 -> Char fromW8 = chr . fromIntegral toBS :: String -> B.ByteString toBS = B.pack . map toW8 fromBS :: B.ByteString -> String fromBS = map fromW8 . B.unpack -- | Put an element, wrapped by two characters wrap :: Char -> Char -> Put -> Put wrap a b m = do putWord8 (toW8 a) m putWord8 (toW8 b) -- | Put something as it is shown using @show@ putShow :: Show a => a -> Put putShow x = mapM_ put (show x)
fromW8 :: Word8 -> Char fromW8 = chr . fromIntegral
toBS :: String -> B.ByteString toBS = B.pack . map toW8
fromBS :: B.ByteString -> String fromBS = map fromW8 . B.unpack
-- | Put an element, wrapped by two characters wrap :: Char -> Char -> Put -> Put wrap a b m = do putWord8 (toW8 a) m putWord8 (toW8 b)
-- | Put something as it is shown using @show@ putShow :: Show a => a -> Put putShow x = mapM_ put (show x)
With these in hand, I set to work implementing the put function. The Integer and Array functions were straight forward:
instance Serialize BCode where put (BInt i) = wrap 'i' 'e' $ putShow i put (BArray arr) = wrap 'l' 'e' . mapM_ put $ arr
The Dictionary and String implementations weren’t too bad either:
put (BDict mp) = wrap 'd' 'e' dict where dict = mapM_ encPair . M.toList $ mp encPair (k, v) = put (BString k) >> put v put (BString s) = do putShow (B.length s) putWord8 (toW8 ':') putByteString s
As you can see, the code is quite clear, and matches the specification quite well.
Parsing the data was the next step. this proved a little more difficult, but with my recent (shallow) experience with Parsec, I knew what was needed.
I decided to start by writing some useful combinators (this is a lie, I wrote them when needed, but lying makes the post flow better >_>). These included the following:
-- | Get a Char. Only works with single byte characters getCharG :: Get Char getCharG = fromW8 <$> getWord8 -- | Parse a given character char :: Char -> Get () char c = do x <- getCharG if x == c then return () else fail $ "Expected char: '" ++ c:"' got: '" ++ [fromW8 x,'\''] -- | Get something wrapped in two Chars getWrapped :: Char -> Char -> Get a -> Get a getWrapped a b p = char a > p < char b -- The same as char a >> p >>= \x -> char b >> return x -- | Parse zero or items using a given parser many :: Get a -> Get [a] many p = many1 p mplus return [] -- | Parse one or more items using a given parser many1 :: Get a -> Get [a] many1 p = (:) <$> p <*> many p -- | Returns a character if it is a digit, fails otherwise. uses isDigit. digit :: Get Char digit = do x <- getCharG if isDigit x then return x else fail $ "Expected digit, got: " ++ show x -- | Get one or more digit characters getDigits :: Get String getDigits = many1 digit
-- | Parse a given character char :: Char -> Get () char c = do x <- getCharG if x == c then return () else fail $ "Expected char: '" ++ c:"' got: '" ++ [fromW8 x,'\'']
-- | Get something wrapped in two Chars getWrapped :: Char -> Char -> Get a -> Get a getWrapped a b p = char a > p < char b -- The same as char a >> p >>= \x -> char b >> return x
-- | Parse zero or items using a given parser many :: Get a -> Get [a] many p = many1 p mplus return []
mplus
-- | Parse one or more items using a given parser many1 :: Get a -> Get [a] many1 p = (:) <$> p <*> many p
-- | Returns a character if it is a digit, fails otherwise. uses isDigit. digit :: Get Char digit = do x <- getCharG if isDigit x then return x else fail $ "Expected digit, got: " ++ show x
-- | Get one or more digit characters getDigits :: Get String getDigits = many1 digit
My favourite two definitions here are many and many1, which nicely show the use of Alternative: they are mutually recursive, with many1 being the only one of the two to actually do and parsing, while many checks to see if many1 failed to parse one object using the parser p. It’s really quite beautiful, and makes the code that follows a hell of a lot nicer to write. This is where the love mentioned in the title comes in by the way.
many
many1
With these in hand, I could now go ahead and write the actual parsers for various BCode types. Parsing BInts and BArrays is dead simple now:
-- | Parses a BInt getBInt :: Get BCode getBInt = BInt . read <$> getWrapped 'i' 'e' getDigits -- | Parses a BArray getBArray :: Get BCode getBArray = BArray <$> getWrapped 'l' 'e' (many get)
-- | Parses a BArray getBArray :: Get BCode getBArray = BArray <$> getWrapped 'l' 'e' (many get)
As as side note, I’ve now come to see just what the folks on #haskell were on about when they said Applicative is nice. I think I’ve fallen in love (yet again!).
BStrings were a little more difficult, but not hard, given what I’ve just written:
-- | Parses a BString getBString :: Get BCode getBString = do count <- getDigits BString <$> ( char ':' *> getByteString (read count))
Here we get as many digits as we can, followed by a colon, and then take the number of bytes the digits specified. Finally, we have the BDict definition, which also is quite nice, if slightly annoying with its use of pattern matching (don’t get me wrong, i love pattern matching, but it’s the only place it’s used in the parser )
-- | Parses a BDict getBDict :: Get BCode getBDict = BDict . M.fromList <$> getWrapped 'd' 'e' (many getPairs) where getPairs = do (BString s) <- getBString x <- get return (s,x)
Putting it all together, we finally have a definition for the get function in the Serialize class.
get = getBInt <|> getBArray <|> getBDict <|> getBString
Please, I implore you, do let me know what you think of this all, I’m always interested in seeing what others think of my code, and ways to improve it.
— Axman
Over the last week or so, I’ve been playing around with some of the minor details of jlouis’ Haskell-Torrent code. The main pieces I’ve been playing with have been the more binary centric pieces: the torrent file en/decoding and since Wednesday, the wire protocol.
After trying to compile the code using GHC 6.12.1, I noticed that a rather strange dependency: HCodecs. Investigating further, I realised that all that was being used was its fairly nice binary builder and parser, and was being used in the parsing and construction of bencoded files. This seemed to me to be the domain of one of my favourite packages, binary. I also noticed that when trying to parse the sample torrent file jlouis had provided, there was binary data that could not be represented in a String type, because there were byte sequences that could not be parses into Char’s.
So I went off to try and implement the BCode module using Data.Binary. The encoding was really simple and straight forward, but parsing was another matter. Binary has no real error handeling, which meant that decoding things like lists where you keep adding elements to the list until you reach an ‘e’, was made quite verbose due to this fact. After implementing a (semi) working module, I went in search of a more parsec like interface to binary parsing. This is when someone on #Haskell informed me of cereal, a package based on binary, which added an Alternative instance, and an Either String a return type for better error reporting.
I fell in love. This was exactly what I was after, I could create my own combinators like many and many1, with the same ease I could with parsec. Using cereal, I was able to make a really clear, concise parser, which should also be quite efficient.
I will post some of this code at a later time, when i’m not writing on my iPhone, and show you just what I mean. I’m also considering releasing the BCode module as a package of it’s own, if anyone else feels it might me useful to them. Either that, or I might write up a module of useful combinators for use with cereal.
Until next time, happy new year and I hope this year treats you all better than the last one did.
– Al
A while ago I found Björn Edström’s quick intro to the Haskell FFI , and I decided to follow along and find out what it was to interface Haskell with C. I found it quite interesting, noticed the C was faster than the haskell version I wrote, and then sort of forgot about it all.
Then Snow Leopard came out. I read the Ars Technica review of it, and realised that Grand Central Dispatch was frigging awesome, because it makes doing things concurrently in C damn easy. I went off and implemented a few programs using it, and had a lot of fun. It was especially interesting because I knew we were coming up to learning about concurrency in C in our Concurrent and Distributed Systems course. I’d heard about pthreads, and could see they were not going to be pleasant to use, at least not without some higher level interface. Blocks along with GCD are obviously an improvement, though certainly not an alternative.
It was about then that I realised two things: Firstly, I could parallelise the dctiv function from from Björn’s article using GCD, and secondly, that I was taking a Signals Processing course, and I actually understood what the code was doing. So off I went, turning this code:
void dctiv(int points, float *in, float *out){ int k, n; float sum, cosConst, kk; for (k = 0;k < points; k++ ){ sum = 0; kk = (k + 0.5f); cosConst = (PI/points) * kk; for (n = 0; n < points; n++){ sum += in[n] * cos(cosConst * (n+0.5f)); } out[k] = sum; } }
#include <dispatch/dispatch.h> ... void dctivGCD(int points, float *in, float *out){ dispatch_apply(points, dispatch_get_global_queue(0,0), ^(size_t k){ float sum, cosConst, kk; int n; sum = 0; kk = (k + 0.5f); cosConst = (PI/points) * kk; for (n = 0; n < points; n++){ sum += in[n] * cos(cosConst * (n+0.5f)); } out[k] = sum; }); }
So I went off and benchmarked this new code, and found that on my Core 2 Duo, it was about 40% faster. Hoorah! (for some reason, I can now not get this code to execute on more than one thread, so I’m no longer getting any speedups But that’s another story). That was all well and good, but where to go next?
While I think GCD is by far the most important feature of Snow Leopard, it also has another related feature that has stirred a lot of attention in the in the lead up to Snow Leopard’s release: OpenCL, the Open Computing Language. It is supposed to provide a common interface allowing a programmer to take advantage of parallelism on their CPU, GPU, Cell or what have you. I’d gotten somewhat interested by this, and decided to take a look at the Apple OpenCL examples. I’d thought that OpenCL would be hard to use, and I was right. Luckily, the OpenCL Hello World example was an example of something that was very close to what I was doing in the dctiv function. I decided to give OpenCL a whirl, and copied and pasted the most of the hello.c file into a new function, modified it… and it didn’t work. I’m not going to go into why it didn’t work, because it was mainly my stupidity, but I will say that if you if you’re working with floats in C/OpenCL, make sure your constants are in the form of 0.5f.
After some work, I finally got my kernel to compile:
const char KernelSource = "\n" \ "__kernel void DCT( \n" \ " __global const float input, \n" \ " __global float* output, \n" \ " const unsigned int count) \n" \ "{ \n" \ " int n, i = get_global_id(0); \n" \ " float sum, cosConst, kk; \n" \ " \n" \ " sum = 0; \n" \ " kk = (i + 0.5f); \n" \ " cosConst = (3.141592653589793f/count) * kk; \n" \ " for(n = 0; n < count; ++n) \n" \ " { \n" \ " sum += input[n] * cos(cosConst * (n+0.5f)); \n" \ " } \n" \ " output[i] = sum; \n" \ "} \n" \ "\n";
I had also made a FFI binding to the function that ran this (left out, because it’s 149 lines long). I started benchmarking, and couldn’t quite believe the speed: on 215 elements, the C takes 44.43s, the OpenCL version on the same input takes 7.11s. I’m a little disappointed with my Haskell versions, running about half as fast as the C version. For some reason I can’t seem to get them to actually run in parallel, it’s rather frustrating. For those interested, here are the two haskell versions:
dctivHask :: Int -> [Float] -> [Float] dctivHask points ps = parMap rnf forK [0..fromIntegral (points-1)] where !piOn = myPi / fromIntegral points forK k = foldl' (f $ (k+0.5) piOn) 0 $ zipWith V [0.0..] ps f !kp !sum' (V !n !p) = sum' + p cos (kp * (n+0.5))
Which takes about 71.22s, and using unboxed arrays:
dctivHaskArr :: Int -> [Float] -> [Float] dctivHaskArr points ps = pmap f . range . bounds $ input where input = listArray (0,points-1) ps :: UArray Int Float f :: Int -> Float f !k = g 0.0 0 where cosConst = (myPi / fromIntegral points) (0.5 + fromIntegral k) :: Float g :: Float -> Int -> Float g !num !n = {-# SCC "dctivHaskArr-worker" #-} let nn = 0.5 + fromIntegral n :: Float in if n < points then g (num + ((input ! n) cos (cosConst * nn))) (n+1) else num
Which takes about 66.96s (these are just one off test numbers because I get bored waiting for the haskell versions to finish).
I should probably follow up this post with… more stuff… so stay tuned (and please leave comments, because i don’t know what that stuff should be yet!)
This isn’t an announcement, but a discussion of the next project I’d like to try writing in haskell. I’ve been thinking of ways in which I can use my TernaryTrees package, and I realised that a proxy server could be a nice way to do that.
I’m still trying to think through the details of what needs to be implemented, and how it should all work, but I’d like to it be something concurrent, with one thread handling each request to fetch objects if needed, and another thread keeping track of a TernaryMap (Or maybe I’ll release the StringMap in the TernaryTrees package that’s in the repo), which can be used to keep track of which elements have been cached, and other info about the objects. It occurs to me that this job seems ideally suited to my AVar package, which is a concurrent ‘variable’, that can be used by many concurrent threads, and will do as much as it can to stop itself from being destroyed by exceptions.
I think there’s a few things I’ll need to add to TernaryTrees before I can get a nice proxy working, mainly a delete function to remove elements from the trees. I’ll probably have to do some more work on AVars before i can use them too. But my main problem is not knowing how to do anything to do with HTTP, both fetching and serving. I’ll be taking a look at the HTTP and HTTP-server libraries to see what i can figure out, probably by starting with seeing if i can fetch some data, save it to a file, and send it on to somewhere else.
I’d really live to hear from others about this if they read it, and give me any ideas you may have had about the subject.
Yesterday, I submitted my first program modification to the computer language shootout game, which is a change to the thread-ring program. If you’ve followed the shootout at all, you will probably know that haskell dominates at this benchmark, with the next contender (Mozart/OZ) being 50% slower than haskell already.
While i was having a look at the code, I noticed that the haskell entry was forkIO’ing a lot of threads, which is exactly what we want. But we weren’t quite forking enough; one of the 503 threads was the main thread. Bad idea!
I learnt a few weeks ago, while working on my AVar package, that communicating between bound and non bound threads (forkIO’d vs. main) when using things like MVars can be really slow. And this is exactly what was happening:
main = do a <- newMVar . read . head =<< getArgs z <- foldM new a [2..ring] thread 1 z a
as you can see, the last/first thread was being run in main, so once per loop around the ring, we were talking to main via an MVar, and then talking to another forkIO’d thread via another MVar. So here’s what I did:
main = do a <- newMVar . read . head =<< getArgs z <- foldM new a [2..ring] ret <- newEmptyMVar forkIO (thread 1 z a >>= putMVar ret) takeMVar ret
Two and a half lines of code. What were the results? Well, when the program was run with n = 50,000,000, the original ran in ~14.2s, with -N{1,2,3,4}, my modified one took on average ~9.3s, which is about a 33% improvement. It should be noted that when both programs are compiled without -threaded, there is almost no difference in speed at all (and they both run considerably faster than the non threaded version). Need to talk to the shootout peeps and find out if it’s against the rules to compile without -threaded, and get an even more massive speed boost.
