Crypto-API Released

September 7, 2010

Crypto-API (hackage, haddock) is now on Hackage.

Crypto-API is a generic interface for cryptographic operations, platform independent quality Entropy, property tests and known-answer tests (KATs) for common algorithms, and a basic benchmark infrastructure. Maintainers of hash and cipher implementations are encouraged to add instances for the classes defined in Crypto.Classes. Crypto users are similarly encouraged to use the interfaces defined in the Classes module.

Previous blogs on crypto-api have discussed its design and the RNG interface. These were to aid design discussion, so note the code there won’t work without minor changes.

Example: Hashes

An example class instance:

 instance Hash MD5Context MD5Digest where
        outputLength = Tagged 128
        blockLength  = Tagged 512
        initialCtx   = md5InitialContext
        updateCtx    = md5Update
        finalize     = md5Finalize

The hash user can remain agnostic about which type of hash is used:

 authMessage :: Hash ctx dgst => B.ByteString -> MacKey -> dgst -> Bool
 authMessage msg k = (==) (hmac' k msg)

 hashFile :: Hash c d => FilePath -> IO d
 hashFile = liftM hash L.readFile

Example: Block Cipher

Users of block cipher instances probably want to use Crypto.Modes:

 import Crypto.Classes
 import Crypto.Modes (cbc)
 import Data.Serialize (encode)

 cipherMsgAppendIV :: (BlockCipher k) => k -> B.ByteString -> IO B.ByteString,
 cipherMsgAppendIV msg = do
     iv <- getIVIO
     return $ B.append (encode iv) (cbc k iv msg)

Example RNG

Its easy to get a DRBG (aka PRNG) that can be used for generating seed material for keys, building asymmetric keys, obtaining initialization vectors, nonces, or many other uses. See Crypto.Random (which users System.Crypto.Random for entropy):

newGenIO :: CryptoRandomGen g => IO g
genBytes :: (CryptoRandomGen g) => g -> ByteLength -> Either GenError (ByteString, g)
getIV :: (CryptoRandomGen g, BlockCipher k) => g -> Either GenError (IV k, g)
buildKeyPair :: CryptoRandomGen g => g -> BitLength -> Maybe ((p, p), g)


A quick peek in the Test.Crypto module will show you that testing is decent (particularly for AES) and getting better all the time.

Given a BlockCipher instance the entire test code for an AES implementation is:

-- Omitting hack-job instances for SimpleAES in this snippet
 main = do
         ts <- makeAESTests (AESKey $ B.replicate 16 0)
         runTests ts

This automatically reads in hundreds of NIST Known Answer Tests (KATs) and checks the implementation. A lesser infrastructure exists for testing Hashes. Cipher property tests are still needed.

Example: Benchmarking

As with tests, benchmarking is quite simple:

 import Data.Digest.Pure.MD5
 import Benchmark.Crypto
 import Criterion.Main

 main = defaultMain [benchmarkHash (undefined :: MD5Digest) "pureMD5"]


So please, if you maintain a hash, cipher, or other cryptographic primitive please add instances for the crypto-api classes. If you need these primitives then consider using the crypto-api interfaces, allowing you to remain algorithm and implementation agnostic in all your low level code.

RandomGen – The Old Solution

Mathematicians talk about random bits and many programmers talk about streams of random bytes (ex: /dev/urandom, block cipher counter RNGs), so its a bit odd that Haskell adopted the RandomGen class, which only generates random Ints. Several aspects of RandomGen that are non-ideal include:

  • Only generates Ints (Ints need to be coerced to obtain other types)
  • By virtue of packaging it is often paired with StdGen, a sub-par generator
  • Mandates a ‘split’ operation, which is non-sense or unsafe for some generators (as BOS pointed out in a comment on my last post)
  • Doesn’t allow for generator failure (too much output without a reseed) – this is important for cryptographically secure RNGs
  • Doesn’t allow any method for additional entropy to be included upon request for new data (used at least in NIST SP 800-90 and there are obvious default implementations for all other generators)

Building Something Better

For these reasons I have been convinced that building the new crypto-api package on RandomGen would be a mistake. I’ve thus expanded the scope of crypto-api to include a decent RandomGenerator class. The proposal below is slightly more complex than the old RandomGen, but I consider it more honest (doesn’t hide error conditions / necessitate exceptions).

class RandomGenerator g where
        -- |Instantiate a new random bit generator
        newGen :: B.ByteString -> Either GenError g

        -- |Length of input entropy necessary to instantiate or reseed a generator
        genSeedLen :: Tagged g Int

        -- |Obtain random data using a generator
        genBytes        :: g -> Int -> Either GenError (B.ByteString, g)

        -- |'genBytesAI g i entropy' generates 'i' random bytes and use the
        -- additional input 'entropy' in the generation of the requested data.
        genBytesAI      :: g -> Int -> B.ByteString -> Either GenError (B.ByteString, g)
        genBytesAI g len entropy =
                ... default implementation ...

        -- |reseed a random number generator
        reseed          :: g -> B.ByteString -> Either GenError g

Compared to the old RandomGen class we have:

  1. Random data comes in Bytestrings. RandomGen only gave Ints (what is that? 29 bits? 32 bits? 64? argh!), and depended on another class (Random) to build other values. We can still have a ‘Random’ class built for RandomGenerator – should we have that in this module?
  2. Constructing and reseeding generators is now part of the class.
  3. Splitting the PRNG is now a separate class (not shown)
  4. Generators can accept additional input (genBytesAI). Most generators probably won’t use this, so there is a reasonable default implementation (fmap (xor additionalInput) genBytes).
  5. The possibility to fail – this is not new! Even in the old RandomGen class the underlying PRNGs can fail (the PRNG has hit its period and needs a reseed to avoid repeating the sequence), but RandomGen gave no failure mechanism. I feel justified in forcing all PRNGs to use the same set of error messages because many errors are common to all generators (ex: ReseedRequred) and the action necessary to fix such errors is generalized too.

    In Closing

    The full Data.Crypto.Random module is online and I welcome comments, complaints and patches. This is the class I intend to force users of the Crypto API block cipher modes and Asymmetric Cipher instances to use, so it’s important to get right!


January 19, 2010

During my “Linux Kernel Modules with Haskell” tech talk I mentioned my next personal project (on my already over-full plate) would be to play with Haskell on ARM.  I’m finally getting around to a little bit of playing!  Step zero was to get hardware so I acquired a touchbook – feel free to ignore all the marketing on that site (though I am quite happy with it) and just understand it is the equivalent of a beagleboard with keyboard, touch-pad, touch screen, speakers, wifi, bluetooth, two batteries, more USB ports, and a custom Linux distribution.

Step 1: Get an Unregistered Build

To start its best to bypass the porting GHC instructions and steal someone elses porting effort in the form of a Debian package (actually, three debian packages).  Convert them to a .tar.gz (unless you have a debian OS on your ARM system) using a handy deb2targz script.  Now untar them onto your ARM system via “sudo tar xzf oneOfThePackages.tar.gz -C /” .  Be sure to copy the package.conf  as it seems to be missing from the .debs “sudo cp /usr/lib/ghc-6.10.4/package.conf.shipped /var/lib/ghc-6.10.4/package.conf”.   After all this you should have a working copy of GHC 6.10.4 – confirm that assertion by compiling some simple test programs.

I now have my copy of 6.10.4 building GHC 6.12.1.  The only hitch thus far was needing to add -mlong-calls to the C options when running ./configure.  With luck I will soon have an unregistered GHC 6.12.1 on my ARM netbook.  I’ll edit this post tomorrow with results (yes, I’m actually compiling on the ARM and not in an x86 + QEMU environment).

Step 2: Get a registered build – upstream patches

This is where things become more black-box to me.  I want to make a native code generator (NCG) for GHC/ARM.  There are some decent notes about the RTS at the end of the previously mentioned porting guide and there is also a (up-to-date?) page on the NCG.  Hopefully this, combined with the GHC source, will be enough but I’ll probably be poking my head into #ghc more often.

Step 3: Write more Haskell

The purpose of all of this was to use Haskell on my ARM system.  Hopefully I’ll find time to tackle some problems that non-developers will care about!

The keen reader might have previously noticed my interest in distributed hash tables (DHTs). This interest is strong enough to motivate me to build a DHT library in Haskell, which I discuss here at an absurdly high level.

Distributed Hash Table Crash Course
(skip if you’ve read anything remotely good on DHTs)

A DHT is a sort of peer to peer network typically characterized with no central or “master” node, random node addresses, and uses a form of content addressable storage. Usually implemented as an overlay network and depicted as a ring, we say each node participating in the DHT “has an address on the ring”.

To locate data first you need an address; addresses are generated in different ways for any given DHT but is commonly a hash of either the description (“Fedora ISO”), file location/name (“dht://news”), or even a hash of the file contents themselves. The lookup message is then sent to this address and in doing so will get routed to the node with the closest matching address. The routing is fish-eye: nodes have more knowledge about the nodes with closer addresses and sparse knowledge of further addresses. The result is that the average number of hops to locate a node is logarthmic to the size of the network but so are the size of any one nodes routing table, so the burden isn’t too much.

To ensure correct operation, DHTs keep track of the closest addressed nodes (typically the closest 8 on each side). These nodes make up the ‘leaf set’ and are often used for special purposes such as redundantly storing data if the DHT is for file sharing/storage. It’s “easy” to keep this correct because the final step when joining a ring is to contact the node who’s currently closest to your desired address.

Functionallity of the Haskell DHT Library
Originally I worked toward Pastry like functionallity, but then I read a paper on churn and opted to implement Chord like policies on join, stabilize and key space management.

I’ve implemented the basic operations of routing, periodic leaf set matainence, join operations requiring an atomic operation on only a single (successor) node, and IPv4 support via network-data. This is built on Control-Engine, so you can instruct nodes to route or deliver using as many haskell threads as you want. Beyond that, adding hooks is what Control-Engine is built for, so its easily to plug in modules for load conditioning, authentication, statistics gathering, and arbitrary message mutation.

Using the DHT Library
The main job in starting a node is building the ‘Application’ definition. The application must provide actions to act on delivered messages and notifications about leaf set changes and forwarded messages (giving it the opportunity to alter forwarded messages). Additionally, the app is provided with a ‘route’ method to send messages of its own. I won’t go in to depth on this right now as I’m not yet releasing the library.

Using the hook instructions (and a custom Application definition) I’ve instrumented the code to log when it deals with join requests (sending, forwarding, finishing) and to show the leafset when it changes. Using the resulting logs I produced graphical representations of the ring state for various simulations (graphical work was part of a Portland State course on Functional Languages).

My student site has several simulations, but the most instructive one is LeafSet50 (22MB OGG warning!). Joining nodes are shown in the top area, active nodes are displayed in the ring at the center, thick lines are join requests being forwarded around, and groups of thin lines show the latest leaf set. Aside from revealing corrupt states caused by a broken stabilize routine, you can see some interesting facts for such a crude rendering:

A) Some areas are completely void while others are so dense that four or five nodes are overlapping almost perfectly. This tells us that, at least for sparsly populated DHTs, random assignment is horrible and can result in nodes having many orders of mangnitude larger area of responsibility than their counterparts. If I had bothered to read papers about applications based on DHT libraries then I might have known exactly how bad the situation could be, but it’s interesting to see this visually as well.

B) The simulated storm of joining nodes combined with periodic stabilization results in leaf sets being massively out of date. This concerns me less given the less-than realisic cause and the fact that everything eventually settles down (in ~120 seconds), but might bite me when I start thinking about join performance.

Other Ponderings
While I know that simulating 500 nodes takes 40% of my CPU time in the steady state (there is lots of sharing of leaf sets to make sure nothing has changed), this can be dramatically decreased by making the LeafSet data structure more efficient. Other than that, I’m just now considering the types of tests I desire to run. There are no serious benchmarks yet, but I hope to understand much more about the network performance such as:
1) Number of messages for joins
2) Number of bytes used for joins
3) Bandwidth needed for the steady state
4) Message rate in the steady state

Future Alterations:
Its not named! So job one is for me to figure out a name.

Code changes needed:
Remove nodes that don’t respond to stabilize messages (they aren’t deleted currently – they just persist).
Check the liveness of nodes appearing in the route table (only leaf set nodes are checked right now)
Generalize about the network – don’t mandate IPv4!
Polymorphic ‘Message a’ type – don’t assume H-DHT does the serialization to/from lazy bytestrings!
Stabilize is rudementary – check the sequence number to make sure responses are current!
Basic security/correctness mechanisms are still needed. When other nodes send route table rows and leaf sets we just add those entries into our own structure without any confirmation.

Protocol changes:
Implement Pastry proximity work – this will require the application to provide a function “proximity :: (NodeId, IPv4,Port) -> IO Int”.
Don’t always route to the optimal node, implement some jitter. Perhaps use the provably competative adaptive routing proposed by Awerbuch et al. for MANETs.
NAT Traversal?

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)
  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

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.

Control Engine


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 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.


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
  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)
  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


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.

hsXenCtrl and pureMD5

August 7, 2008

On vacation I found some time to upload the new hsXenCtrl library (0.0.7) and pureMD5 (0.2.4)

The new hsXenCtrl includes the System.Xen module, which is a WriterT ErrorT transformer stack and a brief attempt at ‘Haskellifying’ the xen control library.  I find it much more useful for simple tasks like pausing, unpasing, creating and destroying domains.  The API is still subject to change without notice as plenty of function are still very ‘C’ like (ex: scheduler / sedf functions).

pureMD5 received a much smaller change – some users noticed the -fvia-c caused compilation headaches on OS X.  After removing the offending flag, some benchmarks revealed no measureable difference in speed, so this is an over-due change. OS X users rejoice!

Hello planet, as my first post that gets placed on I decided to do a quick recap of the libraries I maintain and muse about future libraries.  My past posts include why static buffers make baby Haskell Curry cry and fun academic papers.

The Past

* pureMD5: An implementation of MD5 in Haskell using lazy ByteStrings.  It performs within an order of magnitude of the typical ‘md5sum’ binary, but has known inefficiencies and could be improved.

* ipc: A trivial to use inter-process communication library.  This could use some work, seeing as structures that serialize to over 4k get truncated currently.  I’ll probably only come back to this if I end up with a need for it.

* control-event: An event system for scheduling and canceling events, optimized for use with absolute clock times.

The Present

* hsXenCtrl: This library is intended to open the doors for Haskell apps to interact with and perhaps manage Xen.  Currently its just straight forward ‘c’ bindings to an old version of <xenctrl.h>, but the intent is to build a higher level library with useful plumbing.

* NumLazyByteString: Not sure if I’ll bother finishing this one, but it adds ByteString to the Num, Enum, and Bits type classes.  I just thought it would be funny to have lazy adding allowing: L.readFile “/dev/urandom” >>= \a -> print (a + a `mod` 256)

The Future

I tend to be a bit of a bouncing ball in terms of what nterests me.  Near and mid-term tasks will probably be a couple of these:

* A .cabal parser that can create a dependency graph for Haskell packages (not modules).  But should I use an outside package like graphviz or go pure / self contained Haskell?

* An implementation of something distributed like a P2P or ad-hoc networking protocol.  Would this be Pastry then Awerbuchs work or OLSR2?  These would be large tasks with their own ups and downs.

* Finally learn happs and make some sort of web Xen management system using hsXenCtrl.

* Learn Erlang – just because it looks cool too.

* Forget programming (and blogging) – read more TaPL!

Planet Haskell

June 9, 2008

Yes, the owner of this blog is requesting to get added to Planet Haskell – is this authentication enough? ;-)

Recommended Reading

May 25, 2008

I’ve been wanting to advance my education through a PhD program for a while now. As such, I’ve been reading a reasonable number of papers mostly in the field programming languages (strong bias toward SPJs work), but also in Ad-hoc networks (strong bias toward Baruch Awerbuch papers). I can’t say I’m too selective on what I like, but here are some of my likes anyway. Enjoy and feel free to post your papers or any discussion of the ideas presented in these papers.

Within The World of Languages

Simon Peyton-Jones, “Call-pattern Specialisation for Haskell Programs” *

Simon Marlow et al “Faster Laziness Using Dynamic Pointer Tagging

Simon Peyton-Jones et al, “Playing by the Rules: Rewriting as a practical optimisation technique in GHC” *

Tom Schrijvers et al “Type Checking with Open Type Functions” *

Duncan Coutts et al “Stream Fusion: From Lists to Streams to Nothing at All” *

Neil Mitchell and Colin RuncimanA Supercompiler for Core Haskell” * (Looks great, but I want to try it on my own programs to see if it will benefit me as much as I hope)

Peng Li et al “Lightweight Concurrency Primitives for GHC” * (A simpler to understand RTS would be great, but I fear for the performance)

Robert Ennals et al “Task Partitioning for Multi-Core Network Processors” *

Peng Li and Steve Zdancewic “Encoding Information Flow in Haskell” (perhaps not sound, but certainly useful)

Tim Harris and Simon Peyton Jones “Transactional Memory with Data Invariants” * (some functions aren’t available in the standard GHC/STM load, but the paper is fun anyway)

Dana Xu et al “Static Contract Checking for Haskell” * (I don’t know about you, but I almost can’t wait to see the work embodied in a GHC release!)

Every name you know “Roadmap for Enhanced Languages and Methods to Aid Verification

Ad hoc / Distributed Systems / Protocols

Baruch Awerbuch et al “Towards Scalable and Robust Overlay Networks” (See the entire line of papers, including “A Denial-of-Service Resistant DHT” and “Towards a Scalable and Robust DHT”)

Rudolf Ahlswede et al “Network Information Flow

Sachin Katti et al “Network Coding Made Practical” * (Now why isn’t this an option when I click network manager -> ad hoc network in Fedora 9?)

Joshua Guttman “Authentication Tests and the Structure of Bundles” *

Baruch Awerbuch et al “Provably Competitive Adaptive Routing” *

Baruch Awerbuch et al “Medium Time Metric” (This one is just begging for someone to write a paper “The opportunity cost metric”, don’t you think?)

* Easy read (even if it isn’t your field) / very enjoyable

I posted this as a page by accident – so here it is as a blog entry and I’ll delete the page some day.

My previous post discussed how inet_ntoa uses a static buffer which can cause a race condition. Unlike in ‘C’, this is particularly likely to cause a race in Haskell programs due to the quick, easy, and cheap threads using forkIO that (potentially) share a single OS thread. Two bright spots were that inet_ntoa was marked as IO and that the result is usually unimportant.

Another FFI binding, nano-md5, has a similar race condition but is much more series (not marked as IO and the result is a digest).

An even-handed note: iirc, nano-md5 remains hackage mostly as an FFI example – not that this is advertised in the nano-md5 description. “Real” users are told to look at hsOpenSSL and hopenssl – a cursory glance at the code suggests they don’t have this bug. Also, the other bindings don’t require O(n) space – so they are certainly worth switching to.

The nano-md5 line:

digest <- c_md5 ptr (fromIntegral n) nullPtr

is the culprit. It uses ‘nullPtr’ and according to the OpenSSL manual “If md is NULL, the digest is placed in a static array”.

Test code that confirms the bug can be found here – this will run three hash operations in parallel and eventually one result will be the correct first bits with ending bits from one of the other digests. The developer has already fixed the issue for versions 0.1.2+. I’ll wrap this post up with a request for library developers to please work to avoid use of static buffers – they have no place in this forkIO happy playland I call Haskell.


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