[Haskell-cafe] Parallel Haskell Digest 10
Eric Kow
eric at well-typed.com
Fri May 18 16:22:48 CEST 2012
Hello Haskellers!
Did you see Ambassador Peyton Jones in Scala land? Simon was recently at
ScalaDays 2012 (a large gathering for professional Scala users) giving a
[keynote talk on Cloud Haskell][v1] (one hour video). Cloud Haskell is
a pretty exciting new development in the Haskell space, providing the
beginnings of a story for distributed programming in Haskell. It's also
one of the areas we're focused on over the Parallel GHC project,
building a new implementation to replace the current prototype. We're
looking forward to talking a bit more about Cloud Haskell in the next
(and final) edition of the digest.
Wait, did I say just **final**? Indeed, by the next digest, we'll be
wrapping up the Parallel GHC project. In addition to a bit more Cloud
Haskell material, we'll give a little recap of the things we and our
partners worked on over the two years. It's been fun!
Meanwhile, in this penultimate edition, we'll be taking a look at
concurrent *channels* as our word of month. We also have new parallel
Haskell book to look forward to, an update to Accelerate, the new
`meta-par` family of packages to look at, and also a lot of recent
activity on StackOverflow.
(For fancy HTML version and images, see http://www.well-typed.com/blog/66 )
News
----------------------------------------------------------------------
* [GSoC: concurrent hashtables][n0]
Loren Davis and 7 other students have been accepted to the Google
Summer of Code project under Haskell.org. Loren will be working to
[implement concurrent thread-safe mutable hash-table][n0].
Congratulations and best of luck to Loren, Aditya, David, Mark,
Mikhail, Phillip, Shae, and Shayan. Hope it's a fun summer!
* [Lectureship in Computer Science - Saint Andrews][n3]
Kevin Hammond urges us to consider applying for this lecturship in
functional programming (closing date 22 June) at the University of
Saint Andrews: We seek lectureship applications from researchers
who have a strong research background and excellent publication
record in any area of functional programming, complementing and
enhancing the existing research team, which has a strong focus on
parallel programming models and implementation, resource-aware
functional programming, dependent type systems, refactoring, static
analysis, and performance modelling, and deep connections with the
Haskell community.
* [O'Reilly book on Parallel and Concurrent Haskell][n4] (17 May)
Haskell is getting a third O'Reilly book! Joining the introductory
[Real World Haskell][rwh], and web-oriented [Haskell and
Yesod][yesod-book], will be a forthcoming book on parallelism and
concurrency in Haskell (tentative completion date March 2013).
Simon Marlow will building this book off his [CEFP 2012
tutorial][sm-cefp] tutorial. He's “really keen for this
to be a book that will be useful to people both learning about
parallelism and concurrency in Haskell, and coding stuff for
real-world use.” Please let him know if you have suggestions for
topics or application areas you'd like covered.
## Conferences
* [Erlang Workshop (due 3 Jun, workshop 14 Sep)][n1]
Haskell has been borrowing from Erlang lately (Cloud Haskell!).
John Hughes suggests, “Why not adapt some cool Haskell ideas to
Erlang too?” The Eleventh ACM SIGPLAN Erlang Workshop will take
place this year in Copenhagen, Denmark on the tail end of the
ICFP. Just two weeks to go!
* [Facing the Multicore-Challenge III (19-21 Sep)][n2]
And if ICFP isn't enough for you, how about this Conference for
Young Scientists, just a few days after? The Hochschule für
Technik, will hosting the third multicore-challenge conference
in Stuttgart, Germany, from 19-21 September. It aims to combine
new aspects of multi-/manycore microprocessor technologies,
parallel applications, numerical simulation, software development
and tools. Contributions are welcome from all participating
disciplines. Particular emphasis is placed on the support and
advancement of young scientists.
Word of the month
----------------------------------------------------------------------
This month, we'll be taking a short breather in our exploration of the
Haskell concurrency space, and fleshing out some of the uses for the
tools we already have. In the past two digests, we saw how Haskell
provides *locks* for low-level concurrency, and the vastly safer
*transactions* for concurrency at a higher level. Both
approaches give us the notion of a typed mutable variables, the idea
being that an `MVar Int` would hold a locked integer, whereas a `TVar
Int` would hold instead hold transactional reference to an integer.
These variables can hold arbitrarily complex things of arbitrary type;
you could have anything from a `TVar Char` to a `TVar Customer` (where
`Customer` would be some record you've defined in your application).
Now that we have mutable variables, it's worth thinking a bit harder
about what we might actually put into them. Suppose you find yourself in
a typical producer/consumer scenario, for example, with a web service
that automatically marks student essays, and which is broken into a
piece that accepts submissions (producer) and which passes them on to
the core essay-marking engine (consumer). So the producer generates
essays and the consumer eats them up and does some work on them; how do
we get them talking to each other? It's not enough to just use a single
`TVar` because we want the producer to be able to continue cranking out
essays whilst the consumer is working, rather than waiting for it to
finish. We assume here that essay-marking is a fairly clever and
computationally expensive process, and for this reason, we would want
some kind of backlog that the producer can tack things on to, and the
consumer can pull things off of.
As such, our word of the month is *channel*. The unbounded channel
abstraction is something that you can fairly easily implement out of
either the locky `MVar`'s or transactional `TVar`'s, but we'll focus on
the latter as transactions are just so much more civilised (though the
same concepts would mostly apply). In the STM world, channels look a
little like the following:
-- Control.Concurrent.STM.TChan
data TChan a
newTChan :: STM (TChan a)
writeTChan :: TChan a -> a -> STM ()
readTChan :: TChan -> STM a
In the same fashion as the `TVar`'s that we introduced last time,
`TChan`'s are parameterised with a type variable, meaning that you could
have a channel of characters with `TChan Char`, or a channel of
customers with `TChan Customer`, and so forth. Creating, reading, and
writing to a channel are all transactions (i.e., in the the STM monad).
Revisiting our essay marking service, we can sketch out how these
channels might be used:
import Control.Concurrent.STM.TChan
main :: IO ()
main = do
chan <- newTChan
forkIO (producer chan)
forkIO (consumer chan)
forever $ return ()
producer :: TChan Essay -> IO ()
producer chan = forever $ do
essay <- magicalWebFrameworkStuff
atomically $ writeTChan chan essay
consumer :: TChan Essay -> IO ()
consumer chan = forever $ do
essay <- atomically $ readTChan chan
mark essay
mark :: Essay -> IO ()
mark essay =
putStrLn "Let me think..."
-- State-of-the-art marking technology,
-- just $25000 per site license
randomRIO (1, 10000000) >>= threadDelay
pass <- randomIO
if pass
then putStrLn "Pass, good job!"
eles putStrLn "Fail!"
And that's it! Using concurrent channels does not get more complicated
or deeper than this. You may have noticed that in this particular
example, we have not really gained (or for that matter lost) that much
from sticking to the transactional version of channels. Using the locky
`MVar` version would basically consist of dropping the `atomically`'s,
importing from `Control.Concurrent.Chan`, and using `Chan` instead of
`TChan`.
Now that we have a bit of an idea what channels are about, it could be
worthwhile to consider what it really offers over simpler alternatives.
For example, in the introduction we rejected the idea of just using a
single `TVar` because this would force our producer and consumers to
wait on each other for each and every essay, rather than going about
their asynchronously merry ways.
So we know we want something *like* channels, but how exactly do we go
about building them? For starters, wouldn't we get a channel structure
by just wrapping `Data.Sequence.Seq` with a single `TVar`? It could be
made to work as we are using STM (it simply wouldn't work if we were
using `MVar`'s instead; consider the empty channel), but it would leave
us with the unfortunately inability to simultaneously read from and
write to the channel. These operations would have to grab a hold of the
whole queue, leaving the other to retry until later. It would a little
sad not to enable this bit of concurrency, considering that that reading
and writing take place at opposite ends of the queue, the reader walking
along trying to keep up with the writer.
Instead of naively wrapping a queue, the current implementation uses a
sort of linked list with `TVar`'ed cons cells and `TVar`'s pointing to
both the beginning (the read end) and the end of the list (the write
end). Here are the data structures that make up a channel:
type TVarList a = TVar (TList a)
data TList a = TNil | TCons a (TVarList a)
data TChan a = TChan (TVar (TVarList a)) -- read end
(TVar (TVarList a)) -- write end
It can be a little bit tricky to think about because we've got `TVar`'s
wrapping around things that eventually wrap around `TVar`'s themselves.
It's a whole chain of `TVar`'s, and if you can have a `TVar a`, there's
no reason not to have a `TVar (TVar a)`. If that feels a bit shaky,
try implementing channels yourself as a quick little exercise. We'll
speed things along with a handful of pictures to illustrate how it
might work. First, our visual language for talking about `TVar`'ed cons
cells:
A new channel has three `TVar`'s, one for the linked list (it points to
`TNil`), and a pair of read/write ones pointing to this pointer:
Writing the channel involves adding to the list and moving the write
pointer to the new tail of the list:
And finally reading those items off the channel involves moving the
read pointer forward:
The implementation should be fairly straightforward from the pictures,
although one place you might get stuck when trying to read from an empty
channel. After all, how do you return a value from a channel that
doesn't have any, especially since you're expected to return plain old
`a` instead of `Maybe a`? Well, sometimes you just gotta wait. We
briefly glossed over this in our taste of STM in the [last word of the
month][ph-digest-9], but STM offers a `retry` function simply causes a
transaction to be aborted and tried again. Using this notion of
blocking, you should be able to get a `readTChan` that waits until there
is something to be read.
Hopefully, the exercise of implementing channels will be a useful
reminder to think of the concurrency abstractions that Haskell provides
(threads, transactional mutable variables) as primitives on top of which
you can build more interesting and useful abstractions. For a little
more fun, head over to Simon Marlow's [tutorial on parallelism and
concurrency][tutorial-sm]. In this tutorial, Simon illustrates the
building channels over `MVar`'s (also worth doing) and mentions an easy
generalisation to *multicast* channels (one write end, two read ends!)
and also a small extension to “unread” a value (pushing it back on to
the read end). Both extensions are easy on the surface, but hard to
nail down to the exact desired semantics (there's no known correct
implementation of the unread extension), at least when you're dealing
with locks and `MVar`'s. But if you stick to transactions and `TVar`'s,
both wind up being straightforward. Check out his tutorial!
Videos
----------------------------------------------------------------------
* [Towards Haskell in the Cloud][v1] (17 Apr, 1 hour)
Simon Peyton Jones gave one of the keynote lectures at the recent
ScalaDays 2012 in London. Simon sets the stage with some of the
recent thinking about how functional programming and parallel
programming fit together: that functional is the way forward, but
no one single approach is going to cover all scenarios; that cost
models are important to keep in mind; and that being able to
explore all these different approaches within a single language is
a great thing.
From here, Simon launches into the heart of his presentation, Cloud
Haskell (Scala hackers, think Akka). Cloud Haskell was developed
with distributed programming in mind, in other words, large scale,
multiple machines, no shared memory. Simon's talk covers quite a
bit of ground: the Erlang-style actor model as an explicit embrace
of distributed memory; the Haskell typed channels twist;
programmer-controlled serialisation with type classes; and finally
the longtime big stumper, how we go about serialising functions.
It's interesting stuff. And as a sort of side bonus, even if
don't walk away with a better idea what Cloud Haskell is all about,
you should at least get a concrete sense for how type classes work.
Blogs and packages
----------------------------------------------------------------------
* [hpuns: parallel jeux de mots (plays on words)][b2]
HPuns is a valuable tool for making your future (French speaking)
children's lives miserable. It generates awful puns from names, and
in parallel, no less! Paul Brauner was just looking for some
parallel “speedups out of the box.“ Where he was previously was
able to get only a mediocre speedup with Strategies, with the
monad-par package he was able to knock out a quick 1.3x speedup
(-N3) over a lunchbreak. HPuns may just be a toy (one file!) but
it could be what you're looking for if monad-par usage examples is
what you're after.
* [Accelerate version 0.12: GPU computing with Haskell][b5] (14 May)
Manuel Chakravarty the recent 0.12 release of Accelerate, with full
sharing recovery in scalar expressions and array computations, some
more examples, and bug fixes. Accelerate should still be considered
a beta release, but the folks at UNSW are hungry for early
adopters. So do give it a try!
* [How to write hybrid CPU/GPU programs with Haskell][b3] (4 May)
If you like your monad-par and your Accelerate working
together, consider this proposition from Ryan Newton: “What’s
better than programming a GPU with a high-level, Haskell-embedded
DSL (domain-specific-language)? Well, perhaps writing portable
CPU/GPU programs that utilize both pieces of silicon…”
Ryan walks us through installing the recently released meta-par
packages and writing some simple hybrid code. The general idea
seems to be to define CPU and GPU implementations of a task and to
allow the generalised work-stealing scheduler to choose between
them; the promise being that if a program performs much better on
one device (say the GPU), that device will wind up doing most of
the work.
More generally, the meta-par family of packages is aimed at
heterogeneous programming (it provides a mechanism for building
parallel schedulers out of "mix-in" components). Ryan's post
focuses on the CPU/GPU scenario, but there are also future packages
coming our way for distributed programming too. If you are
interested in learning more, have a look at the [meta-par
GitHub][meta-par] page and the the draft paper [A Meta-Scheduler
for the Par-Monad][meta-par-paper].
Mailing lists
----------------------------------------------------------------------
* [Is protocol-buffers package maintainer reachable?][m1] (23 Apr)
Paul Graphov finds the protocol-buffers and hprotoc packages
failing to build with the latest GHC. Unfortunately, he seems to
be having trouble getting his patches to maintainer Chris
Kuklewicz. The mailing list thread came up with suggestions for
places we might get in touch with Chris, and also touched on a
sort of recurring dilemma in the Haskell community: what do we do
when package maintainers disappear? The general feeling seems to
be that provided we can find a new maintainer, we should discuss
it on the mailing list, then go ahead and take over.
* [Threads and hGetLine][m5] (28 Apr)
H.M. has a simplified scenario he's having trouble with:
suppose you have thread waiting on input with `hGetLine`,
and you have a supervisor thread to close the handle and/or
kill the first thread. How do you make it work? It seems
that `hClose` and `killThread` do not work for H. because
the first thread is blocking for input. Alvaro Gutierrez
suggests perhaps using a different approach which doesn't
involve killing threads, perhaps non-blocking IO and a
synchronised condition variable.
Perhaps H.M. is on Windows? Joey Adams points out that “GHC
currently doesn't have proper IO manager support for Windows.
On Windows, IO is performed through FFI calls. An FFI call masks
asynchronous exceptions… If another thread tries to `killThread`
the thread waiting for input, the exception will not be received
until the FFI call completes. This means both threads will hang.”
* [using ResourceT with MVars][m2] (2 May)
Warren Harris would like to use LevelDB (an open source on-disk
key-value store inspired by BigTable). Unfortunately for Warren,
the 0.0.3 version of leveldb-haskell switches from `get`/`put`
in the `IO` monad to `ResourceT`. This makes it tricky for him
to write code like
withMVar state $ \db -> do
maybeValue <- get db rdOpts key
put db wrOpts key $ maybe init incr maybeValue
In effect, the question is “how do I run MVar operations in
ResourceT?” Michael Snoyman suggests the
[lifted-based][lifted-base] package for exactly this. Lifted-base
exports IO operations from the base library lifted to any instance
of `MonadBase` or `MonadBaseControl`. Warren can use any of the
`Control.Concurrent.MVar.Lifted` functions since `ResourceT` is an
instance of `MonadBaseControl`
* [meaning of `__PARALLEL_HASKELL__` and -parallel][m3] (13 Apr)
Facundo Domínguez was wondering what the `__PARALLEL_HASKELL__`
CPP macro, and the `-parallel` flags stand for. Jost Berthold
explains that they both relate to parallel versions of GHC on
distributed memory platforms.
* [Can Haskell outperform C++?][m4] (6 May)
Janek S. has seen claims floating around the internet that Haskell
programs can have comparable performance to C and C++ versions.
He's also seen claims about the promise of functional programming
for automatically parallelising the software. Is there any
evidence for these claims? This is a tricky question to pin down,
and one that is vulnerable to apple/orange comparisons (some
suggestions in the thread, [1][donscore], [2][bfusion],
[3][shootout]) or debates about what makes a programming language
desirable. Janek's question sparked a fairly epic thread, maybe
worth digging through if you're struggled with Haskell performance
before; or are member of the shadowy Haskell propaganda committee.
For parallel Haskellers, I'll pick out a handful of sub-topics that
came up.
Ertugrul Söylemez makes a general comment to look at the bigger
picture. While (currently) seldomly beats C/C++ for number
crunching algorithms, “where [it] really gets beyond C and C++ is
in concurrency. Multithreaded network servers written in Haskell
are not only a lot easier to write, but they also perform better
than well written server programs in C/C++. This is because
Haskell's concurrency goes well with its parallel execution model,
where in machine code you don't really have a notion of procedures.
You have thunks and they can not only be executed in parallel, but
they can also be moved between threads freely.”
As for automatic parallelisation, Simon Marlow points out that
there isn't yet any fully implicit parallelism in Haskell. What
Haskell does provide, on the other hand, is a “a guarantee that you
can safely call any function in parallel… as long as the function
does not have an IO type”. So while fully automatic parallelism
isn't here (and may not ever be), you can at least take an
arbitrary piece of Haskell code and use it with something like
`parMap`, or call it from mulitple threads, and expect some sort of
speedup from it; and safely, with a fully deterministic result and
no nasty concurrency bugs to worry about.
Finally, showing how it can be tricky to performance can be
quite tricky, Ryan Newton had an example in his monad-par repo
of a (rather C-ish) [aggressively optimised Haskell
program][mandel] that was 6⨉ slower than its counterpart and worse
9⨉ with the new LLVM backend (said backend has been helpful in
other cases, though, eg. for Manuel Chakravarty). There's a happy
ending behind this. After some digging, Ryan uncovered a clever
math trick used in the OCaml standard library. Porting it to
Haskell makes his program not only faster than OCaml and Scheme but
48% faster than C++ too! Looks like we'll be getting a library
patch from Ryan soon. “The moral,” Ryan comments, “is that
community members could do a great deal to help "Haskell"
performance by simply microbenchmarking and optimizing library
routines in base!”
StackOverflow and Reddit
----------------------------------------------------------------------
This month saw quite a lot of activity on StackOverflow, largely from
[user Clinton][so-clinton] trying to puzzle through STM and other
concurrency issues. The STM and `atomicModifyIORef` series of questions
could be interesting, at the very least, to see what sorts of things
people wonder about when they first run into Haskell concurrency.
## General questions
* [Identifying the current HEC for a function in Haskell][s1]
* [Haskell parallel computation using an STArray][s2]
* [Slowdown when using parallel strategies in Haskell][s3]
* [Haskell parallel performance][s4]
* [“Monad-friendly” event-based IO][s5]
* [Haskell concurrency - is forkIO really nondeterministic?][s6]
* [TimeoutManager uses tryPutMVar to put nothing][s8]
* [Software transactional memory with a big, shared piece of data][s9]
## STM
* [How does TVar work?][sc11]
* [Container for Haskell TVars][s7]
* [Updating two or more TVars atomically. Possible?][sc10]
* [TVar: Preventing starvation][sc9]
* [TVar: orElse][sc8]
* [Concurrent generic data structure without deadlocks or resource starvation][sc7]
* [Concurrent data structure guidelines][sc2]
* [“Wait-free” data in Haskell][sc1]
## atomicModifyIORef
* [How does 'atomicModifyIORef' work?][sc6]
* [Concurrency using simple IORef?][sc5]
* [Atomic IO wrapper/laziness?][sc4]
* [Attempt at parallel `atomicModifyIORef` implementation][sc3]
* [Concurrent data structure guidelines][sc2]
## Reddit
* [SPJ talk: Towards Haskell in the Cloud : haskell][r1]
* [Revamped Haskell concurrency and parallelism libraries page: from parallel arrays to Cloud Haskell : haskell][r2]
Help and Feedback
----------------------------------------------------------------------
If you'd like to make an announcement in the next Haskell Parallel
Digest, then get in touch with me, Eric Kow, at
<parallel at well-typed.com>. Please feel free to leave any comments and
feedback!
[lifted-base]: http://hackage.haskell.org/package/lifted-base
[tutorial-sm]: http://community.haskell.org/~simonmar/par-tutorial.pdf
[ph-digest-6]: http://www.well-typed.com/blog/60
[ph-digest-9]: http://www.well-typed.com/blog/65
[so-clinton]: http://stackoverflow.com/users/525980/clinton
[meta-par]: https://github.com/simonmar/monad-par/
[meta-par-paper]: http://www.cs.indiana.edu/~rrnewton/papers/meta-par_submission.pdf
[donscore]: http://donsbot.wordpress.com/2007/11/29/use-those-extra-cores-and-beat-c-today-parallel-haskell-redux/
[bfusion]: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.90.3166
[mandel]: https://github.com/simonmar/monad-par/blob/662fa05b2839c8a0a6473dc490ead8dd519ddd1b/examples/src/mandel.hs#L24H
[shootout]: http://shootout.alioth.debian.org/
[rwh]: http://book.realworldhaskell.org/
[yesodbook]: http://www.yesodweb.com/book
[sm-cefp]: http://community.haskell.org/~simonmar/papers/par-tutorial-cefp-2012.pdf
[yesod-book]: http://www.yesodweb.com/book
[n0]: http://www.google-melange.com/gsoc/project/google/gsoc2012/lorehead/36001
[n1]: http://www.haskell.org/pipermail/haskell/2012-April/023275.html
[n2]: http://www.multicore-challenge.org/
[n3]: https://www.vacancies.st-andrews.ac.uk//ViewVacancy.aspx?enc=mEgrBL4XQK0+ld8aNkwYmP7j9uKB0Q3XDyDQqVA9vP1JLwBKRtw4rNHXJis9NOK7tfyZGpjmqWuqi2gq2EDJAA==
[n4]: http://www.haskell.org/pipermail/haskell/2012-May/023328.html
[b1]: http://www.haskell.org/haskellwiki/Applications_and_libraries/Concurrency_and_parallelism
[b2]: http://code.google.com/p/hpuns/source/browse/Main.hs
[b3]: http://parfunk.blogspot.co.uk/2012/05/how-to-write-hybrid-cpugpu-programs.html
[b4]: http://www.haskell.org/pipermail/haskell-cafe/2012-May/101113.html
[b5]: http://justtesting.org/gpu-accelerated-array-computations-in-haskell
[v1]: http://skillsmatter.com/podcast/home/haskell-cloud/js-4179
[m1]: http://www.haskell.org/pipermail/haskell-cafe/2012-April/100867.html
[m2]: http://www.haskell.org/pipermail/haskell-cafe/2012-May/101027.html
[m3]: https://groups.google.com/d/msg/parallel-haskell/kU0Ndl-IH3Y/GoQog9psZJIJ
[m4]: http://www.haskell.org/pipermail/haskell-cafe/2012-May/101123.html
[m5]: http://www.haskell.org/pipermail/haskell-cafe/2012-April/100988.html
[s1]: http://stackoverflow.com/questions/10105127/identifying-the-current-hec-for-a-function-in-haskell
[s2]: http://stackoverflow.com/questions/10079710/haskell-parallel-computation-using-an-starray
[s3]: http://stackoverflow.com/questions/10009361/slowdown-when-using-parallel-strategies-in-haskell
[s4]: http://stackoverflow.com/questions/9578416/haskell-parallel-performance
[s5]: http://stackoverflow.com/questions/10364549/monad-friendly-event-based-io
[s6]: http://stackoverflow.com/questions/10247555/haskell-concurrency-is-forkio-really-nondeterministic
[s7]: http://stackoverflow.com/questions/10098506/container-for-haskell-tvars
[s8]: http://stackoverflow.com/questions/9869763/timeoutmanager-uses-tryputmvar-to-put-nothing
[s9]: http://stackoverflow.com/questions/9547325/software-transactional-memory-with-a-big-shared-piece-of-data
[r1]: http://www.reddit.com/r/haskell/comments/t0qg0/spj_talk_towards_haskell_in_the_cloud/
[r2]: http://www.reddit.com/r/haskell/comments/stn2b/revamped_haskell_concurrency_and_parallelism/
[sc1]: http://stackoverflow.com/questions/10239239/wait-free-data-in-haskell
[sc2]: http://stackoverflow.com/questions/10220629/haskell-concurrent-data-structure-guidelines
[sc3]: http://stackoverflow.com/questions/10203446/haskell-attempt-at-parallel-atomicmodifyioref-implementation
[sc4]: http://stackoverflow.com/questions/10169434/haskell-atomic-io-wrapper-laziness
[sc5]: http://stackoverflow.com/questions/10115756/haskell-concurrency-using-simple-ioref
[sc6]: http://stackoverflow.com/questions/10102881/haskell-how-does-atomicmodifyioref-work
[sc7]: http://stackoverflow.com/questions/10101861/haskell-concurrent-generic-data-structure-without-deadlocks-or-resource-starvat
[sc8]: http://stackoverflow.com/questions/10101044/haskell-tvar-orelse
[sc9]: http://stackoverflow.com/questions/10099990/haskell-tvar-preventing-starvation
[sc10]: http://stackoverflow.com/questions/10099815/haskell-updating-two-or-more-tvars-atomically-possible
[sc11]: http://stackoverflow.com/questions/10092655/haskell-how-does-tvar-work
--
Eric Kow <http://erickow.com>
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