[Haskell-cafe] Parallel Haskell Digest 7
eric at well-typed.com
Sat Dec 24 21:40:22 CET 2011
Parallel Haskell Digest 7
GHC 7.4 is coming! There is loads to look forward to, but sometimes,
it's the little things that count. For example, do you hate the fact
that you can't just flip on an `+RTS -N` without having to first
recompile your program, this time remembering to throw an `-rtsopts` on
it? Duncan Coutts has relaxed the requirement so that commonly used RTS
options can be used without it. This flag was originally implemented to
counter security problems for CGI or setuid programs; however, it was
also a hassle for regular users because it got in the way of common
options like `-eventlog`, `-N`, or `-prof`. The GHC 7.4 RTS will make a
better tradeoff between security and convenience, allowing a common set of
benign flags without needing `-rtsopts`.
That's the sort of thing that the Parallel GHC Project is about. We
want to push parallel Haskell out into the real world, first by helping
real users (our ~~guinea pigs~~ industrial partners) to apply it to their
work, second by making it easier to use (tools, libraries), and finally
communicating more about it (this digest).
In this month's digest, we'll be catching up on news from the community.
After the holidays, we'll be back with some new words of the month
exploring a bit of concurrent Haskell. In the meantime, happy hacking
and Merry Christmas!
[Job Opportunity at Parallel Scientific][ph-job]
Peter Braam wants you, parallel Haskeller!
> Parallel Scientific, LLC is a Boulder, CO based early stage, but funded
> startup company working in the area of scalable parallelization for
> scientific and large data computing. We are implementing radically new
> software tools for the creation and optimization of parallel programs
> benefiting applications and leveraging modern systems architecture. We
> build on our mathematical knowledge, cutting edge programming languages and
> our understanding of systems software and hardware. We are currently
> working with the Haskell development team and major HPC laboratories world
> wide on libraries and compiler extensions for parallel programming.
Note the mandatory Haskell experience and the desirability of “in depth
knowledge of core Haskell libraries for parallel programming (NDP, REPA
Parallel GHC Project Update
The Parallel GHC Project is an MSR-funded project, run by Well-Typed,
with the aim of demonstrating that parallel Haskell can be employed
successfully in real world projects.
Our most recent work has been in polishing the upcoming ThreadScope
release that we previewed this September at the Haskell Implementor's
Workshop. This new release comes with goodies for users of Strategies
or the basic `par/pseq` parallelism: spark creation/conversion graphs,
visualisations showing your spark pools filling and emptying, and
histograms displaying the distribution of spark sizes. All this with
the aim of helping you gain deeper insight, not just what your program
is doing but *why*.
We've also done backend work to make ThreadScope even more useful
further down the road. First, we have improved the ghc-events package
by encoding the meanings of events in state machines. This makes it
possible to validate eventlogs, and doubles as an always up-to-date
source of code as documentation. Second, we have extended the GHC RTS
to emit the startup wall-clock time and Haskell threads labels to the
eventlog. The wall-clock time event allows us to synchronise logs for
simultaneous processes, brining us a step closer to using ThreadScope on
distributed programs. Named Haskell thread make it easier to distinguish
threads from each other.
Finally, we have been exploring the use of Cloud Haskell for high
performance computing on clusters. To do this we would need to abstract
Cloud Haskell over different transport mechanisms, that is to develop a
robust Cloud Haskell implementation sitting on top of a swappable
transport layer. We have posted an [initial design][mc3] for this layer
on the parallel-haskell list. We have taken the substantial feedback
into consideration and will be sending a revised design and recording it
in a page on the GHC wiki. Meanwhile, we are working to further
validate our design on simple models of both the transport layer and a
cloud Haskell layer on top. Longer term, we aim to implement some
transports, an IP transport in particular and perhaps a single-node
multi-process transport using forks and pipes.
Tutorials and Papers
* [Tutorial: Deterministic Parallel Programming in Haskell][hal6] (7 Oct)
Well-Typed's Andres Löh presented a parallel programming tutorial
at the recent Haskell in Leipzig meeting. The tutorial comes with
slides, exercises, sample code. It paints a picture of the
parallel Haskell landscape, and then focuses on one of the many
possible approaches (namely, strategies). One nice feature of the
tutorial is an emphasis on practicalities, for example, on using
ThreadScope to figure out where performance goes wrong in a program.
So if you're looking for a way to get started using on parallelism
to speed up your Haskell code, give Andres' tutorial a try!
* [Parallel Genome Assembly with Software Transactional Memory][stm-ketil] (27 Oct)
Ketil Malde wrote up some of his experiences using STM to
parallelise an inherently complicated program best solved with
multiple interacting threads. His article demonstrates that a
program using STM is able to successfully parallelize the genome
scaffolding process with a near linear speedup. Ketil would be
interested in any feedback the community may have.
Blogs and Packages
### Actors, actors everywhere
* [remote: Cloud Haskell is here!][b1] (27 Oct)
You may have been hearing a lot about Cloud Haskell lately, the new
Erlang-ish distributed programming library for Haskell. Now's your
chance to see what all the fuss is about! Jeff Epstein has uploaded
the remote package to Hackage, so take it for a spin by doing
cabal install remote
Library documentation is on the Hackage page, and more details are
available in the paper [Towards Haskell in the Cloud][ch-paper]
* [Distributed storage in Haskell][b2] (30 Oct)
So what are people doing with Cloud Haskell? Julian Porter for one
has been working on a distributed monadic MapReduce implementation.
Along the way he's produced a general proof of concept for
distributed storage. Have a look at Julian's page for a short
paper and GitHub page.
* [simple-actors 0.1.0 released][b3] (11 Oct)
Brandon Simmons accounced simple-actors, an EDSL-style library for
writing more structured concurrent programs, based on the Actor
Model. It was designed for local concurrency, as an alternative to
ad-hoc use of Chans, but could be extended to a distributed system
by defining appropriate [SplitChan][SplitChan] instances for some
* [Haskell Actors][b4] (28 Oct)
Martin Sulzmann wishes he'd named his [actor package][mh-actor]
“multi-headed-actor”. With the recent interest in actor style
concurrency in Haskell, there may be some confusion about the
various packages that are out there. The point in Martin's library
is being to pattern match over multiple events in the message
queue, which makes it easier elegantly express ideas like a
marketplace actor which matchmakes buyer/seller messages. While
Martin's library is built on concurrent channels, it could be
adapted to use distributed channels provided by haskell-mpi or
Cloud Haskell. See the paper for more information [Actors with
Multi-Headed Message Receive Patterns][mh-actor-pdf].
### More concurrency
* [stm-stats: Retry statistics for STM transaction][b5] (9 Oct)
Joachim Breitner blogged about the stm-stats package, which
provides wrappers around `atomically` to track how often a
transaction was initiated and how often it was retried. The
stm-stats library is used interally by Factis research, but
recently released to the wider Haskell community. In fact,
Factis have recently hired Joachim to help them contribute
back to the Free Software community where possible. So,
thanks, Factis and congratulations, Joachim!
* [How to deal with concurrent external events?][b6] (11 Oct)
Apfelmus has been scratching his head over a design problem for
event-based frameworks such as GUI libraries: how do you deal with
events that occur while you are currently handling another event?
Apfelmus gave a simple wxHaskell demonstrator illustrating the
problem, (A) reacting to an event while handling another one may
expose internal invariants but (B) reacting to an event after
finishing another one may render it “impossible”, i.e. it should
not have happened in the first place. Any thoughts on the dilema?
* [Concurrency And Foreign Functions In The Glasgow Haskell Compiler][b7] (24 Oct)
Leon P. Smith posted an overview of the interaction between Haskell
concurrency and FFI calls in GHC. Leon's post walks us through
some the basic concepts: capabilities, Haskell threads, OS threads,
and bound threads. This could be good place to start before
delving into papers or library documentation.
* [iteratee-stm][b8] (4 Nov)
John Lato announced the new iteratee-stm library recently
uploaded to Hackage. Iteratee-stm provides an iteratee interface
that uses bounded TChans for communication. This makes it simple to
run IO in a separate thread from processing.
* [Automatic deparallelization][b9] (17 Nov)
Ken Takusagawa explored a different perspective on parallelism.
Instead of adding parallelism to programs, what if we started
with too much parallelism and stripped it away to fit reality?
> Consider always writing code in a style using egregious fine
> grained parallelism: assume lots of cores with no communication
> latency and no overhead.
> It is the compiler's job to deparallelize (unparallelize,
> serialize) the program to run on the actual number of cores
> available, taking into account communication latency and the
> overhead of parallelization
Oh, and [qkhsskbg] ([Document Refinding Key][doc-refind])
* [Introducing Speculation][b10] (22 Jul 2010)
Recently, I got a chance to catch up with Edward Kmett, getting my
mind twisted into delightful funny shapes in the process. Edward
mentioned his speculation library, yet more parallelism in Haskell!
The library is based on the paper [Safe Programmable Speculative
Parallelism][sps-par] by Prakash Prabhu et al. It provides a way
to parallelise inherently sequential algorithms (eg. lexing,
Huffman decoding) by guessing the value of intermediate results.
You start working in parallel to build work off the guess, only
discarding it if the guess turns out to be wrong later on. Check
out Edward's blog and slides for more details.
* [Quasicrystals as sums of waves in the plane][b11] (24 Oct)
Keegan McAllister posted an somewhat hypnotic animation of
quasicrystals. His post comes with complete source code for
his program using the Repa parallel arrays library. Repa was
useful to Keegan because it provides
* Immutable arrays, supporting clean, expressive code
* A fast implementation, including automatic parallelization
* Easy output to image files, via repa-devil
* [Simple library for CAS posted][b12] (7 Dec)
Ryan Newton released IORefCAS, which provides a drop-in replacement
for atomicModifyIORef that takes advantage of the new `casMutVar#`
primop from GHC 7.2. Ryan says that “[b]ecause it's an easy change
it might be worth trying that for hot IOrefs in your parallel app.”
* [OpenCL 10.2.2][b13] (23 Nov)
Luis Cabellos has updated the Haskell OpenCL package with better
documentation and improved error handling using Control.Exception
instead of Either error.
Mailing list discussions
### Help wanted
* [Alternative STM implementation][mh1] (10 Dec)
Daniel Waterworth shared his [alternative STM
implementation][stm-danielw], written as a learning exercise.
Could anybody provide Daniel with some criticism and pointers
to STM benchmarks for comparison? His replacement should drop
right in if you only use TVars.
* [Parallel Matrix Multiplication][mh2] (10 Dec)
Mukesh Tiwari is trying to teach himself parallel Haskell
(welcome!). He's gone through Real World Haskell and the
[tutorial][afp08] by Simon Peyton-Jones and Satnam Singh, but
now trying to implement a parallel matrix multiplication function,
he finds himself with no sparks converted. Can anybody give Mukesh
Mukesh also asked about resources for Parallel Haskell, which
would be where I come in. Mukesh, have a look at the parallel
Haskell portal: <http://www.haskell.org/haskellwiki/Parallel>
### Cloud Haskell
* [Cloud Haskell now on Hackage][mc1] (27 Oct)
Jeff Epstein's announcement that he had uploaded “remote” to
Hackage was greeted with joy and a somewhat lengthy discussion on
package/module naming. It looks like the modules will be moved
from 'Remote' to 'Control.Distributed.Actor' or
'Control.Distributed.Process' to match the approach used for the
concurrency packages. The final package name seems to be
![Anyone got a paintbrush?](bikeshed.jpg)
* [Haskell Cloud and Closures][mc2] (1 Oct)
Fred Smith gave Cloud Haskell a try, using it to remotely compute
the plus function. Now he wants to be able to send a function to a
remote host, no matter if the function is locally declared or at
the top level. Erik de Castro Lopo replied that this was a known
limitation with the only known workaround being to move the
required function to the top-level. Chris Smith pointed out that
while the current restrictions may be too tight, there is good
reason to have them. As for alternatives approaches to serialising
functions, David Barbour suggested maybe looking at the [tangible
values][tv] work by Conal Elliot.
* [Feedback on Cloud Haskell transport layer interface][mc3] (2 Nov)
As I mentioned in the Parallel GHC Project update, we've been
looking quite a bit into Cloud Haskell lately. Duncan Coutts
posted a request for feedback on the design for a Cloud Haskell
transport layer interface. We're hoping one day to make use of
Cloud Haskell on for high performance computing on clusters. To do
this, we hope to develop a robust Cloud Haskell implementation
sitting on top of a swappable transport layer, for example, an IP
transport, or a single-node multi-process transport using forks and
One issues that emerged from the discussion is how to deal with
potentially a plethora of paramaters (eg. buffered vs eager?
ordered? reliable?) associated with connection/endpoint creation.
It doesn't help that each connection type may have its own set of
parameters. Is it enough to be able to set and forget them during
transport session initialisation, or is it essential for Cloud
Haskell be able to set these parameters differently for different
connections in the same session?
* [Parallel Haskell in industry][mc4] (7 Nov)
Sébastien Lannez also got a chance to try out Cloud Haskell. The
remote package uploaded by Jeff seems to work well and — dabblers
take note — the examples shipped with the code are very easy to
adapt. Before digging deeper, Sébastien wanted to know more about
1. performance limitations
2. communication requirements/overheads
4. already developed applications
Jeff cautioned that while he thinks Cloud Haskell could be a good
platform to develop distributed applications, it's still very much
research software and a work in progress. Don't stake your company
on Cloud Haskell just yet.
That said, Duncan Coutts added, we are pretty happy with the design
and optimistic about developing a robust implementation, because we
can build it as an ordinary Haskell library without requiring
tricky extensions to the runtime system. As for Sébastien's fourth
question, a couple of Parallel GHC Project partners are rather keen
on Cloud Haskell. We are working on the implementation and will
hopefully have more to report on performance, overheads and other
issues we encounter.
### Multicore performance
* [SMP parallelism increasing GC time dramatically][mm1] (5 Oct)
It takes a village to tune a program. Tom Thorne has a program
with a function does some fairly intensive calculations on with
hmatrix. When Tom tries to get some simple parallelism on his 12
core machine, replacing a `map` with a `parMap rdeepseq`, he finds
GC time going through the roof, from 1s (1.7%) to 248s (40.5%). Is
the big scary number just an artefact of how GC time is reported,
or is something really wrong?
ThreadScope is a good first response here and Tom was duly nagged
by the community. Tom promises to give it a go, although the last
time he tried, the event log output produced about 1.8GB, and then
crashed. The ThreadScope team would love to get hold of any hints
about reproducing the crash
Ryan Newton observed that GC aside, the program does not appear to
be scaling; the mutator time itself isn't going down with
parallelism. Tom improved the parallelism a bit, breaking the work
into chunks and spreading it around more evenly, and provided he
disables the parallel GC, it turns much faster and outperforms the
sequential version. Having loads of RAM to play and code that
doesn't use much memory, Tom then tried telling the RTS to perform
GC less often. This worked. Increasing the minimum allocation
area size from its default 512K with `+RTS -A32M` allows Tom to get
performance with the parallel GC comparable to that without.
Hooray! But there's still this little problem… now Tom's program
intermittently segfaults. Getting a bug report out of this may take
a while though as Tom attempts to boil it down.
Meanwhile, Oliver Batchelor offered his experience that enabling
more threads than he has cores makes his program get drastically
slower. Alexander Kjeldaas and Austin Seipp commented that this is
due to GC needing to co-ordinate with blocked threads, and that the
problem of oversaturating is well known. There's also the "dreaded
last core slowdown" bug which once affected Linux users but seems
to have gone away in recent Linux/GHC.
* [AMD Bulldozer modules and Haskell parallelism][mm2] (13 Oct)
Herbert Valerio Riedel has been eyeing the AMD FX-8120 [Bulldozer
processor][bulldozer]. Bulldozer cores are not
independent from each other, but grouped into pairs. So Herbert
wanted to know how this might affect Haskell parallelism; would
8 cores *really* mean 8 or just 4 with slightly better SMT
capability? Simon Marlow does not know (benchmarks). Duncan
Coutts believes that it should be all fine as the pairing is not
at all like hyperthreading.
* [Estimating contention on an IORef hammered with atomicModifyIORef][mm3] (27 Oct)
Ryan Newton starts us off with a hypothetical scaling bottleneck:
all threads frequently accessing a single IORef using
`atomicModifyIORef` (Data.IORef). This is commonly understood to
be likely a bad idea, but how do we go about measuring just *how*
bad it is? This sort of design appears in monad-par, as pointed
out by Johan Tibbell, in the GHC IO manager, so it would be good to
know how much it really hurts. (See also Ryan's [IORefCAS][b12]
package which seems to be partly a result of this discussion)
One approach is to use GHC events to count operations on particular
IORefs, then put that through a model that reports whether if the
IORef is being used acceptably, or is "hot". Duncan Coutts
suggests a simple way to get partway there: stick something like a
`traceEvent "IORef #3"` on each use of `atomicModifyIORef` and do
something like a `ghc-events show | grep IORef` to at least get an
idea which `IORefs` are hotter than others and some orders of
magnitude. We'll hear back from Ryan when he's had a chance to try
Meanwhile, Duncan's suggestion made Ryan curious about the
overheads from the `traceEvent`s. He attached a microbenchmark
showing some strange behaviour on one of his machines. Check the
thread out for a little magic from Simon Marlow and perhaps some
clues for your own benchmarking.
Also for the interested, it's worth mentioning that GHC 7.4 will be
sporting a new and improved `traceEvent`, this time exported
through `Debug.Trace` and offering versions for use in pure code
and IO both.
* [Way to expose BLACKHOLES through an API?][mm4] (7 Nov)
A BLACKHOLE in GHC acts as a placeholder for a thunk that is
currently being evaluated. When the thunk is forced, GHC replaces
it with a BLACKHOLE object, which it later replaces when it has the
evaluation result. In a parallel/concurrent setting, it may happen
that two threads are trying to evaluate the same thunk at the same
time. In that case, the first thread creates the blackhole, which
the second thread notices and blocks on until the evaluation result
Ryan Newton observes that this blocking is implicit, whereas
“[w]hen implementing certain concurrent systems-level software in
Haskell it is good to be aware of all potentially blocking
operations”. He proposes a mechanism to expose blackholes, for
example with a `evaluateNonblocking :: a -> IO (Maybe a)` that
returns `Nothing` if the value is blackholed. Simon Marlow points
out that this may be slightly problematic as thunks depend on each
other and “you might be a long way into evaluating the argument and
have accumulated a deep stack before you encounter the BLACKHOLE”
See the discussion for a counter-proposal.
### Data structures and concurrency
* [Efficient mutable arrays in STM][md1] (25 Oct)
Ben Franksen has large arrays (millions of elements) with mostly
small elements (Int or Double) and largely chunk-wise access
patterns. The current implementation of
`Control.Concurrent.STM.TArray` as `Array ix (TVar e)` is not
nearly efficient enough for his use case. A more efficient
implementation would be most welcome, but for now Ben is eyeing
`Data.Vector.Unboxed` from the vector package instead. The idea is
to use `unsafeIOToSTM` to provide shared transactional access to
his arrays. Ben thinks he can live with the consequences: IO code
being rerun, aborting, and inconsistent views.
But does the STM transaction actually "see" that he changed part of
the underlying array so that the transaction is retried? If not,
how does he go about manually implementing this behaviour? Antoine
Latter reports that no `unsafeIOToSTM` is not transactional - IO
actions will be performed immediately and are not rolled back, and
are then re-performed on retry. David Barbour and Ketil Malde
suggested possible implementations, either keeping an extra `TVar
Int` for every chunk in the array, or (B) cleaner and safer: create
a “chunked” `TArray` that works with fixed-width immutable chunks
in a spine.
Another issue that came up is that transactions scale quadratically
with the number of TVars touched. Bryan O'Sullivan and Ryan Ingram
explained that this is due to choice of data structure (a list)
for the STM transaction log, and should be easy to fix.
* [High performance threadsafe mutable data structures in Haskell?][md2] (27 Oct)
Ryan Newton wanted to know if anybody else was working on
threadsafe mutable data structures in Haskell. He and the monad-par
team were planning to replace their work stealing deques with
something more efficient. If anybody else is working in the same
general area, teaming up would be great!
Ryan will be exploring both a pure Haskell approach and one based
on wrapping foreign data structures with the FFI. Ultimately, Ryan
is aiming for an "abstract-deque" parameterizable interface that
abstracts over many variants (bounded/unbounded, concurrent/
non-concurrent, single/1.5/double-ended, etc). His
current prototype makes use of phantom types and the type families
extension to handle all this abstraction, with the intended end
result being that someone can create a new queue by setting all the
switches on the type (eg. `q :: Deque NT T SingleEnd SingleEnd
Bound Safe Int <- newQ`), but this brings up a set of Haskell
language and type system questions. More details in the thread!
* [Persistent Concurrent Data Structures][md3] (1 Nov)
Like Ryan, Dmitri Kondratiev is interested in concurrent
mutable data structures, but this time with persistence to boot.
His goal is to program at a higher level of abstraction,
avoiding the detail bloat that would result from directly using
some data storage API (eg. SimpleDB). Dmitri's idea: a module tree
of data structures mirroring Data.List, Data.Map, etc but with
concurrency and persistence. One would be able to configure
through the type interfaces:
1. media to persist data (file? DBMS?)
2. caching policy
3. concurrency configuration (optimistic/pessimistic locking?).
Dmitri's post prompted some suggestions for packages to look into:
* [safecopy][safecopy]: addresses both the issues of serializing
the data and migrating it when the datastructure changes
* [acid-state][acid-state]: builds on top of safecopy to add a
notion of transactions to any Haskell data structure
* [TCache][TCache]: a transactional cache with configurable
* Haskell web server frameworks (eg. Yesod, Happstack [acid-state
was formerly happstack-state]), as some come with persistence
Jeremy Shaw and David Barbour had reservations about what Dmitri
had in mind when he said "concurrent". How would he deal with
transaction boundaries, and would a concurrently modified Data.List
variant still be a list? Evan Laforge also expressed skepticism
about the viability of abstracting over data stores with
potentially very different needs.
### Threads, blocking
* [Waiting on input with `hWaitForInput' or `threadWaitRead'][mt1] (17 Oct)
Jason Dusek would like to use evented I/O for a proxying
application, in particular, to fork a thread for each new
connection and then to wait for data on either socket in this
thread, writing to one or the other socket as needed. He's found
two functions which could help, `System.IO.hWaitForInput` and
`Control.Concurrent.threadWaitRead` but each comes with some
difficulties. Is there something like `select()` that works with
handles rather than file descriptors?
Ertugrul Soeylemez suggested an alternative approach, just
plain Concurrent Haskell because “[a] hundred Haskell threads
reading from Handles are translated to one or more OS threads using
whatever polling mechanism (select(), poll(), epoll) your operating
system supports”. He pasted a small [echo server][ertu-echo]
to demonstrate the idea. It wasn't entirely clear for Jason
how to apply this to a proxy server. Jason has a `lazyBridge ::
Handle -> Handle -> IO ()` which writes everything it reads from
one handle into the other and vice-versa, but it blocks and does
not allow packets to go back and forth. Gregory Collins sketched
out a possible solution: how about `forkIO`ing two threads (one for
the read end, one for the write end), with a loop over lazy I/O?
[This works][jd-bridge], but is still somewhat surprising.
* [System calls and Haskell threads][mt2] (3 Nov)
Andreas Voellmy noticed this in Kazu Yamamoto's [Monad
Reader][mr-kazu] article on a high performance web server.
> When a user thread issues a system call, a context switch occurs.
> This means that all Haskell user threads stop, and instead the
> kernel is given the CPU time.
Can that be right? Andreas thought, and Johan Tibell confirms,
that when a Haskell thread is blocking a particular OS threads,
other Haskell threads can continue run concurrently on other OS
threads on other CPUs (see [Extending the Haskell
Foreign Function Interface with Concurrency][conc-ffi]).
Further clarification comes from David Barbour, who points out why
Kazu's original statement was correct in the context of the
article. While Mighttpd uses Haskell threads for concurrency; it
does not go the traditional route of using the RTS `-Nx` argument
to generate OS threads. Instead it gets its parallelism from
a "prefork" model that creates separate processes to balance user
invocations (each process may itself be running multiple Haskell
threads). This unusual approach is chosen to avoid issues with
* [Where threadSleep is defined?][mt3] (6 Dec)
Dmitri Kondratiev was looking for a function to make the current
process (executing thread) go to sleep for a given time. Felipe
Almeida Lessa pointed to the `threadDelay` function in
Stack Overflow and Reddit
* [Space analysis for parfib in monad-par example][s1]
* [Using the Par monad with STM and Deterministic IO][s2]
* [Haskell: thread blocked indefinitely in an STM transaction][s3]
* [How to install haskell Parallel on mac?][s4]
* [Mutable, (possibly parallel) Haskell code and performance tuning][s5]
* [Why does my concurrent Haskell program terminate prematurely?][s6]
* [Parallel Matrix Multiplication][s7]
* [Yo /r/haskell, I heard you like monads... : haskell][r1]
* [GHC commit: Allow the number of capabilities to be increased at runtime : haskell][r2]
* [Haswell processor (hardware transactional memory) : haskell][r3]
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
[Bikeshed image][bikeshed] by banlon1964 available under a CC-NC-ND-2.0 license.
Eric Kow <http://erickow.com>
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