LVars are one outcome of Lindsey Kuper's ongoing
PhD work at Indiana University. They generalize existing models for
deterministic parallelism by considering a general framework of
monotonic read and write operations. They were briefly mentioned
on LtU before (along with the strongly related work on Bloom in the distributed
systems community), and were recently presented in two
distinct and complementary articles.
The first article describes the basic building blocks and ideas of LVars:
LVars: Lattice-Based Data Structures for Deterministic Parallelism
Lindsey Kuper, Ryan R. Newton
Programs written using a deterministic-by-construction model of
parallel computation are guaranteed to always produce the same
observable results, offering programmers freedom from subtle,
hard-to-reproduce nondeterministic bugs that are the scourge of
parallel software. We present LVars, a new model for deterministic-
by-construction parallel programming that generalizes existing
single-assignment models to allow multiple assignments that are
monotonically increasing with respect to a user-specified lattice.
LVars ensure determinism by allowing only monotonic writes and
"threshold" reads that block until a lower bound is reached. We
give a proof of determinism and a prototype implementation for a
language with LVars and describe how to extend the LVars model
to support a limited form of nondeterminism that admits failures
but never wrong answers
The second relaxes the original model by introducing failure, which
widens its applicability:
Freeze After Writing: Quasi-Deterministic Parallel Programming with LVars
Lindsey Kuper, Aaron Turon, Neelakantan Krishnaswami, Ryan R. Newton
Deterministic-by-construction parallel programming models offer
programmers the promise of freedom from subtle, hard-to-reproduce
nondeterministic bugs in parallel code. A principled approach to
deterministic-by-construction parallel programming with shared state
is offered by LVars: shared memory locations whose semantics are
defined in terms of a user-specified lattice. Writes to an LVar take
the least upper bound of the old and new values with respect to the
lattice, while reads from an LVar can observe only that its contents
have crossed a specified threshold in the lattice. Although it
guarantees determinism, this interface is quite limited.
We extend LVars in two ways. First, we add the ability to “freeze”
and then read the contents of an LVar directly. Second, we add the
ability to attach callback functions to an LVar, allowing events to be
triggered by writes to it. Together, callbacks and freezing enable
an expressive and useful style of parallel programming. We prove that
in a language where communication takes place through freezable LVars,
programs are at worst quasi-deterministic: on every run, they either
produce the same answer or raise an error. We demonstrate the
viability of our approach by implementing a library for Haskell
supporting a variety of LVar-based data structures, together with
two case studies that illustrate the programming model and yield
promising parallel speedup.
Something I personally found surprising and impressive about LVars is
that, while I was initially interested in the very formal aspects of
providing a theoretical framework for deterministic concurrency, it
very quickly produced a practical library that people can use to write
parallel program -- and competitive with existing high-performance
approaches. As described in a
recent blog post, a Haskell library is available on Hackage -- but
surely LVars-inspired libraries could make sense in a lot of other
languages as well.
Concurrent Revisions is a Microsoft Research project doing interesting work in making concurrent programming scalable and easier to reason about. These papers work have been mentioned a number of times here on LtU, but none of them seem to have been officially posted as stories.
Concurrent Revisions are a distributed version control-like abstraction  for concurrently mutable state that requires clients to specify merge functions that make fork-join deterministic, and so make concurrent programs inherently composable. The library provide default merge behaviour for various familiar objects like numbers and lists, and it seems somewhat straightforward to provide a merge function for many other object types.
They've also extended the work to seamlessly integrate incremental and parallel computation  in a fairly intuitive fashion, in my opinion.
Their latest work  extends these concurrent revisions to distributed scenarios with disconnected operations, which operate much like distributed version control works with source code, with guarantees of eventual consistency.
All in all, a very promising approach, and deserving of wider coverage.
 Sebastian Burckhardt and Daan Leijen, Semantics of Concurrent Revisions, in European Symposium on Programming (ESOP'11), Springer Verlag, Saarbrucken, Germany, March 2011
 Sebastian Burckhardt, Daan Leijen, Caitlin Sadowski, Jaeheon Yi, and Thomas Ball, Two for the Price of One: A Model for Parallel and Incremental Computation, in Proceedings of the ACM International Conference on Object Oriented Programming Systems Languages and Applications (OOPSLA'11), ACM SIGPLAN, Portland, Oregon, 22 October 2011
 Sebastian Burckhardt, Manuel Fahndrich, Daan Leijen, and Benjamin P. Wood, Cloud Types for Eventual Consistency, in Proceedings of the 26th European Conference on Object-Oriented Programming (ECOOP), Springer, 15 June 2012
Visi.io comes from David Pollak and aims at revolutionizing building tablet apps, but the main attraction now seems to be in exploring the way data flow and cloud computing can be integrated. The screencast is somewhat underwhelming but at least convinces me that there is a working prototype (I haven't looked further than the website as yet). The vision document has some nice ideas. Visi.io came up recently in the discussion of the future of spreadsheets.
The Milner Symposium 2012 was held in Edinburgh this April in memory of the late Robin Milner.
The Milner Symposium is a celebration of the life and work of one of the world's greatest computer scientists, Robin Milner. The symposium will feature leading researchers whose work is inspired by Robin Milner.
The programme consisted of academic talks by colleagues and past students. The talks and slides are available online.
I particularly liked the interleaving of the personal and human narrative underlying the scientific journey. A particularly good example is Joachim Parrow's talk on the origins of the pi calculus. Of particular interest to LtU members is the panel on the future of functional programming languages, consisting of Phil Wadler, Xavier Leroy, David MacQueen, Martin Odersky, Simon Peyton-Jones, and Don Syme.
Tony Arcieri, author of the Reia Ruby-like language for the Erlang BEAM platform, wrote a piece in July, The Trouble with Erlang (or Erlang is a ghetto), bringing together a long laundry list of complaints about Erlang and the concepts behind it, and arguing at the end that Clojure now provides a better basis for parallel programming in practice.
While the complaints include many points about syntax, data types, and the like, the heart of the critique is two-fold: first, that Erlang has terrible problems managing memory and does not scale as advertised, and that these failures partly follow from "Erlang hat[ing] state. It especially hates shared state." He points to the Goetz and Click argument in Concurrency Revolution From a Hardware Perspective (2010) that local state is compatible with the Actors model. He further argues that SSA as it is used in Erlang is less safe than local state.
A good place to start is here. And here you can find several example programs with accompanying source code.
Memory Models: A Case for Rethinking Parallel Languages and Hardware by Sarita V. Adve and Hans-J. Boehm
This is a pre-print of the actual version.
The era of parallel computing for the masses is here, but writing correct parallel programs remains far more difficult than writing sequential programs. Aside from a few domains, most parallel programs are written using a shared-memory approach. The memory model, which specifies the meaning of shared variables, is at the heart of this programming model. Unfortunately, it has involved a tradeoff between programmability and performance, and has arguably been one of the most challenging and contentious areas in both hardware architecture and programming language specification. Recent broad community-scale efforts have finally led to a convergence in this debate, with popular languages such as Java and C++ and most hardware vendors publishing compatible memory model specifications. Although this convergence is a dramatic improvement, it has exposed fundamental shortcomings in current popular languages and systems that prevent achieving the vision of structured and safe parallel programming.
This paper discusses the path to the above convergence, the hard lessons learned, and their implications. A cornerstone of this convergence has been the view that the memory model should be a contract between the programmer and the system - if the programmer writes disciplined (data-race-free) programs, the system will provide high programmability (sequential consistency) and performance. We discuss why this view is the best we can do with current popular languages, and why it is inadequate moving forward. We then discuss research directions that eliminate much of the concern about the memory model, but require rethinking popular parallel languages and hardware. In particular, we argue that parallel languages should not only promote high-level disciplined models, but they should also enforce the discipline. Further, for scalable and efficient performance, hardware should be co-designed to take advantage of and support such disciplined models. The inadequacies of the state-of-the-art and the research agenda we outline have deep implications for the practice, research, and teaching of many computer science sub-disciplines, spanning theory, software, and hardware.
Concurrent Pattern Calculus by Thomas Given-Wilson, Daniele Gorla, and Barry Jay:
Concurrent pattern calculus drives interaction between processes by comparing data structures, just as sequential pattern calculus drives computation. By generalising from pattern matching to pattern unification, interaction becomes symmetrical, with information flowing in both directions. This provides a natural language for describing any form of exchange or trade. Many popular process calculi can be encoded in concurrent pattern calculi.
Barry Jay's Pattern Calculus has been discussed a few times here before. I've always been impressed with the pattern calculus' expressive power for computing over arbitrary structure. The pattern calculus supports new forms of polymorphism, which he termed "path polymorphism" and "pattern polymorphism", which are difficult to provide in other calculi. The closest I can think of would be a compiler-provided generalized fold over any user-defined structure.
This work extends the pattern calculus to the concurrent setting by adding constructs for parallel composition, name restriction and replication, and argues convincingly for its greater expressiveness as compared to other concurrent calculi. He addresses some of the obvious concerns for symmetric information flow of the unification operation.
Hassan Chaﬁ, Zach DeVito, Adriaan Moors, Tiark Rompf, Arvind Sujeeth, Pat Hanrahan, Martin Odersky, and Kunle Olukotun describe an approach to parallel DSLs that is a hybrid between external DSLs and internal DSLs in Language Virtualization for Heterogeneous Parallel Computing.
As heterogeneous parallel systems become dominant, application developers are being forced to turn to an incompatible mix of low level programming models (e.g. OpenMP, MPI, CUDA, OpenCL). However, these models do little to shield developers from the difﬁcult problems of parallelization, data decomposition and machine-speciﬁc details. Ordinary programmers are having a difﬁcult time using these programming models effectively. To provide a programming model that addresses the productivity and performance requirements for the average programmer, we explore a domain-speciﬁc approach to heterogeneous parallel programming.
We propose language virtualization as a new principle that enables the construction of highly efﬁcient parallel domain speciﬁc languages that are embedded in a common host language. We deﬁne criteria for language virtualization and present techniques to achieve them.We present two concrete case studies of domain-speciﬁc languages that are implemented using our virtualization approach.
While the motivation of the paper is parallelization the proposed design looks like LINQ expression trees dialed to 11.
The Scala research group at EPFL is excited to announce that they have won a 5 year European Research Grant of over 2.3 million Euros to tackle the "Popular Parallel Programming" challenge. This means that the Scala team will nearly double in size to pursue a truly promising way for industry to harness the parallel processing power of the ever increasing number of cores available on each chip.
As you can see from a synopsis of the proposal, Scala will be providing the essential mechanisms to allow a simpler programming model for common problems that benefit from parallel processing. The principal innovation is to use "language virtualization", combining polymorphic embeddings with domain-specific optimizations in a staged compilation process.
This may yet lead to very interesting developments.