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The first textbook of its kind, Programming Massively Parallel Processors: A Hands-on Approach launches today, authored by Dr. David B. Kirk, NVIDIA Fellow and former chief scientist, and Dr. Wen-mei Hwu, who serves at the University of Illinois at Urbana-Champaign as Chair of Electrical and Computer Engineering in the Coordinated Science Laboratory, co-director of the Universal Parallel Computing Research Center and principal investigator of the CUDA Center of Excellence. The textbook, which is 256 pages, is the first aimed at teaching advanced students and professionals the basic concepts of parallel programming and GPU architectures. Published by Morgan Kaufmann, it explores various techniques for constructing parallel programs and reviews numerous case studies. With conventional CPU-based computing no longer scaling in performance and the world’s computational challenges increasing in complexity, the need for massively parallel processing has never been greater. GPUs have hundreds of cores capable of delivering transformative performance increases across a wide range of computational challenges. The rise of these multi-core architectures has raised the need to teach advanced programmers a new and essential skill: how to program massively parallel processors.

F# is Microsoft's first functional programming language to be included as one of Visual Studio's official set of languages. F# is a succinct, efficient, expressive functional/object-oriented programming language under joint development by Microsoft Developer Division and Microsoft Research. During the course of Erik Meijer's fantastic lecture series on functional programming fundamentals several of you asked for examples of specific topics in F#. Well, we listened. Dr. Don Syme is a principal researcher in MSR Cambridge. He has a rich history in programming language research, design, and implementation (C# generics being one of his most recognized implementations), and is the principle creator of F#. Who better to lecture on the topic than Don? This three part series will serve as an introduction to F#, including insights into the rationale behind the history and creation of Microsoft's newest language.

We had to cover monads eventually, and there are many great monad tutorials out there (see, for example, here: http://www.haskell.org/haskellwiki/Tutorials#Using_monads). In fact, there are web resources concerned solely with organizing the many monad tutorials available in the wild, and developing new monad tutorials seems to be a popular sport in the Haskell community.Today, Ralf Lämmel's lecture goes back to the roots, essentially revisiting Wadler's "The essence of functional programming"—the 1992 paper that discovered monads and popularized their use in functional programming. Ralf Lämmel's lecture and accompanying code distribution show Wadler's seminal insight: those original scenarios and observations still make sense today. Indeed, Simon Marlow (a Haskell/GHC high priest @ MSR Cambridge) recently noted: "it's still the best monad tutorial" (see http://twitter.com/simonmar/status/21397398061).

The past couple of years have seen some extended debates on whether Scala is a functional language. On the one hand, Scala offers essentially all programming constructs typically associated with functional programming and a lot of Scala code is purely functional. On the other hand quite a few people disagree that Scala is a functional language. For instance, Robert Fischer writes that Scala is Not a Functional Programming Language and Daniel Spiewak summarizes some of the arguments asking Is Scala Not Functional Enough?

Ralph Johnson presents a pattern language that he and his colleagues are working on in an attempt to solve the hard issues of parallel programming through a set of design patterns: Structural Patterns, Computational Patterns, Parallel Algorithm Strategy Patterns, Implementation Strategy Patterns, and Concurrent Execution Patterns.

You would be forgiven if you thought the "F" in F# -- which made its debut as part of Visual Studio 2010 -- stands for "functional." After all, F# (pronounced "F sharp") is a functional programming language for the .NET Framework that combines the succinct, expressive, and compositional style of functional programming with the runtime, libraries, interoperability, and object model of .NET. But Don Syme, inventor of F# and leader of the team that incubated the language, has a different, truncated, and entirely whimsical definition. "In the F# team," says Syme, a principal researcher at Microsoft Research Cambridge, "We say, 'F is for Fun.' F# enables users to write simple code to solve complex problems. Programming with F# really does make many programming tasks simpler, and our users have consistently reported that they've found using the language enjoyable." Indeed, F#, which has been developed in a partnership between Microsoft Research and the Microsoft Developer Division, is already popular with the .NET developer community. The language is widely known in the academic community and among thought leaders, and the list of admirers will only increase as Visual F#, the result of a partnership between Microsoft Research Cambridge and Microsoft's Developer Division, becomes a first-class language in Visual Studio 2010.

The R programming language was featured about a year ago in a New York Times article (http://bit.ly/iaqQ). I've been an R user since 2000, so I've collected some resources for people who want to get started with R.   The first place to start is the R Project web site at http://www.r-project.org/. Next, you'll actually want to install R itself. There are several options, depending on your environment.

As many long time readers know, I am an enthusiast of the F# programming language.  I make no apologies for the fact that, if you are developing software on the .NET platform, F# is one of the better choices you can make for numerous reasons.  It is one of the reasons I proudly contributed as a co-author to the book, Professional F# 2.0, which is being published by Wrox in October. Some of the oft cited benefits of F# are that, to distill them quickly, it is good at doing intensely mathematical operations, it is built for parallelism, and it is good at helping define domain specific languages.  Those benefits are so often cited by speakers on the F# speaker circuit that they pretty much seem cliche to me at this point (note, yours truly is proud to call himself a member of said circuit, and often gives this talk!)  As great as these features are, there are a couple features, that in my more mundane F# experiences, seem to stand out as things that “save my ass”, for lack of a better phrase, more often than not.

In case you haven’t realized it, the new trend in computer chip technology is multi-core. This is where most of the speed improvements moving forward will come from on our computers. To take full advantage of this however it is necessary to design your applications using Parallel Programming practices, also known as "parallelism". In today’s episode, we will meet with Stephen Toub, who will share with us some of the overarching concepts associated with parallelism, and some of the ways we are trying to empower developers to develop applications to take advantage of it.

Since I wrote a bittorrent client in both Erlang and Haskell, etorrent and combinatorrent respectively, I decided to put up some bait. This might erupt in a language war and “My language is better than yours”, but I feel I am obligated to write something subjective. Here is to woes of programming in Haskell and Erlang.Neither Haskell, nor Erlang was a first language for me. I have programmed serious programs in C, Standard ML, Ocaml, Python, Java and Perl; tasted the cake of Go, Javascript, Scheme and Ruby; and has written substantial stuff in Coq and Twelf. I love static type systems, a bias that will rear its ugly head and breathe fire.I have written Haskell code seriously since 2005 and Erlang code seriously since 2007. I have programmed functionally since 1997 or so. My toilet reading currently is “Categories for the working mathematician” by Mac Lane. Ten years ago it was “ML for the working programmer” by Paulson.Enough about me.

Microsoft Technical Fellow and C# creator Anders Hejlsberg explains the new C# and VB.NET asynchronous programming model, available as Async CTP now, which makes async programming much easier for .NET developers.

We describe a practical method for reasoning about realistic concurrent programs. Our method allows global two-state invariants that restrict update of shared state. We provide simple, sufficient conditions for checking those global invariants modularly. The method has been implemented in VCC, an automatic, sound, modular verifier for concurrent C programs. VCC has been used to verify functional correctness of tens of thousands of lines of Microsoft's Hyper-V virtualization platform and of SYSGO's embedded real-time operating system PikeOS.

Andrew Phillips holds the title of Scientist with Microsoft Research Cambridge, and he's working on a method of programming that compiles into DNA. Part of this involves a visual programming language called Stochastic Pi Machine, or SPiM. This system models biological processes to help give researchers feedback on how organisms will react to modifications. The hope is that this can be used to help scientists program for large biological systems using modular components compiled to DNA. Yes, I’m in way over my head here, but I do my best to ask Andrew about the role this will play in medical treatment going forward, what it means to DNA computing, and the ability of back-engineering the genetic code we don’t use now

Andrew Phillips holds the title of Scientist with Microsoft Research Cambridge, and he's working on a method of programming that compiles into DNA. Part of this involves a visual programming language called Stochastic Pi Machine, or SPiM. This system models biological processes to help give researchers feedback on how organisms will react to modifications. The hope is that this can be used to help scientists program for large biological systems using modular components compiled to DNA. Yes, I’m in way over my head here, but I do my best to ask Andrew about the role this will play in medical treatment going forward, what it means to DNA computing, and the ability of back-engineering the genetic code we don’t use now.

CHESS is a tool for disciplined testing of concurrent programs. CHESS requires users to develop concise concurrent tests for that explore one concurrency scenario—or a few scenarios—in their programs. Given such a test, CHESS systematically enumerates all behaviors of the program being tested to find concurrency errors, data races, deadlocks, and livelocks.

F# is ideal for data-rich, concurrent and algorithmic development: "simple code to solve complex problems". F# is a simple and pragmatic programming language combining functional, object-oriented and scripting programming, and supports cross-platform environments including PC, Mac, and Linux. We'll provide the tutorials, resources and tools you’ll need to begin working with F# right away.

In Stack Machines, Expression Evaluation, and the Magic of Reverse Polish, I showed how expressions can be evaluated by rewriting them in reverse Polish and translating this into machine-code instructions for a stack machine. I demonstrated with a stack-machine interpreter that I'd written as part of a working model of a Pascal compiler. But as well as expressions, the compiler needs to compile assignments and jumps, so — in my progress towards explaining the compiler — I'm going to extend the machine code so it can handle these. I'll demonstrate by interpreting a program that calculates five factorial.

In this interview made by InfoQ’s Sadek Drobi, Don Syme, a Senior Researcher at Microsoft Research, answers questions mostly on F#, but also on functional programming, C# generics, type classes in Haskell, similarities between F# and Scala.

Robots mean many things to many people, and National Instruments offers intuitive and productive design tools for everything from designing autonomous vehicles to teaching robotics design principals. The NI LabVIEW graphical programming language makes it easy to program complex robotics applications by providing a high level of abstraction for sensor communication, obstacle avoidance, path planning, kinematics, steering, and more.

The Khronos™ Group today announced the release of the OpenGL® 4.0 specification; a significant update to the most widely adopted 2D and 3D graphics API (application programming interface) that is deployed on all major desktop operating systems.  OpenGL 4.0 brings the very latest in cross-platform graphics acceleration and functionality to personal computers and workstations and the OpenGL standard serves as the basis for OpenGL® ES, the graphics standard on virtually every shipping smart phone.   The OpenGL 4.0 specification has been defined by the OpenGL ARB (Architecture Review Board) working group at Khronos, and includes the GLSL 4.00 update to the OpenGL Shading language in order to enable developers to access the latest generation of GPU acceleration with significantly enhanced graphics quality, acceleration performance and programming flexibility.  This new release continues the rapid evolution of the royalty-free OpenGL standard to enable graphics developers to portably access cutting-edge GPU functionality across diverse operating systems and platforms. The full specification is available for immediate download at http://www.opengl.org/registry .