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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.

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.

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.

ScalaModules is an open source project aimed at providing fluent support for OSGi to Scala developers. It takes advantage of Scala's infix operator notation, higher order functions, and implicit conversions. ScalaModules transparently uses the Scala compiler to wrap an OSGi BundleContext with its own RichBundleContext model. This general technique is not unusual for creating DSLs in mainstream languages. Sean McDirmid uses similar tricks for his C# Bling library for WPF, except that Bling must overcome the lack of C# offering comparable extensions to Scala.

OpenGL 3.1 introduced two new sources from where shaders can retrieve their data, namely uniform buffers and texture buffers. These can be used to accelerate rendering when heavy usage of application provided data happens like in case of skeletal animation, especially when combined with geometry instancing. However, even if the functionality is in the core specification for about a year now, there are few demos out there to show their usage, as so, there is a big confusion around when to use them and which one is more suitable for a particular use case. Both AMD and NVIDIA have updated their GPU programming guides to present the latest facilities provided by both OpenGL and DirectX, however I still see that people don’t really understand how they work and that prevents them from effectively taking advantage of these features. Once, at some online forum, I found somebody arguing why is this whole confusion introduced by the Khronos Group and why there is no general buffer type to use instead and the decision whether to use uniform or texture buffers should be a decision made by the driver. This particular post motivated me to write this article.

Verification of algorithm-intensive systems is a long, costly process. Studies show that the majority of flaws in embedded systems are introduced at the specification stage, but are not detected until late in the development process. These flaws are the dominant cause of project delays and a major contributor to engineering costs. For algorithm-intensive systems —including systems with communications, audio, video, imaging, and navigation functions— these delays and costs are exploding as system complexity increases. It doesn't have to be this way. Many designers of algorithm-intensive systems already have the tools they need to get verification under control. Engineers can use these same tools to build system models that help them find and correct problems earlier in the development process. This can not only reduce verification time, but also improves the performance of their designs. In this article, we'll explain three practical approaches to early verification that make this possible. First, let's examine why the current algorithm verification process is inefficient and error-prone. In a typical workflow, designs start with algorithm developers, who pass the design to hardware and software teams using specification documents.

Orchard will create shared components for building ASP.NET applications and extensions, and specific applications that leverage these components to meet the needs of end-users, scripters, and developers. Additionally, we seek to create partnerships with existing application authors to help them achieve their goals. Orchard is delivered as part of the ASP.NET Open Source Gallery under the CodePlex Foundation. It is licensed under a New BSD license, which is approved by the OSI. The intended output of the Orchard project is three-fold: Individual .NET-based applications that appeal to end-users , scripters, and developers A set of re-usable components that makes it easy to build such applications A vibrant community to help define these applications and extensions In the near term, the Orchard project is focused on delivering a .NET-based CMS application that will allow users to rapidly create content-driven Websites, and an extensibility framework that will allow developers and customizers to provide additional functionality through extensions and themes. Erik Porter and Nathan Heskew are two of the developers of Orchard. Do they look familiar? Sure they do. They used to be devs on the C9 team.

We consider the problem of specifying combinations of data structures with complex sharing in a manner that is both declarative and results in provably correct code. In our approach, abstract data types are specified using relational algebra and functional dependencies. We describe a language of decompositions that permit the user to specify different concrete representations for relations, and show that operations on concrete representations soundly implement their relational specification. It is easy to incorporate data representations synthesized by our compiler into existing systems, leading to code that is simpler, correct by construction, and comparable in performance to the code it replaces.