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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.

It is no secret that field-programmable gate arrays (FPGAs) are getting bigger and more complex all the time. The fabrication process creates smaller transistors and makes more dense chips packing more digital processing per nanometer. Engineers love to see advancement because it means they can do more with modern silicon, and many times NI LabVIEW FPGA Module technology helps by abstracting the complexity to a higher level so that engineers can more smoothly take advantage of these improvements.  Unfortunately, there is one issue with FPGAs that continues to be a time sink and only gets worse with denser FPGAs: compilation time.

Have you ever written code that behaves correctly under a simulator only to have intermittent failures in the field? Or maybe your code no longer functions properly when you compile with a newer version of your tool chain. You review your test bench and verify 100 percent complete test coverage and that all tests have passed with no errors--yet the problem stubbornly remains. While designers understandably place great emphasis on coding and simulation, they often have only a nodding acquaintance with the internal workings of the silicon within an FPGA. As a result, incorrect logic synthesis and timing problems, rather than logic errors, are the cause of most logic failures. But writing FPGA code that creates predictable, reliable logic is simple if designers take the right steps. In FPGA design, logic synthesis and related timing closure occur during compilation. And many things, including I/O cell structure, asynchronous logic and timing constraints, can have a big impact on the compilation process, varying results with each pass through the tool chain. Let's take a closer look at ways to eliminate these variances to better and more quickly achieve timing closure.

Today I'm excited to share the project a small team of amazing people and I have been working on for the past two years; HipHop for PHP. With HipHop we've reduced the CPU usage on our Web servers on average by about fifty percent, depending on the page. Less CPU means fewer servers, which means less overhead. This project has had a tremendous impact on Facebook. We feel the Web at large can benefit from HipHop, so we are releasing it as open source this evening in hope that it brings a new focus toward scaling large complex websites with PHP. While HipHop has shown us incredible results, it's certainly not complete and you should be comfortable with beta software before trying it out. HipHop for PHP isn't technically a compiler itself. Rather it is a source code transformer. HipHop programmatically transforms your PHP source code into highly optimized C++ and then uses g++ to compile it. HipHop executes the source code in a semantically equivalent manner and sacrifices some rarely used features — such as eval() — in exchange for improved performance. HipHop includes a code transformer, a reimplementation of PHP's runtime system, and a rewrite of many common PHP Extensions to take advantage of these performance optimizations.

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.

DSP core licensor Ceva Inc. is due to unveil a software-programmable multimedia video processor architecture at the Mobile World Congress in Barcelona next week. The multicore architecture, called MM3000, which comes complete with C compilers, power management provision and an RTOS/multithreading scheduler is intended to be able to process any and all video codecs up to the highest resolutions and frame rates currently available as well as future codecs for things like 3-D video.

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.

The VMKit project is an implementation of a JVM and CLI virtual machine (.Net is an implementation of the CLI). It translates Java bytecode and MSIL in the LLVM IR and uses the LLVM framework for optimizations and compilation. For garbage collection, it uses MMTk. You can get and build the source today.

Today sees the launch of Visual Studio 2010, at five launch events around the world, as announced by Bob Muglia, Jason Zander and S. Somasegar, and presented live today in Las Vegas.   Visual Studio 2010 includes the official version 2.0 of the F# language. As is our custom on the F# team, we also release a matching MSI and ZIP of F# 2.0 (for use with Visual Studio 2008 and as a standalone compiler on a range of platforms)   Today represents the culmination of 7 years of work on the language at Microsoft Research, and, more recently, the Microsoft Developer Division. I am immensely proud of what we’ve achieved. F# brings a productive functional and object-oriented programming language to .NET, extending the platform to new audiences in technical, algorithmic, data-rich, parallel and explorative domains, and its inclusion in Visual Studio 2010 represents a huge milestone for the language.   To help understand what we’re doing with F#, I’ve listed some of the common questions people have about the language below.  We thank everyone who has been involved in the production of F#, especially the many users who have given us feedback on the language!