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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.

There's much excitement about JDK 7 and in particular Lambdas! I've waded through the bloat to help you get an understanding of it. If you search for JDK 7 in your favourite search engine the chances are you'll hit the controversies surrounding lambadas in Java fairly early on in your hunt. It's a contentious subject, which means it's getting a lot of attention from a lot of clever people, but this in turn makes the process slow and adds difficulty in making decisions. My take is that lambdas will be in JDK 7 - you can see plenty of evidence of that around the web and in the snapshot builds. That said, no decision is concrete (which is a wise tip from The Pragmatic Programmer no less!). This article is aimed at those who don't know much about functional programming or what Lambdas, Closures or Currying are and want to get 'primed'.

The latest episode of the Robots Podcast looks at the following scenario: Imagine being able to throw a hand-full of smart matter in a tank full of liquid and then pulling out a ready-to-use wrench once the matter has assembled. This is the vision of this episode's guests Michael Tolley and Jonas Neubert from the Computational Synthesis Laboratory run by Hod Lipson at Cornell University, NY. Tolley and Neubert give an introduction into Programmable Matter and then present their research on stochastic assembly of matter in fluid, including both simulation (see video above) and real-world implementation. Read on or tune in!

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.

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

This API provides users with a standard FPGA communication interface from C++ code. It is intended to encourage more widespread adoption of FPGAs and reconfigurable computing platforms—particularly among Windows application developers

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.

One challenge designers face is the need to translate their algorithms into code for use in embedded targets. The task has proven to be long and prone to error. This article examines how the use of high-level design tools and C code generation capabilities improves the design flow by exploring different use cases and how to reduce the amount of embedded technology expertise required to program embedded targets.

In this demo we showcase efforts in MSR to collaborate with external researchers to explore the application of new technologies, specifically Dryad and DryadLINQ, to big data research problems in science. We also highlight our efforts to provide software and services to academics across the world, through the release of Dryad and DryadLINQ free of charge to the research community, along with associated programming guides, user documentation, and code libraries. Dryad is a general-purpose distributed computing engine, more flexible than MapReduce or Hadoop!, that was designed to simplify the task of implementing distributed applications on clusters of Windows computers. DryadLINQ is an abstraction layer which simplifies the process of implementing Dryad-based applications. Microsoft Research is acutely aware of the ubiquity of big data and the challenges this presents. We are offering researchers the tools, resources and collaboration to explore this new area.