Group items tagged

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.

In order to test and run benchmarks on the work I'm doing around creating a managed variant of the JSR-166y ForkJoinPool along with supporting infrastructure for use with Scala Actors, I'm creating a test harness that captures a host of environmental factors about how it was run, and writing sbt actions to make it easy to run the benchmarks and automatically permute the variables.

Welcome to the first episode of Cloud Cover!  Join Ryan and Steve as they cover the Windows Azure platform, digging into features, discussing the latest news and announcements, and sharing tips and tricks. Follow and interact with us at @cloudcovershow In this episode: Learn about the Service Management API and how to use PowerShell cmdlets to manage your cloud services. Find out how to get started quickly on the Windows Azure platform. Other topics include: SQL Azure updates! Windows Azure Drives (XDrive).  Hear about some cool new Windows Azure storage management tools. Azure Reader architecture.

Asynchronous work flows are a very powerful tool in programming. They allow your threads to do other work while you wait for results from a long running piece of work. How would you write an asynchronous work flow in C#? Logically you might consider chaining together callbacks.

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.

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.

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.

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.