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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.

Erlang allows for massively scalable concurrency, often with millions of lightweight, thread-like components known as actors. Unfortunately, using Erlang requires rewriting all of your legacy code into a rather esoteric language. But there are other options, such as the little known CCR platform that was developed by .NET's robotics department. Actor based languages such as Erlang are able to achieve high degrees of parallelism by using the Actor model. Under this model the fundamental unit of concurrency is not a thread or fiber, but rather something much smaller. Known as a "process" in Erlang, each unit of concurrency has a base overhead of about 1200 bytes on a 32-bit system. By comparison, a thread on the Windows operating system defaults to 1 MB just for the stack, additional space is also needed for bookkeeping and thread local storage. Because they are so lightweight, an application can spawn literally millions of processes simultaneously.

In this session, Don McCrady discusses how C++ programmers can fully utilize multicore in their applications using the Concurrency Runtime (ConcRT), the Parallel Pattern Library (PPL), and the Asynchronous Agents Library that ship with Visual Studio 2010.

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.

When a data (typically a variable) can be accessed by several threads, you must synchronize the access to the data to ensure visibility and correctness.

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.

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.

When C# 4.0 was released we C# developers were given a new toy. to play with, and that's Parallel Programming. This is done with the System.Threading.Tasks.Parallel Namespace. This allows for parallel loops & regions (to be discussed at a later date). What is parallel programming you ask, well in todays age most, if not all, household computers have multi-core processors, and parallel programming allows us to take davantage of this new found power, well not actually new as multi-core processors have been around a while now, but software has not kept up with the changes in hardware.

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.