John Nolan shows the state of hardware acceleration with GPUs and FPGAs, why it's hard to write efficient code for them, and why to favor polymorphism over if statements for performance.
Hoping to make programming data-parallel hardware easier, Microsoft has published its open specification for C++ AMP. By building its implementation directly into Visual Studio 11 Microsoft seeks to improve access to the GPU for developers.
It is hard to leverage the parallelism provided by recent processor architectures. As these CPUs are now available even in the low cost price sector, the main challenge of software engineers is to utilize the processors in their applications or apps. The International Conference on Multicore Software Engineering, Performance, and Tools (MSEPT'12) will focus on possible answers.
In his latest book "Programming Concurrency on the JVM" author Venkat Subramaniam talks about the concurrency techniques using different JVM programming languages. He also discusses Software Transactional Memory (STM) and Actor-based Concurrency. InfoQ spoke with Venkat about his new book.
In this IEEE article, authors Frank Feinbube, Peter Troger and Andreas Polze discuss two major hardware trends in the desktop parallel programming space, multi-core CPU architectures and Graphics Processing Units (GPUs). They also talk about the best practices for GPU code optimization like algorithm design, memory transfer, control flow, instructions and precision.
Danny Coward talks on how Oracle intends to maintain Java in the front line by investing in two features that are trendy today: support for multiple JVM languages and parallel programming.
Stuart Halloway discusses how we use a total control time model, proposing a different one that represents the world more accurately helping to solve some of the concurrency and parallelism problems.
Kostis Sagons talks about how type checking can help with a dynamic language like Erlang and how static analysis tools like Dialyzer or automated refactoring tools like Tidier help keep code clean.
Ville Tuulos talks about Disco, the Map/Reduce framework for Python and Erlang, real-world data mining with Python, the advantages of Erlang for distributed and fault tolerant software, and more.
