NVIDIA CUDA is a general purpose parallel computing architecture that leverages the parallel compute engine in NVIDIA graphics processing units (GPUs) to solve many complex computational problems in a fraction of the time required on a CPU.

The first article in this series showed how to use Hadoop in a single-node cluster. This article continues with a more advanced setup that uses multiple nodes for parallel processing. It demonstrates the various node types required for multinode clusters and explores MapReduce functionality in a parallel environment.

Perl is a high-level, general-purpose, interpreted, dynamic programming language. Perl was originally developed in 1987 as a general-purpose Unix scripting language to make report processing easier.

Chipmaker AMD has integrated the open standard OpenCL specification for parallel programming into its ATI Stream development environment.

The purpose of this book is to help you understand how to program shared-memory parallel machines without risking your sanity. By describing the algorithms and designs that have worked well in the past, we hope to help you avoid at least some of the pitfalls that have beset parallel projects.

I was user of nvidia video card and I was happy to use it in F7 and F8 but unfortunately I replaced that card with a newer one. That time the card was ATI. Of course I did not check if that card is supported in Fedora 9 (I have to blame myself) and just took it. Recently I noticed the news about that how "open" ATI would be in the future. And now I experience their "openness" with my computer.

"The GCC team has released Version 4.3.0 of its GNU Compiler Collection. [...] The C++ standard library (libstdc++) has been given new classes for parallel data processing [...] The developers have also incorporated parts of the future ISO C++ standard..."

"Programming AI using standard programming languages, e.g., C/C++ or Java is not a good idea. It's too general purpose language. I'm considering to select between (Common) Lisp ..." -- Cool, but it's better on GNU systems ;-)

An intersting video showing how an application's performance increases when optimized for parallel execution. It's a book's promotion, but it is very instructive.