This affordable platform is designed for developing and implementing high performance, parallel processing applications developed to take advantage of . Applications developed to take . The open source, community supported platform is ideal for developers in industry, academia, or for personal use. Основатели проекта признаются, что черпали вдохновение из . GitHub is where people build software. Сами продажи начались вчера.

На хабре ранее упоминалось, например здесь habrahabr. The board is intended to democratise access to parallel programming, due to the price, . Supercomputing for everyone. Open source, affordable, fun.

This board is an obvious stand-out, featuring an . Parallella is a single. By connecting many simple general-purpose RISC CPUs with a Network-on-Chip memory system, the Epiphany co-processor architecture provides promising power-efficiency. However, many new users find it very hard to get started with developing so. What is the parallella ?

In short, the parallella is a single board computer, much like the Raspberry Pi. It similarly sized and has roughly the same IO set as the Pi. The primary SoC is a Xilinx Zynq, a duel core ARMvprocess coupled to an FPGA, with a secondary Epiphany multi-core coprocessor. For the price and power . Inspired by great hardware communities like Raspberry Pi and Arduino, we see a critical need for a truly open, high-performance computing platform that will close the knowledge gap in parallel programing.

Which means that with a few hacks, you could have a GFlop machine in your hand. I want to try my hand at writing a real, efficient, many:many message-passing API on top of SHM. Two general purpose expansion connectors.

Department of Information Technology. School of Electrical and Computer Engineering. National Technical University of Athens. Magnus Lång, Kostis Sagonas. To give a comparison, our 4. A dialog will appear prompting you to specify some configuration information for your parallella.

You will additionally need to select your processor . Many of our algorithms are implemented in a way that enables them to execute many calculations simultaneously. It is called parallelization.