Marvin is a deep learning framework designed first and foremost to be hackable. It is naively simple for fast prototyping, uses only basic C/C++, and only calls CUDA and cuDNN as dependencies.
CUDA lets you work with familiar programming concepts while developing software that can run on a GP This is the first of a series of articles to introduce you to the power of CUDA -- through working code -- and to the thought process to help you map applications onto multi-threaded hardware (such as GPUs) to get big performance increases. Of course, not all problems can be mapped efficiently onto multi-threaded hardware, so part of my thought process will be to distinguish what will and what won't work, plus provide a common-sense idea of what might work "well-enough". "CUDA programming" and "GPGPU programming" are not the same (although CUDA runs on GPUs). CUDA permits working with familiar programming concepts while developing software that can run on a GPU. It also avoids the performance overhead of graphics layer APIs by compiling your software directly to the hardware (GPU assembly language, for instance), thereby providing great performance.
The buzz has been loud at times. Almost sounds to good to be true. Use your video card for HPC and get a 10, or maybe even 50 times, speed up of your application. Those kind of comments get my attention. Initially there were some skeptics, but the results keep coming. And, the results were not from some academic lab with some esoteric application.
GpuCV is an open-source GPU-accelerated image processing and Computer Vision library. It offers an Intel's OpenCV-like programming interface for easily porting existing OpenCV applications, while taking advantage of the high level of parallelism and computing power available from recent graphics processing units (GPUs). It is distributed as free software under the CeCILL-B license.
Modern graphics processing units (GPUs) contain hundreds of arithmetic units and can be harnessed to provide tremendous acceleration for many numerically intensive scientific applications. The key to effective utilization of GPUs for scientific computing
A. Cheik Ahamed, и F. Magoulès. Distributed Computing and Applications to Business, Engineering Science (DCABES), 2013 12th International Symposium on, стр. 16-20. (сентября 2013)
A. Cheik Ahamed, и F. Magoulès. High Performance Computing and Communications, 2014 IEEE 6th Intl Symp on Cyberspace Safety and Security, 2014 IEEE 11th Intl Conf on Embedded Software and Syst (HPCC,CSS,ICESS), 2014 IEEE Intl Conf on, стр. 121-128. (августа 2014)
A. Cheik Ahamed, и F. Magoulès. Distributed Computing and Applications to Business, Engineering and Science (DCABES), 2014 13th International Symposium on, стр. 19-23. (ноября 2014)
C. Abal-Kassim, и M. Frédéric. Distributed Computing and Applications to Business, Engineering and Science (DCABES), 2014 13th International Symposium on, стр. 46-50. (ноября 2014)