JCublas is providing Java bindings for the NVIDIA CUDA BLAS implementation, thus making the parallel processing power of modern graphics hardware available for Java programs.
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
NVIDIA Corporation, the world leader in visual computing technologies and the inventor of the GPU, today announced that the Korea Institute of Science and Technology Information (KISTI) Supercomputing Center has selected NVIDIA Quadro® FX 5600 graphics c
A. Dallmann, P. Beck, and J. von Gudenberg. Parallel Processing and Applied Mathematics, volume 8385 of Lecture Notes in Computer Science, Springer Berlin Heidelberg, (2014)