Tiago over at http://www.nehalemlabs.net/ has posted four articles on his "little corner of the internet" covering the use of OpenCL via Python. Parallel programming with opencl and python Parallel programming with opencl and python: vectors and concurrency Parallel programming with opencl and python: parallel reduce Parallel programming with opencl and python: parallel scan
AMD have launched the AMD FirePro™ W8100 professional graphics card based on their second generation Graphics Core Next (GCN) architecture. "Powered by OpenCL", the AMD FirePro W8100 is designed for the next generation of 4K CAD (Computing Aided Design) and Media and Entertainment (M&E) workflows, engineering analysis and supercomputing applications. The AMD FirePro W8100 features [...]
Abstract Due to the increasing complexity of multi/many-core architectures (with their mix of caches and scratch-pad memories) and applications (with different memory access patterns), the performance of many workloads becomes increasingly variable. In this work, we address one of the main causes for this performance variability: the efficiency of the memory system. Specifically, based on [...]
Abstract With the advent of many-core computer architectures such as GPGPUs from NVIDIA and AMD, and more recently Intel’s Xeon Phi, ensuring performance portability of HPC codes is potentially becoming more complex. In this work we have focused on one important application area — structured grid codes — and investigated techniques for ensuring performance portability [...]
Abstract To fully tap into the potential of today heterogeneous machines, offoading parts of an application on accelerators is no longer sufficient. The real challenge is to build systems where the application would permanently spread across the entire machine, that is, where parallel tasks would be dynamically scheduled over the full set of available processing [...]
Abstract The OpenCL API allows for the abstract expression of parallel, heterogeneous computing, but hardware implementations have substantial implementation differences. The abstractions provided by the OpenCL API are often insufficiently high-level to conceal differences in hardware architecture. Additionally, implementations often do not take advantage of potential performance gains from certain features due to hardware limitations [...]
Abstract: Today, it is the amount of available data rather than its acquisition that poses a significant challenge to computer science. It is the issue of extracting valuable and useful information from increasing data volumes, and it has manifested itself throughout most of modern global digital frameworks, like economic analysis, weather forecast, or, indeed, national [...]
A new Intel tutorial (May 2014) provides guidelines on using OpenCL in Android applications. The tutorial is an interactive image processing application with the main focus being to show how to use OpenCL in an Android application, how to start writing OpenCL code, and how to link to OpenCL runtime. The tutorial shows a typical [...]
Abstract: For 3D skeleton extraction, the algorithm based on generalized potential fields, known as the outstandingly flexible and robust method, is suffering from seriously heavy computational burden. In this paper, we put forward a parallel algorithm based on OpenCL heterogeneous parallel framework, which can make full use of the great computing power provided by heterogeneous [...]
Abstract: Intense computations in engineering and science, especially bioinformatics have been made practical by the recent advances in Graphical Processing Unit (GPU) computing technology. In this study, implementation and performance evaluations for a GPU-accelerated dynamic functional connectivity (DFC) analysis, which is an analysis method for investigating dynamic interactions among different brain networks, is presented. [...]