high performance computing on graphics processing units: hgpu.org

In an exploration context, Oil and Gas (O&G) companies rely on HPC to accelerate depth imaging algorithms. Solutions based on CPU clusters and hardware accelerators are widely embraced by the industry. The Graphics Processing Units (GPUs), with a huge compute power and a high memory bandwidth, had attracted significant interest. However, deploying heavy imaging workflows, […]

=Publication= ICOIP 2016 conference Proceedings by IEEE *Reviewed by IEEE Xplore and Ei Compendex. =Keynote Speakers= Prof. Ryszard Tadeusiewicz AGH University of Science and Technology, Poland Prof. Alexander Balinsky Cardiff University, UK Prof. BEN-OTHMAN Jalel University of Paris 13, France =Agenda= June 10, 2016 – Registration & Conference Materials Collection June 11, 2016 – Keynote […]

General-relativistic radiative transfer (GRRT) calculations coupled with the calculation of geodesics in the Kerr spacetime are an essential tool for determining the images, spectra and light curves from matter in the vicinity of black holes. Such studies are especially important for ongoing and upcoming millimeter/submillimeter (mm/sub-mm) Very Long Baseline Interferometry (VLBI) observations of the supermassive […]

We studied the nonequilibrium aging behavior of the Random Field Ising Model in three dimensions for various values of the disorder strength. This allowed us to investigate how the aging behavior changes across the ferromagnetic-paramagnetic phase transition. We investigated a large system size of $N=256^3$ spins and up to $10^8$ Monte Carlo sweeps. To reach […]

We provide a case study of work-stealing, a popular method for run-time load balancing, on FPGAs. Following the Cederman-Tsigas implementation for GPUs, we synchronize workitems not with locks, mutexes or critical sections, but instead with the atomic operations provided by Altera’s OpenCL SDK. We evaluate work-stealing for FPGAs by synthesizing a K-means clustering algorithm on […]

In particle physics, Higgs Boson to tau-tau decay signals are notoriously difficult to identify due to the presence of severe background noise generated by other decaying particles. Our approach uses neural networks to classify events as signals or background noise.

Approximate computing trades off computation quality with the effort expended and as rising performance demands confront with plateauing resource budgets, approximate computing has become, not merely attractive, but even imperative. In this paper, we present a survey of techniques for approximate computing (AC). We discuss strategies for finding approximable program portions and monitoring output quality, […]

High performance computing studies the construction and programming of computing system with tremendous computational power playing a key role in scientific computing and research across disciplines. The graphics processing unit (GPU) developed for fast 2D and 3D visualizations has turned into a programmable general purpose accelerator device boosting today’s high performance clusters. Leveraging these computational […]

The MapReduce framework is a programming model proposed by Google to process large datasets. It is an efficient framework that can be used in many areas, such as social network, scientific research, electronic business, etc. Hence, more and more MapReduce frameworks are implemented on different platforms, including Phoenix (based on multicore CPU), MapCG (based on […]

Dedispersion, the removal of deleterious smearing of impulsive signals by the interstellar matter, is one of the most intensive processing steps in any radio survey for pulsars and fast transients. We here present a study of the parallelization of this algorithm on many-core accelerators, including GPUs from AMD and NVIDIA, and the Intel Xeon Phi. […]

We study the effect of isospin-dependent nuclear forces on the pasta phase in the inner crust of neutron stars. To this end we model the crust within the framework of quantum molecular dynamics (QMD). For maximizing the numerical performance, the newly developed code has been implemented on GPU processors. As a first application of the […]

In this paper, a fast and practical GPU-based implementation of Fuzzy C-Means (FCM) clustering algorithm for image segmentation is proposed. First, an extensive analysis is conducted to study the dependency among the image pixels in the algorithm for parallelization. The proposed GPU-based FCM has been tested on digital brain simulated dataset to segment white matter(WM), […]