high performance computing on graphics processing units: hgpu.org

Graphics Processing Units (GPUs) are becoming the workhorse of scalable computations. MADNESS is a scientific framework used especially for computational chemistry. Most MADNESS applications use operators that involve many small tensor computations, resulting in a less regular organization of computations on GPUs. A single GPU kernel may have to multiply by hundreds of small square […]

Efficient large-scale scientific computing requires efficient code, yet optimizing code to render it efficient simultaneously renders the code less readable, less maintainable, less portable, and requires detailed knowledge of low-level computer architecture, which the developers of scientific applications may lack. The necessary knowledge is subject to change over time as new architectures, such as GPGPU […]

Utilising graph theory is a common activity in computer science. Algorithms that perform computations on large graphs are not always cost effective, requiring supercomputers to achieve results in a practical amount of time. Graphics Processing Units provide a cost effective alternative to supercomputers, allowing parallel algorithms to be executed directly on the Graphics Processing Unit. […]

Video codecs have undergone dramatic improvements and increased in complexity over the years owing to various commercial products like mobiles and Tablet PCs. With the emergence of standards, such H.264 which has emerged as the de facto standard for video, uniformity in the delivery of video is observed. With constraints of memory and transmission bandwidth, […]

We perform large-scale Monte Carlo simulations of the classical XY model on a three-dimensional $Ltimes L times L$ cubic lattice using the graphics processing unit (GPU). By the combination of Metropolis single-spin flip, over-relaxation and parallel-tempering methods, we simulate systems up to L=160. Performing the finite-size scaling analysis, we obtain estimates of the critical exponents […]

If you need to learn CUDA but don’t have experience with parallel computing, CUDA Programming: A Developer’s Introduction offers a detailed guide to CUDA with a grounding in parallel fundamentals. It starts by introducing CUDA and bringing you up to speed on GPU parallelism and hardware, then delving into CUDA installation. Chapters on core concepts […]

Medical imaging currently plays a crucial role throughout the entire clinical applications from medical scientific research to diagnostics and treatment planning. However, medical imaging procedures are often computationally demanding due to the large three-dimensional (3D) medical datasets to process in practical clinical applications. With the rapidly enhancing performances of graphics processors, improved programming support, and […]

Markov-Chain Monte-Carlo (MCMC) methods are an important class of simulation techniques, which execute a sequence of simulation steps, where each new step depends on the previous ones. Due to this fundamental dependency, MCMC methods are inherently hard to parallelize on any architecture. The upcoming generations of hybrid CPU/GPGPU architectures with their multi-core CPUs and tightly […]

Ambient occlusion techniques were introduced to improve data comprehension by bringing soft fading shadows to the visualization of 3D datasets. They consist in attenuating light by considering the occlusion resulting from the presence of neighboring structures. Nevertheless they often come with an important precomputation cost, which prevents their use in interactive applications based on transfer […]

Modern processor architectures, in addition to having still more cores, also require still more consideration to memory-layout in order to run at full capacity. The usefulness of most languages is deprecating as their abstractions, structures or objects are hard to map onto modern processor architectures efficiently. The work in this paper introduces a new abstract […]

Modern processors are evolving into hybrid, heterogeneous processors with both CPU and GPU cores used for general purpose computation. Several languages, such as BrookGPU, CUDA, and more recently OpenCL, have been developed to harness the potential of these processors. These languages typically involve control code running on a host CPU, while performance-critical, massively data-parallel kernel […]

In this work, a novel graphics processing unit (GPU) distributed sharing model for hybrid many-core architectures is introduced and employed in the acceleration of a production-level computational fluid dynamics (CFD) code. The latest generation graphics hardware allows multiple processor cores to simultaneously share a single GPU through concurrent kernel execution. This feature has allowed the […]