high performance computing on graphics processing units: hgpu.org

Continuum robots, with the capability of bending and extending at any point along their length mimic the abilities of an octopus arm or an elephant trunk. These manipulators present a number of exciting possibilities. While calculating a static solution for the system has been proven with certain models to produce satisfactory results [1], this approach […]

Nearest neighbor search is a core computational task in database systems and throughout data analysis. It is also a major computational bottleneck, and hence an enormous body of research has been devoted to data structures and algorithms for accelerating the task. Recent advances in graphics hardware provide tantalizing speedups on a variety of tasks and […]

Metagenomic sequence classification is a procedure to assign sequences to their source genomes. It is one of the important steps for metagenomic sequence data analysis. Although many methods exist, classification of high-throughput metagenomic sequence data in a limited time is still a challenge. We present here an ultra-fast metagenomic sequence classification system (MetaBinG) using graphic […]

We present a computational framework for multi-scale simulations of real-life biofluidic problems. The framework allows to simulate suspensions composed by hundreds of millions of bodies interacting with each other and with a surrounding fluid in complex geometries. We apply the methodology to the simulation of blood flow through the human coronary arteries with a spatial […]

Many-cores, processors with 100s of cores, are becoming increasingly popular in general-purpose computing, yet power is a limiting factor in their performance. In this paper, we compare the power and performance of two design points in the many-core processor domain. The XMT general-purpose processor provides significant runtime advantage on irregular parallel programs (e.g., graph algorithms). […]

In this contribution we describe a specialised data processing system for Spectral Optical Coherence Tomography (SOCT) biomedical imaging which utilises massively parallel data processing on a low-cost, Graphics Processing Unit (GPU). One of the most significant limitations of SOCT is the data processing time on the main processor of the computer (CPU), which is generally […]

We have improved our prior implementation of Strassens algorithm for high performance multiplication of very large integers on a general purpose graphics processor (GPU). A combination of algorithmic and implementation optimizations result in a factor of up to 13.9 speed improvement over our previous work, running on an NVIDIA 295. We have also reoptimized the […]

This paper presents a novel vision system for people detection using an omnidirectional camera mounted on a mobile robot. In order to determine regions of interest (ROI), we compute a dense optical flow map using graphics processing units, which enable us to examine compliance with the ego-motion of the robot in a dynamic environment. Shape-based […]

Stream parallelism is often cited as a powerful programming model for expressing parallel computation for multi-core and heterogeneous computers. It allows programmers to concisely describe the concurrency and communication requirements found in a program and it allows compilers and runtime systems to easily generate efficient code targeting parallel hardware. This type of stream parallelism is […]

Recently, many numerical methods that are developed for the solution of electromagnetic problems have greatly benefited from the hardware accelerated scientific computing capability provided by graphics processing units (GPUs) and orders of magnitude speed-up factors have been reported. Among these methods, the finite-difference frequency-domain (FDFD) method as well can be accelerated substantially by utilizing an […]

In this paper, we present a hybrid circular queue method that can significantly boost the performance of stencil computations on GPU by carefully balancing usage of registers and shared-memory. Unlike earlier methods that rely on circular queues predominantly implemented using indirectly addressable shared memory, our hybrid method exploits a new reuse pattern spanning across the […]

The Kernel Polynomial Method (KPM) is one of the fast diagonalization methods used for simulations of quantum systems in research fields of condensed matter physics and chemistry. The algorithm has a difficulty to be parallelized on a cluster computer or a supercomputer due to the fine-grain recursive calculations. This paper proposes an implementation of the […]