high performance computing on graphics processing units: hgpu.org

Developing image reconstruction algorithms for diagnostic medical devices requires physically accurate and effective simulation tools. In this paper we present a hybrid Monte Carlo (MC) particle simulation method for Computed Tomography (CT) scanners. To meet the performance requirements, we combine several variance reduction techniques and tailor the algorithms for effective GPU execution. Variance reduction methods […]

In this paper, we propose fast string matching system using GPU for large scale string matching. The key of our proposed system is the use of bit-parallel pattern matching approach for compact NFA representation and fast simulation of NFA transition on GPU. In the experiments, we show the usefulness of our proposed pattern matching system.

In this work a unified treatment of solid and fluid vibration problems is developed by means of the Finite-Difference Time-Domain (FDTD). The scheme here proposed introduces a scaling factor in the velocity fields that improves the performance of the method and the vibration analysis in heterogenous media. In order to accurately reproduce the interaction of […]

The performance of General-Purpose computation on Graphics Processing Units (GPGPU) is heavily dependent on the memory access behavior. This sensitivity is due to a combination of the underlying Massively Parallel Processing (MPP) execution model present on GPUs and the lack of architectural support to handle irregular memory access patterns. Application performance can be significantly improved […]

In this dissertation we had the aim of utilizing GPU technology in order to optimize and improve on the problem of EST clustering. Extensive research on this cross-disciplinary approach was required before even considering such an approach. It was found that though this line of research has not received significant attention, there are significant gains […]

This work describes the challenges presented by porting code to the Intel Xeon Phi coprocessor, as well as opportunities for optimization and tuning. We use micro-benchmarks, code segments, assembly listings and application level results to illustrate the key issues in porting to the Xeon Phi coprocessor, always keeping in mind both portability and performance. While […]

This paper deals with parallelization of the Quine-McCluskey algorithm. This boolean function minimization algorithm has a limitation when dealing with more than four variables. The problem computed by this algorithm is NP-hard and runtime of the algorithm grows exponentially with the number of variables. The goal is to show that parallel implementation of the Quine-McCluskey […]

Computational Neuroscience is an emerging field that provides unique opportunities to study complex brain structures through realistic neural simulations. However, as biological details are added to models, the execution time for the simulation becomes longer. Graphics Processing Units (GPUs) are now being utilized to accelerate simulations due to their ability to perform computations in parallel. […]

We have developed a GPU-based parallel linear solver package. When solving matrices from reservoir simulation, the parallel solvers are much more efficient than CPU-based linear solvers. However, efforts should be made to improve the setup phase of domain decomposition, the factorization of ILUT and parallelism of block ILUT preconditioner.

Affine invariance is one of the main performances of a good feature extraction algorithm. SIFT is a kind of scale-invariant feature extraction algorithm, but it is not affine invariant. In order to improve SIFT algorithm’s affine invariance. Affine and Scale Invariant Feature Transform (ASIFT) algorithm takes affine Model into SIFT. However, serial ASIFT algorithm’s computing […]

Solving linear systems remains a key activity in of economics modelling, therefore making fast and accurate methods for computing solutions highly desirable. In this paper, a proof of concept C++ AMP implementation of an iterative method for solving linear systems, BiConjugate Gradient Stabilized (henceforth BiCGSTAB), is presented. The method relies on matrix and vector operations, […]

Graphics Processing Unit (GPU) has obtained great success in scientific computations for its tremendous computational horsepower and very high memory bandwidth. This paper discusses the efficient way to implement polynomial preconditioned conjugate gradient solver for the finite element computation of elasticity on NVIDIA GPUs using Compute Unified Device Architecture (CUDA). Sliced Block ELLPACK (SBELL) format […]