high performance computing on graphics processing units: hgpu.org

The continuum theory applied to bimolecular electrostatics leads to an implicit-solvent model governed by the Poisson-Boltzmann equation. Solvers relying on a boundary integral representation typically do not consider features like solvent-filled cavities or ion-exclusion (Stern) layers, due to the added difficulty of treating multiple boundary surfaces. This has hindered meaningful comparisons with volume-based methods, and […]

GPU devices offer great performance when dealing with algorithms that require intense computational resources. A developer can configure the L1 cache memory of the latest GPU Kepler architecture with different cache size and cache set associativity, per Streaming Multiprocessors (SM). The performance of the computation intensive algorithms can be affected by these cache parameters. In […]

A recently proposed approach, called seam carving, has been widely used for content-aware resizing of images and videos with little to no perceptible distortion. Unfortunately, for high-resolution videos and large images it is not computationally feasible to do the resizing in real-time using small-scale CPU systems. In this paper, we exploit highly parallel computational capabilities […]

This paper explores the performance and energy efficiency of CUDA-enabled GPUs and multi-core SIMD CPUs using a set of kernels and full applications. Our implementations efficiently exploit both SIMD and thread-level parallelism on multi-core CPUs and the computational capabilities of CUDA-enabled GPUs. We discuss general optimization techniques for our CPU-only and CPU-GPU platforms. To fairly […]

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. […]