high performance computing on graphics processing units: hgpu.org

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 […]

Data are traditionally represented using native format such as integer or floating-point numbers in various flavor. However, some applications rely on more complex representation format. This is the case when uncertainty needs to be apprehended. Fuzzy arithmetic is one of the major tools to address this problem, but the execution time of basic operations such […]

The number of active threads required to achieve peak application throughput on graphics processing units (GPUs) depends largely on the ratio of time spent on computation to the time spent accessing data from memory. While compute-intensive applications can achieve peak throughput with a low number of threads, memory-intensive applications might not achieve good throughput even […]

Halo exchange patterns are very common in scientific computing, since the solution of PDEs often requires communication between neighbor points. Although this is a common pattern, implementations are often made by programmers from scratch, with an accompanying feeling of "reinventing the wheel". In this paper we describe GCL, a C++ generic library that implements a […]