high performance computing on graphics processing units: hgpu.org

Verification has grown to dominate the cost of electronic system design, consuming about 60% of design effort. Among several verification techniques, logic simulation remains the major verification technique. Speeding up logic simulation results in great savings and shorter time-to-market. We parallelize logic simulation using Graphics Processing Units (GPUs). In the past, GPUs were special-purpose application […]

This article presents the parallel implementation on a GPU of a real-time dynamic tone-mapping operator. The operator we describe in this article is generic and may be used by any application. However, the goal of our work is to integrate this operator into the graphic rendering process of a car driving simulator; thus, we studied […]

A collaborative filtering predicts customers’ unknown preferences from known preferences. In a computation of the collaborative filtering, a singular value decomposition (SVD) is needed to reduce the size of a large scale matrix so that the burden for the next phase computation will be decreased. In this application, SVD means a roughly approximated factorization of […]

The International Workshop on OpenMP (IWOMP) is an annual workshop dedicated to the promotion and advancement of all aspects of parallel programming with OpenMP. It is the premier forum to present and discuss issues, trends, recent research ideas and results related to parallel programming with OpenMP. The international workshop affords an opportunity for OpenMP users […]

Fluorescence microscopy provides a powerful method for localization of structures in biological specimens. However, aspects of the image formation process such as noise and blur from the microscope’s point-spread function combine to produce an unintuitive image transformation on the true structure of the fluorescing molecules in the specimen, hindering qualitative and quantitative analysis of even […]

A major cryptanalytic computation is currently underway on multiple platforms, including standard CPUs, FPGAs, PlayStations and Graphics Processing Units (GPUs), to break the Certicom ECC2K-130 challenge. This challenge is to compute an elliptic-curve discrete logarithm on a Koblitz curve over F2131. Optimizations have reduced the cost of the computation to approximately 2^77 bit operations in […]

In this paper, we explore the implementation of Monte Carlo based statistical static timing analysis (SSTA) on a graphics processing unit (GPU). SSTA via Monte Carlo simulations is a computationally expensive, but important step required to achieve design timing closure. It provides an accurate estimate of delay variations and their impact on design yield. The […]

In this chapter, we discuss multiple strategies to perform general computations on unstructured grids, with specific application to the assembly of finite element methods (FEMs). We review and apply two methods, discussed in depth in [1], for assembly of FEMs to produce and accelerate a FEM model for a nonlinear hyperelastic solid where the assembly, […]

In a previous publication, we have examined the fundamental difference between computational precision and result accuracy in the context of the iterative solution of linear systems as they typically arise in the Finite Element discretization of Partial Differential Equations (PDEs) [1]. In particular, we evaluated mixed- and emulatedprecision schemes on commodity graphics processors (GPUs), which […]

In visualization and computer graphics it has been shown that the numerical solution of PDE problems can be obtained much faster on graphics processors (GPUs) than on CPUs. However, GPUs are restricted to single precision floating point arithmetics which is insufficient for most technical scientific computations. Since we do not expect double precision support natively […]

We report on our experiences with integrating GPUs as fast, parallel floating-point coprocessors into the parallel FE package FEAST. Since a full re-implementation of such a package is not feasible, we identify the smoothing of an outer domain-decomposition multigrid solver as a natural entry-point for a minimally invasive integration of GPUs. We address the issue […]

In this chapter, we present efficient fine-grained parallelization techniques for robust multigrid solvers, in particular for numerically strong, inherently sequential smoothing operators. We apply them to sparse ill-conditioned linear systems of equations that arise from grid-based discretization techniques like finite differences, volumes and elements. Our exemplary results demonstrate both the numerical and runtime performance of […]