high performance computing on graphics processing units: hgpu.org

In the field of Tracking and Data Fusion most, if not all, computations executed by a computer are carried out serially. The sole part of the process that is not entirely serial is the collection of data from multiple sensors, which can be executed in parallel. However, once the data is to be filtered the […]

Many emerging online data analysis applications require Large-scale streams data processing. GPU cluster is becoming a significantly parallel computing scheme to handling large-scale streams data tasks. However power optimization is a challenging issue. In this paper, we present a novel power consumption control model to shift power budge among nodes in the cluster based on […]

The race to computing power increases every day in the simulation community. A few years ago, scientists have started to harness the computing power of Graphics Processing Units (GPUs) to parallelize their simulations. As with any parallel architecture, not only the simulation model implementation has to be ported to the new parallel platform, but all […]

Software development for cellular computing is growing up yielding new applications. In this paper, we describe a simulator for the class of recognizer P systems with active membranes, which exploits the massively parallel nature of P systems computations by using GPUs (Graphics Processing Units). The newest generation of GPUs provide a massively parallel framework to […]

The proliferation of heterogeneous computing systems has led to increased interest in parallel architectures and their associated programming models. One of the most promising models for heterogeneous computing is the accelerator model, and one of the most cost-effective, high-performance accelerators currently available is the general-purpose, graphics processing unit (GPU). Two similar programming environments have been […]

With the amount of sequence data deluge as a result of next generation sequencing, there comes a need to leverage the large-scale biological sequence data. Therefore, the role of high performance computational methods to mining interesting information solely from these sequence data becomes increasingly important. Almost everything in bioinformatics counts on the inter-relationship between sequences, […]

MOTIVATION: Quantification of the contribution of genetic variation to phenotypic variation for complex traits becomes increasingly computationally demanding with increasing numbers of SNPs and individuals. To meet the challenges in making feasible large scale studies, we present the REACTA software. Adapted from ACTA (and, in turn, GCTA), it is tailored to exploit the parallelism present […]

The interaction of flows and solid objects is a recurring problem in several engineering disciplines. The objective of this work is to present a fully coupled model, based on the fundamental conservation laws of hydrodynamics, namely the continuity and Navier-Stokes equations, and the equation of conservation of momentum of solid bodies. The coupled numerical solution, […]

Tools that aim to automatically map parallel computations to heterogeneous and hierarchical systems try to divide the whole computation in parts with computational loads adjusted to the capabilities of the target devices. Some parts are executed in node cores, while others are executed in accelerator devices. Each part requires one or more data-structure pieces that […]

Computer systems increasingly rely on heterogeneity to achieve greater performance, scalability and energy efficiency. Because heterogeneous systems typically comprise multiple execution contexts with different programming abstractions and runtimes, programming them remains extremely challenging. Dandelion is a system designed to address this programmability challenge for data-parallel applications. Dandelion provides a unified programming model for heterogeneous systems […]

This thesis presents a parallel intermediate representation for embedded languages called PIRE, and its incorporation into the Feldspar language. The original Feldspar backend translates the parallel loops of Feldspar to ordinary for loops, meaning that they are not actually parallel in the generated code. We create an alternate backend for the Feldspar project, where the […]

We present an implementation of Overdetermined Laplacian Partial Differentiation Equations (ODETLAP) that uses CUDA directly. This lossy compression technique approximates a solution to an overdetermined system of equations in order to reconstruct gridded, correlated data. ODETLAP can be used to compress a dataset or to reconstruct missing data. Parallelism in CUDA provides speed performance improvements […]