Improving terrain tile data selection efficiency, real-time loading of visible tile data and building GPU-based continuous Level of Details (LOD) are the key technologies for large scale terrain rendering on GPU. In this article, in order to reduce terrain tile data selection time, we build double layers tile quad tree for massive terrain data and organize tile data by designing Z-order space f...

GPUs in High-Performance Computing systems remain under-utilised due to the unavailability of schedulers that can safely schedule multiple applications to share the same GPU. The research reported in this paper is motivated to improve the utilisation of GPUs by proposing a framework, we refer to as schedGPU, to facilitate intra-node GPU co-scheduling such that a GPU can be safely shared among m...

The subset-sum problem is a well-known non-deterministic polynomial-time complete (NP-complete) decision problem. This paper proposes a novel and efficient implementation of a parallel two-list algorithm for solving the problem on a graphics processing unit (GPU) using Compute Unified Device Architecture (CUDA). The algorithm is composed of a generation stage, a pruning stage, and a search stag...

Fireworks Algorithm (FWA) is a recently developed Swarm Intelligence Algorithm (SIA), which has been successfully used in diverse domains. When applied to complicated problems, many function evaluations are needed to obtain an acceptable solution. To address this critical issue, a GPU-based variant (GPU-FWA) was proposed to greatly accelerate the optimization procedure of FWA. Thanks to the act...

Heterogeneous multiand many-core systems are increasingly prevalent in the desktop and mobile domains. On these systems it is common for programs to compete with co-running programs for resources. While multi-task scheduling for CPUs is a well-studied area, how to partitioning and map computing tasks onto the hetergeneous system in the presence of GPU contention (i.e. multiple programs compete ...

Even given today’s rich hardware platforms, computation-intensive algorithms and applications, such as large-scale simulations, are still challenging to run with acceptable response times. One way to increase the performance of these algorithms and applications is by using the computing power of Graphics Processing Units (GPU). However, effectively mapping distributed software models to GPU is ...

In this paper, we introduce GPU VSIPL1, an implementation of the Vector Signal Image Processing Library (VSIPL) Core Lite profile developed for the graphics processing unit (GPU). This implementation achieves a speedup of one to two orders of magnitude over the reference implementation. Because VSIPL is an open standard for high-performance platform-independent applications, GPU VSIPL provides ...

We present a modified GPU (graphics processing unit) version of MrBayes, called ta(MC)(3) (GPU MrBayes V3.1), for Bayesian phylogenetic inference on protein data sets. Our main contributions are 1) utilizing 64-bit variables, thereby enabling ta(MC)(3) to process larger data sets than MrBayes; and 2) to use Kahan summation to improve accuracy, convergence rates, and consequently runtime. Versus...

This talk presents work on transforming SQL-IMDB, a commercial available inmemory database system, into a massive parallel, array structured data processor extending the “classic” query engine architecture with GPU based co-processing facilities. The chosen approach is not just a simple re-implementation of common database functionality like sorting, stream processing and joins on GPUs, instead...