We present a method to accelerate line-ofsight computation for computer generated forces (CGF) using graphics processing units (GPUs). GPUs have become commodity processors and they are part of every game console or PC system. Moreover, their performance has been increasing at a rate faster than CPUs and the trend is expected to continue in the foreseeable future. We present a hybrid algorithm ...

Several papers show that symbolic regression is suitable for data analysis and prediction in financial markets. Grammatical Evolution (GE), a grammar-based form of Genetic Programming (GP), has been successfully applied in solving various tasks including symbolic regression. However, often the computational effort to calculate the fitness of a solution in GP can limit the area of possible appli...

The Gillespie t-Leaping Method is an approximate algorithm that is faster than the exact Direct Method (DM) due to the progression of the simulation with larger time steps. However, the procedure to compute the time leap t is quite expensive. In this paper, we explore the acceleration of the t-Leaping Method using Graphics Processing Unit (GPUs) for ultra-large networks (w0:5e reaction channels...

SAR simulators usually apply the ray-tracing approach. Ray-tracing, which is also used for virtual image generation, is based on accurate physical models, but is unfortunately rather computational time intensive. Because of this, real-time applications, like interactive visualisation, in general use the rasterization method. Rasterization is less complex to calculate and is therefore faster. Th...

We report on early results of a numerical and statistical study of binary black hole inspirals. The two black holes are evolved using post-Newtonian approximations starting with initially randomly distributed spin vectors. We characterize certain aspects of the distribution shortly before merger. In particular we note the uniform distribution of black hole spin vector dot products shortly befor...

In this paper, we present a new MapReduce framework, called Grex, designed to leverage general purpose graphics processing units (GPUs) for parallel data processing. Grex provides several new features. First, it supports a parallel split method to tokenize input data of variable sizes, such as words in e-books or URLs in web documents, in parallel using GPU threads. Second, Grex evenly distribu...

This paper presents a comprehensive survey on current status of integral equation solvers implemented on parallel computing systems accelerated by graphics processing units (GPUs) and proposes several key points for efficiently utilizing this type of fundamentally different processors to accelerate several categories of algorithms used by today’s integral equation solvers. Three spatial interpo...

The recent emergence of Graphics Processing Units (GPUs) as general-purpose parallel computing devices provides us with new opportunities to develop scalable learning methods for massive data. In this work, we consider the problem of parallelizing two inference methods on GPUs for latent Dirichlet Allocation (LDA) models, collapsed Gibbs sampling (CGS) and collapsed variational Bayesian (CVB). ...

The paper describes how one can use commodity graphics cards (GPUs) as a high-performance parallel computer to simulate the dynamics of ideal gases in two and three spatial dimensions. The dynamics is described by the Euler equations, and numerical approximations are computed using state-of-the-art high-resolution finite-volume schemes. These schemes are based upon an explicit time discretisati...

The Graphics Processing Unit (GPU) is a highly parallel, many-core streaming architecture that can execute hundreds of threads concurrently. The data parallel architecture of the GPU is suitable to perform computation intensive applications. In recent years, the use of GPUs for general purpose computation has increased and a large set of problems can be tackled by mapping onto GPUs. The program...