Due to the rapid advance of general-purpose graphics processing unit (GPU), it is an active research topic to study performance improvement of non-linear optimization with parallel implementation on GPU, as attested by the much research on parallel implementation of relatively simple optimization methods, such as the conjugate gradient method. We study in this context the L-BFGS-B method, or th...

A computation of a 3D fluid flow simulation for virtual environment with user interaction can be a non-trivial issue. This is especially how to reach good performances and balancing between visualization, user feedback interaction, and computations. In this paper, we describe our approach of computation methods based on parallel programming on a GPU. The 3D fluid flow solvers have been develope...

We present two efficient histogram algorithms designed for NVIDIA’s compute unified device architecture (CUDA) compatible graphics processor units (GPUs). Our algorithm can be used for parallel computation of histograms on large data-sets and for thousands of bins. Traditionally histogram computation has been difficult and inefficient on the GPU. This often means that GPU-based implementation o...

Due to its high performance/cost ratio, a GPU cluster is an attractive platform for large scale general-purpose computation and visualization applications. However, the programming model for high performance generalpurpose computation on GPU clusters remains a complex problem. In this paper, we introduce the Zippy framework, a general and scalable solution to this problem. It abstracts the GPU ...

High Performance Computing (HPC) has recently been considerably improved, for instance General Purpose computation on Graphics Processing Units (GPGPU) has been developed to accelerate parallel computing by using hundreds of cores simultaneously. GPU computing with Compute Unified Device Architecture (CUDA) is a new approach to solve complex problems and transform the GPU into a massively paral...

This paper deals with the numerical solution of financial applications, more specifically the computation of American option derivatives modelled by nonlinear boundary values problems. In such applications we have to solve largescale algebraic systems. We concentrate on synchronous and asynchronous parallel iterative algorithms carried out on CPU and GPU networks. The properties of the operator...

In 2006 NVIDIA introduced a new unified GPU architecture facilitating generalpurpose computation on the GPU. The following year NVIDIA introduced CUDA, a parallel programming architecture for developing general purpose applications for direct execution on the new unified GPU. CUDA exposes the GPU’s massively parallel architecture of the GPU so that parallel code can be written to execute much f...

We present a novel high-level parallel programming model aimed at graphics processing units (GPUs). We embed GPU kernels as data-parallel array computations in the purely functional language Haskell. GPU and CPU computations can be freely interleaved with the type system tracking the two different modes of computation. The embedded language of array computations is sufficiently limited that our...

Parallel Computing can be made possible using the multiple cores of the Graphics Processing Unit (GPU) thanks to the modern programmable GPU models. This allows the use of parallel computing techniques to improve upon the computation time of large scale traffic simulations. This paper proposes the use of a multi-processor algorithm for creating efficient traffic simulation software. The method ...

WPSD (Weighted Pose Space Deformation) is an example based skinning method for articulated body animation. The per-vertex computation required in WPSD can be parallelized in a SIMD (Single Instruction Multiple Data) manner and implemented on a GPU. While such vertex-parallel computation is often done on the GPU vertex processors, further parallelism can potentially be obtained by using the frag...