A graphical processing unit (GPU) is a hardware device normally used to manipulate computer memory for the display of images. GPU computing, also known as general purpose GPU computing, is the practice of using a GPU device for scienti c or general purpose computations that are not necessarily related to the display of images. The ability of a GPU to render rapidly changing complex images depen...

Principal component analysis (PCA) is a key statistical technique for multivariate data analysis. For large data sets, the common approach to PCA computation is based on the standard NIPALS-PCA algorithm, which unfortunately suffers from loss of orthogonality, and therefore its applicability is usually limited to the estimation of the first few components. Here we present an algorithm based on ...

Discrete element modelling (DEM) is widely used to simulate granular systems, nowadays routinely on graphical processing units. Graphics units (GPUs) are inherently designed for parallel computation, and recent advances in the architecture, compiler design language development allowing general-purpose computation be computed multiple GPUs. Application of DEM bonded particle systems much less co...

We provide an efficient multi-node, multi-GPU implementation of the Block Wiedemann Algorithm (BWA)to find solution a large sparse system linear equations over GF(2). One important applications ofsolving such systems arises in most integer factorization algorithms like Number Field Sieve. In this paper, wedescribe how hybrid parallelization can be adapted to speed up time-consuming sequence gen...

Dominance-based Rough Sets Approach (DRSA) is a useful tool for multi-criteria classification problems solving. Parallel computing is an efficient way to accelerate problems solving. Computation of approximations is a vital step to find the solutions with rough sets methodologies. In this paper, we propose a matrix-based approach for computing approximations in DRSAand design the corresponding ...

Recent advances in GPUs (graphics processing units) lead to massively parallel hardware that is easily programmable and widely applied in areas which require intensive computation besides graphics acceleration. The appearance of GPU clusters gains popularity in the scientific computing community, and the study on GPU clusters becomes an increasingly hot issue. While extending a singleGPU system...

Molecular dynamics simulations allow us to study the behavior of complex biomolecular systems by modeling the pairwise interaction forces between all atoms. Molecular systems are subject to slowly decaying electrostatic potentials, which turn molecular dynamics into an n-body problem. In this paper, we present a parallel and scalable solution to compute long-range molecular forces, based on the...

Computed tomography (CT) has been widely used to acquire volumetric anatomical information in the diagnosis and treatment of illnesses in many clinics. However, the ART algorithm for reconstruction from under-sampled and noisy projection is still time-consuming. It is the goal of our work to improve a block-wise approximate parallel implementation for the ART algorithm on CUDA-enabled GPU to ma...

Financial derivatives are financial instruments whose payoff is linked to some fundamental financial assets or indices. They are essential tools for speculation and risk-management. This paper focuses on the pricing of a common type of derivatives: convertible bonds (CBs), which incorporate the features of both bonds and stocks. Chambers and Lu propose a popular two-factor tree model for CBs pr...

The number of cores on graphical computing units (GPUs) is reaching thousands nowadays, whereas the clock speed processors stagnates. Unfortunately, constraint programming solvers do not take advantage yet GPU parallelism. One reason that were primarily designed within mental frame sequential computation. To solve this issue, we a step back and contribute to simple, intrinsically parallel, lock...