high performance computing on graphics processing units: hgpu.org

Given a multivariate data set, sparse principal component analysis (SPCA) aims to extract several linear combinations of the variables that together explain the variance in the data as much as possible, while controlling the number of nonzero loadings in these combinations. In this paper we consider 8 different optimization formulations for computing a single sparse […]

In this paper we present clustering method is very sensitive to the initial center values, requirements on the data set too high, and cannot handle noisy data the proposal method is using information entropy to initialize the cluster centers and introduce weighting parameters to adjust the location of cluster centers and noise problems. The navigation […]

In this paper, a novel implementation of the distributed 3D Fast Fourier Transform (FFT) on a multi-GPU platform using CUDA is presented. The 3D FFT is the core of many simulation methods, thus its fast calculation is critical. The main bottleneck of the distributed 3D FFT is the global data exchange which must be performed. […]

The Finite-Difference Time-Domain method (FDTD) is a modelling technique for electromagnetic waves propagation. There is a great range of domains of application, for example geophysics, defence, microwaves like radar, or biomedicine. However, FDTD is a computationally intensive method, but has potential for parallelisation. The use of General-Purpose computing on Graphics Processing Units (GPGPU) is examined […]

GPUs have been gaining popularity as general purpose parallel processors that deliver a performance to cost ratio superior to that of CPUs. However, programming on GPUs has remained a specialised area, as it often requires significant knowledge about the GPU architecture and platform-specific parallelisation of the algorithms that are implemented. Furthermore, the dominant programming models […]

Structured Adaptive Mesh Refinement (SAMR) is a popular numerical technique to study processes with high spatial and temporal dynamic range. It reduces computational requirements by adapting the lattice on which the underlying differential equations are solved to most efficiently represent the solution. Particularly in astrophysics and cosmology such simulations now can capture spatial scales ten […]

The performance of database operations has always been an important factor in database research being done. This has never been more important, as the quantity of data is growing at an alarming rate. This coupled with the recent growth of using graphics processors as general compute processors has led to many advancements in the field […]

A feasibility study into the acceleration of multivariate analysis techniques using Graphics Processing Units (GPUs) will be presented. The MLP-based Artificial Neural Network method contained in the TMVA framework has been chosen as a focus for investigation. It was found that the network training time on a GPU was lower than for CPU execution as […]

This letter presents strategies on how to optimize graphics processing unit (GPU)-based finite-element matrix-generation that occurs in the finite element method (FEM) using higher-order curvilinear elements. The goal of the optimization is to increase the speed of evaluation and assembly of large finite-element matrices on a single GPU while maintaining the accuracy of numerical integration […]

As the complexity of machines and architectures has increased, performance tuning has become more challenging, leading to the failure of general compilers to generate the best possible optimized code. Expert performance programmers can often hand-write code that outperforms compiler-optimized low-level code by an order of magnitude. At the same time, the complexity of programs has […]

Multigrid methods are widely used to accelerate the convergence of iterative solvers for linear systems in a number of different application areas. In this report, we explore communication-avoiding implementations of Geometric Multigrid on Nvidia GPUs. We achieved an overall gain of 1.2x for the whole multigrid algorithm over baseline implementation. We also provide an insight […]

In this paper, the details of the circular hough transform are explained and the performances of three different implementations(CPU, OpenCL and CUDA) are also shown. The goal of this project is to contribute to the computer vision literature by porting the circular hough transform written in CUDA to OpenCL.