## Posts

Nov, 8

### On the utility of graphics cards to perform massively parallel simulation of advanced Monte Carlo methods

We present a case-study on the utility of graphics cards to perform massively parallel simulation of advanced Monte Carlo methods. Graphics cards, containing multiple Graphics Processing Units (GPUs), are self-contained parallel computational devices that can be housed in conventional desktop and laptop computers. For certain classes of Monte Carlo algorithms they offer massively parallel simulation, […]

Nov, 8

### N-Body Simulations on GPUs

Commercial graphics processors (GPUs) have high compute capacity at very low cost, which makes them attractive for general purpose scientific computing. In this paper we show how graphics processors can be used for N-body simulations to obtain improvements in performance over current generation CPUs. We have developed a highly optimized algorithm for performing the O(N^2) […]

Nov, 8

### Integrating Post-Newtonian Equations on Graphics Processing Units

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 […]

Nov, 8

### Graphic-Card Cluster for Astrophysics (GraCCA) – Performance Tests

In this paper, we describe the architecture and performance of the GraCCA system, a Graphic-Card Cluster for Astrophysics simulations. It consists of 16 nodes, with each node equipped with 2 modern graphic cards, the NVIDIA GeForce 8800 GTX. This computing cluster provides a theoretical performance of 16.2 TFLOPS. To demonstrate its performance in astrophysics computation, […]

Nov, 8

### Quantile Mechanics II: Changes of Variables in Monte Carlo methods and a GPU-Optimized Normal Quantile

This article presents differential equations and solution methods for the functions of the form $A(z) = F^-1(G(z))$, where $F$ and $G$ are cumulative distribution functions. Such functions allow the direct recycling of Monte Carlo samples from one distribution into samples from another. The method may be developed analytically for certain special cases, and illuminate the […]

Nov, 8

### Calculation of HELAS amplitudes for QCD processes using graphics processing unit (GPU)

We use a graphics processing unit (GPU) for fast calculations of helicity amplitudes of quark and gluon scattering processes in massless QCD. New HEGET ( HELAS Evaluation with GPU Enhanced Technology) codes for gluon self-interactions are introduced, and a C++ program to convert the MadGraph generated FORTRAN codes into HEGET codes in CUDA (a C-platform […]

Nov, 8

### GPUs for data processing in the MWA

The MWA is a next-generation radio interferometer under construction in remote Western Australia. The data rate from the correlator makes storing the raw data infeasible, so the data must be processed in real-time. The processing task is of order ~10 TFLOPS. The remote location of the MWA limits the power that can be allocated to […]

Nov, 8

### Caracteristiques arithmetiques des processeurs graphiques

Les unites graphiques (Graphic Processing Units-GPU) sont desormais des processeurs puissants et flexibles. Les dernieres generations de GPU contiennent des unites programmables de traitement des sommets (vertex shader) et des pixels (pixel shader) supportant des operations en virgule flottante sur 8, 16 ou 32 bits. La representation flottante sur 32 bits correspond a la simple […]

Nov, 8

### A framework for exploring numerical solutions of advection-reaction-diffusion equations using a GPU-based approach

In this paper we describe a general purpose, graphics processing unit (GP-GPU)-based approach for solving partial differential equations (PDEs) within advection-reaction-diffusion models. The GP-GPU-based approach provides a platform for solving PDEs in parallel and can thus significantly reduce solution times over traditional CPU implementations. This allows for a more efficient exploration of various advection-reaction-diffusion models, […]

Nov, 8

### GPU accelerated Monte Carlo simulation of the 2D and 3D Ising model

The compute unified device architecture (CUDA) is a programming approach for performing scientific calculations on a graphics processing unit (GPU) as a data-parallel computing device. The programming interface allows to implement algorithms using extensions to standard C language. With continuously increased number of cores in combination with a high memory bandwidth, a recent GPU offers […]

Nov, 8

### Particle-Based Fluid Simulation on the GPU

Large scale particle-based fluid simulation is important to both the scientific and computer graphics communities. In this paper, we explore the effectiveness of implementing smoothed particle hydrodynamics on the streaming architecture of a GPU. A dynamic quadtree structure is proposed to accelerate the computation of inter-particle forces. Our method readily extends to higher dimensions without […]

Nov, 8

### Multifold Acceleration of Neural Network Computations Using GPU

With emergence of graphics processing units (GPU) of the latest generation, it became possible to undertake neural network based computations using GPU on serially produced video display adapters. In this study, NVIDIA CUDA technology has been used to implement standard back-propagation algorithm for training multiple perceptrons simultaneously on GPU. For the problem considered, GPU-based implementation […]