high performance computing on graphics processing units: hgpu.org

Through computational methods, biologists are able learn more about molecular biology by building accurate models. These models represent and predict the reactions among species populations within a system. One popular method to develop predictive models is to use a stochastic, Monte Carlo method developed by Gillespie called the stochastic simulation algorithm (SSA). Since this algorithm […]

We present a multi-core, GPU-based framework for simulation and visualization of two-dimensional floods, based on the full implementation of Saint Venant equations. A validated CPU-based flood model was converted to NVIDIA’s CUDA architecture. The model was run on two different NVIDIA graphics cards, a GeForce 8400 GS and a Tesla T10. The model was tested […]

The performance of data-parallel processing can be highly sensitive to any contention in memory. In contrast to multi-core CPUs which employ a number of memory latency minimization techniques such as multi-level caching and prefetching, Graphics Processing Units (GPUs) require that the data-parallel computations reference memory in a deterministic pattern in order to reap the benefits […]

This paper describes the use of graphics processors to accelerate the backpropagation method of forming images in Synthetic Aperture Sonar (SAS) systems. SAS systems coherently process multiple pulses to provide a higher level of detail in the resolved image than is otherwise possible with a single pulse. Several models are available to resolve an image […]

Accelerator processors allow energy-efficient computation at high performance, especially for computationintensive applications. There exists a plethora of different accelerator architectures, such as GPUs and the Cell Broadband Engine. Each accelerator has its own programming language, but the recently introduced OpenCL language unifies accelerator programming languages. Hereby, OpenCL achieves functional protability, allowing to reduce the development […]

Today’s graphics processing units (GPU) have tremendous resources when it comes to raw computing power. The simulation of large groups of agents in transport simulation has a huge demand of computation time. Therefore it seems reasonable to try to harvest this computing power for traffic simulation. Unfortunately simulating a network of traffic is inherently connected […]

We present MuMax, a general-purpose micromagnetic simulation tool running on Graphical Processing Units (GPUs). MuMax is designed for high performance computations and specifically targets large simulations. In that case speedups of over a factor 100x can easily be obtained compared to the CPU-based OOMMF program developed at NIST. MuMax aims to be general and broadly […]

We have previously suggested mixed precision iterative solvers specifically tailored to the iterative solution of sparse linear equation systems as they typically arise in the finite element discretization of partial differential equations. These schemes have been evaluated for a number of hardware platforms, in particular, single-precision GPUs as accelerators to the general purpose CPU. This […]

In this paper we describe a successful effort to accelerate the two-point angular correlation function—a basic statistics tool used in the field of cosmology to characterize the distribution of the matter and energy in the Universe—by using an NVIDIA GPU-based system. We demonstrate the use of GPUs to accelerate the calculation of histograms of angular […]

We present an implementation of one of the direct self-consistent-field (DSCF) calculation techniques, the restricted Hartree-Fock method, on a high-performance computing cluster outfitted with graphics processing units (GPUs) and demonstrate its effectiveness and scalability up to 128 cluster nodes on molecules of as many as 1,732 atoms. We discuss the overall parallel application architecture that […]

Evaluation of electron repulsion integrals (ERIs) takes considerable time in modern quantum chemistry applications and also presents a certain difficulty to be efficiently computed on GPUs. Here, we describe a novel methodology for generating high-arithmetic-density kernels for ERI evaluation of d and higher angular momentum functions, as well as highlight challenges associated with the efficient […]

We present a CUDA C implementation of the Conjugate Gradient (CG) and multi-mass CG solver from the MILC quantum chromodynamics package to speedup improved staggered quarks computations on NVIDIA GPUs. The implementation is built on the QUDA package from Boston University.