high performance computing on graphics processing units: hgpu.org

The Multicore+GPU architecture has been adopted in some of the fastest supercomputers listed on the TOP500. The MAGMA project aims to develop a dense linear algebra library similar to LAPACK but for heterogeneous/hybrid architectures processors like Multicore+GPU. However, to provide portable performance, manual parameter tuning is required. This paper presents automatically tuned LU factorization. The […]

In recent years, Graphics Processing Units (GPUs) have emerged as a powerful accelerator for general-purpose computations. Current approaches to program GPUs are still relatively low-level programming models such as Compute Unified Device Architecture (CUDA), a programming model from NVIDIA, and Open Compute Language (OpenCL), created by Apple in cooperation with others. These two programming models […]

GPUs constitute nowadays a solid alternative for high performance computing, and the advent of CUDA/OpenCL allow programmers a friendly model to accelerate a broad range of applications. The way GPUs exploit parallelism differ from multi-core CPUs, which raises new challenges to take advantage of its tremendous computing power. In this respect, P systems or Membrane […]

In order for scientists to learn more about molecular biology, it is imperative that they have the ability to construct and evaluate models. Model statistics consistent with the chemical master equation can be obtained using Gillespie’s stochastic simulation algorithm (SSA). Due to the stochastic nature of the Monte Carlo simulations, large numbers of simulations must […]

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 […]