Sep, 28

A Study of the Potential of Locality-Aware Thread Scheduling for GPUs

Programming models such as CUDA and OpenCL allow the programmer to specify the independence of threads, effectively removing ordering constraints. Still, parallel architectures such as the graphics processing unit (GPU) do not exploit the potential of data-locality enabled by this independence. Therefore, programmers are required to manually perform data-locality optimisations such as memory coalescing or […]
Sep, 28

High-performance Implementations and Large-scale Validation of the Link-wise Artificial Compressibility Method

The link-wise artificial compressibility method (LW-ACM) is a recent formulation of the artificial compressibility method for solving the incompressible Navier-Stokes equations. Two implementations of the LW-ACM in three dimensions on CUDA enabled GPUs are described. The first one is a modified version of a state-of-the-art CUDA implementation of the lattice Boltzmann method (LBM), showing that […]
Sep, 28

NAS Parallel Benchmarks for GPGPUs using a Directive-based Programming Model

The broad adoption of accelerators boosts the interest in accelerator programming. Accelerators such as GPGPUs are optimized for throughput and offer high GFLOPS and memory bandwidth. CUDA has been adopted quite rapidly but it is proprietary and only applicable to GPUs, and the difficulty in writing efficient CUDA code has kindled the necessity to create […]
Sep, 28

Accelerating Phylogenetic Inference on GPUs: an OpenACC and CUDA comparison

Phylogenetic inference is used to derive a "tree of life" for a collection of species whose DNA sequences are known. While several software packages have already been developed to take advantage of GPUs to accelerate phylogenetic inference, they typically require significant changes to the original code, constraining code maintenance. Recently, the OpenACC API was proposed […]
Sep, 25

An open source finite-difference time-domain solver for room acoustics using graphics processing units

Wave based simulation methods have been utilized to numerically estimate wave propagation in domains where low-frequency wave effects dominate the response. Finite-difference time-domain (FDTD) methods are increasingly useful for such problems, but they require massive spatial oversampling to increase the bandwidth of the simulation, which leads to significant computational expense. The advantage of explicit time-stepping […]
Sep, 25

Study on semi-global matching algorithm extended for multi baseline matching and parallel processing method based on GPU

This paper extended semi-global matching algorithm into multi baseline matching to improve matching reliability, especially studies kernel function optimization strategies and GPU threads’ executing scheme of matching cost cube computing and aggregating, and realized its fine granularity parallel processing based on GPU. The experiment results using three UCD aerial images based on Tesla C2050 GPU […]
Sep, 25

Performance Evaluation of Edge Detection Techniques on GPU Using OpenCL

GPU (Graphic processing system) enhance the performance of the performance of the computing field due to its hundreds of cores in parallel. CUDA (Compute Unified Device Architecture) and OpenCL (Open Computing Language) programming models are included in GPU. The advantage of these two programming models in GPU is that developers don’t have to understand any […]
Sep, 25

MASCOT: Fast and Highly Scalable SVM Cross-validation using GPUs and SSDs

Cross-validation is a commonly used method for evaluating the effectiveness of Support Vector Machines (SVMs). However, existing SVM cross-validation algorithms are not scalable to large datasets because they have to (i) hold the whole dataset in memory and/or (ii) perform a very large number of kernel value computation. In this paper, we propose a scheme […]
Sep, 25

Scalability Analysis of Parallel Algorithms on GPU Clusters

Scalability is an important concept in the domain of parallel computing. Since Graphics Processing Unit (GPU) clusters are and will be widely utilized in high performance computing platforms, we investigate the factors influencing the scalability for combinations of parallel algorithms (PA) and GPU clusters (GC).We present a scalability model for combination PA-GC and then validate […]
Sep, 24

Calculation of Force Field Grids for Molecular Docking Using Graphics Processing Unit

The vast majority of problems faced by bioinformatics are very complex and time consuming. They require the use of modern high-performance computational systems and the development of algorithms for such system. Heterogeneous computing systems which include graphics processing unit (GPU) occupy a separate niche. Such systems allow to accelerate solving of some task significantly. The […]
Sep, 23

Advanced Optimizations of An Implicit Navier-Stokes Solver on GPGPU

General-purpose computing on graphics processing units (GPGPU) is a massive fine-grain parallel computation platform, which is is particularly attractive for CFD tasks due to its potential of one or two magnitudes of performance improvement with relatively low capital investment. Many successful attempts have been reported in recent years (see, for example [1, 2, 3, 4, […]
Sep, 23

Explicit Integration with GPU Acceleration for Large Kinetic Networks

We demonstrate the first implementation of recently-developed fast explicit kinetic integration algorithms on modern graphics processing unit (GPU) accelerators. Taking as a generic test case a Type Ia supernova explosion with an extremely stiff thermonuclear network having 150 isotopic species and 1604 reactions coupled to hydrodynamics using operator splitting, we demonstrate the capability to solve […]
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Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: AMD APP SDK 2.9
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.2
  • SDK: nVidia CUDA Toolkit 6.0.1, AMD APP SDK 2.9

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