3475

Posts

Mar, 28

GPU-based efficient realistic techniques for bleeding and smoke generation in surgical simulators

BACKGROUND: In actual surgery, smoke and bleeding due to cauterization processes provide important visual cues to the surgeon, which have been proposed as factors in surgical skill assessment. While several virtual reality (VR)-based surgical simulators have incorporated the effects of bleeding and smoke generation, they are not realistic due to the requirement of real-time performance. […]
Mar, 28

Data Assimilation using a GPU Accelerated Path Integral Monte Carlo Approach

The answers to data assimilation questions can be expressed as path integrals over all possible state and parameter histories. We show how these path integrals can be evaluated numerically using a Markov Chain Monte Carlo method designed to run in parallel on a Graphics Processing Unit (GPU). We demonstrate the application of the method to […]
Mar, 28

High Performance Computing Using MPI and OpenMP on Multi-core Parallel Systems

The rapidly increasing number of cores in modern microprocessors is pushing the current high performance computing (HPC) systems into the petascale and exascale era. The hybrid nature of these systems – distributed memory across nodes and shared memory with non-uniform memory access within each node – poses a challenge to application developers. In this paper, […]
Mar, 28

Parallel data mining on graphics processors

We introduce GPUMiner, a novel parallel data mining system that utilizes new-generation graphics processing units (GPUs). Our system relies on the massively multi-threaded SIMD (Single Instruction, Multiple-Data) architecture provided by GPUs. As specialpurpose co-processors, these processors are highly optimized for graphics rendering and rely on the CPU for data input/output as well as complex program […]
Mar, 27

Study on Transient Temperature Field Parallel Computing in Cooling Control Based on a GPU Fourier Method

With the evolution of graphics processing units (GPUs) in floating point operations and programmability, GPU has increasingly become powerful and cost-efficient computing architectures, its range of application has expanded tremendously, especially in the area of computational simulation. In this article, the Fourier method combined with GPU acceleration techniques is applied to simulate large-scale transient temperature […]
Mar, 27

Parallel frequent patterns mining algorithm on GPU

Extraction of frequent patterns from a transactional database is a fundamental task in data mining. Its applications include association rules, time series, etc. The Apriori approach is a commonly used generate-and-test approach to obtain frequent patterns from a database with a given threshold. Many parallel and distributed methods have been proposed for frequent pattern mining […]
Mar, 27

CaravelaMPI: Message Passing Interface for Parallel GPU-Based Applications

With the ever increasing demand for high quality 3D image processing on markets such as cinema and gaming, graphics processing units (GPUs) capabilities have shown tremendous advances. Although GPU-based cluster computing, which uses GPUs as the processing units, is one of the most promising high performance parallel computing platforms, currently there is no programming environment, […]
Mar, 27

A parallelization cost model for GPU

Using GPU for general computing has become an important research direction in high performance computing technology. However, this is not a lossless optimization method. Due to the impact of device initialization cost, data transmission delay, specific characteristics of programs, and other factors, the general computing on GPU may not always achieve the desired speedup, and […]
Mar, 27

Accelerate video decoding with generic GPU

Most modern computers or game consoles are equipped with powerful yet cost-effective graphics processing units (GPUs) to accelerate graphics operations. Though the graphics engines in these GPUs are specially designed for graphics operations, can we harness their computing power for more general nongraphics operations? The answer is positive. In this paper, we present our study […]
Mar, 27

Exploiting Parallelism in Iterative Irregular Maxflow Computations on GPU Accelerators

The Graphics Processing Unit (GPU) is an asymmetric, heterogeneous multi-core architecture that can be used for high performance parallel computing applications. However, a significant level of interest has been focused on algorithms for solving regular problems, as these applications typically map well to the GPU. Irregular applications, which rely on pointer or graph-based data structures, […]
Mar, 27

Image parallel processing based on GPU

In order to solve the compute-intensive character of image processing, based on advantages of GPU parallel operation, parallel acceleration processing technique is proposed for image. First, efficient architecture of GPU is introduced that improves computational efficiency, comparing with CPU. Then, Sobel edge detector and homomorphic filtering, two representative image processing algorithms, are embedded into GPU […]
Mar, 27

Data structure design for GPU based heterogeneous systems

This paper reports on our experience with data structure design for systems having both multiple CPU cores and a programmable graphics card. We integrate our data structures into the game-like application OpenSteerDemo and compare our data structures on two pc-systems. One System has a relative fast single core CPU and slower GPU, whereas the other […]
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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: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • 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: nVidia CUDA Toolkit 6.5.14, AMD APP SDK 3.0
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.3
  • SDK: AMD APP SDK 3.0

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

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