Aug, 26

Speedup of Type-1 Fuzzy Logic Systems on Graphics Processing Units Using CUDA

Parallelcomputing is one of significant components of the High Performance Computing (HPC) and is being used to solve problems, which are large and complex in nature. Fuzzy Logic System (FLS) is a problem that becomes computationally intensive with increase in number of inputs and/or fuzzy rules. Running an FLS is highly parallel in nature, therefore, […]
Aug, 23

Structured Orthogonal Inversion of Block p-Cyclic Matrices on Multicore with GPU Accelerators

We present a block structured orthogonal factorization (BSOF) algorithm and its parallelization for computing the inversion of block p-cyclic matrices.We aim at the high performance on multicores with GPU accelerators. We provide a quantitative performance model for optimal host-device load balance, and validate the model through numerical tests. Benchmarking results show that the parallel BSOF […]
Aug, 23

GPU Virtualization for High Performance General Purpose Computing on the ESX Hypervisor

Graphics Processing Units (GPU) have become important components in high performance computing (HPC) systems for their massively parallel computing capability and energy efficiency. Virtualization technologies are increasingly applied to HPC to reduce administration costs and improve system utilization. However, virtualizing the GPU to support general purpose computing presents many challenges because of the complexity of […]
Aug, 23

Illustrative Rendering of Particle Systems

Sets of particles are a frequently used tool for the exploration of time-varying flow fields due to their ease of use and conceptual simplicity. Understanding temporal changes in such particle systems can be difficult with traditional visualization methods such as isosurface rendering and particle splatting. These types of methods only show the current shape of […]
Aug, 23

Estimating GPU Speedups for Programs Without Writing a Single Line of GPU Code

Heterogeneous processing using GPUs is here to stay and today spans mobile devices, laptops, and supercomputers. Although modern software development frameworks like OpenCL and CUDA serve as a high productivity environment, software development for GPUs is time consuming. First, much work needs to be done to restructure software and data organization to match the GPU’s […]
Aug, 23

Encrypting video and image streams using OpenCL code on-demand

The amount of multimedia information transmitted through the web is very high and increasing. Generally, this kind of data is not correctly protected, since users do not appreciate the amount of information that images and videos may contain. In this work, we present architecture for managing safely multimedia transmission channels. The idea is to encrypt […]
Aug, 23

High Level Programming for Heterogeneous Architectures

This work presents an effort to bridge the gap between abstract high level programming and OpenCL by extending an existing high level Java programming framework (APARAPI), based on OpenCL, so that it can be used to program FPGAs at a high level of abstraction and increased ease of programmability. We run several real world algorithms […]
Aug, 23

Caffe: Convolutional Architecture for Fast Feature Embedding

Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models. The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures. Caffe fits industry and […]
Aug, 23

A Domain Specific Approach to Heterogeneous Computing: From Availability to Accessibility

We advocate a domain specific software development methodology for heterogeneous computing platforms such as Multicore CPUs, GPUs and FPGAs. We argue that three specific benefits are realised from adopting such an approach: portable, efficient implementations across heterogeneous platforms; domain specific metrics of quality that characterise platforms in a form software developers will understand; automatic, optimal […]
Aug, 23

Code Generation for High-Level Synthesis of Multiresolution Applications on FPGAs

Multiresolution Analysis (MRA) is a mathematical method that is based on working on a problem at different scales. One of its applications is medical imaging where processing at multiple scales, based on the concept of Gaussian and Laplacian image pyramids, is a well-known technique. It is often applied to reduce noise while preserving image detail […]
Aug, 23

Parallel Bio-Inspired Methods for Model Optimization and Pattern Recognition

Nature based computational models are usually inherently parallel. The collaborative intelligence in those models emerges from the simultaneous instruction processing by simple independent units (neurons, ants, swarm members, etc…). This dissertation investigates the benefits of such parallel models in terms of efficiency and accuracy. First, the viability of a parallel implementation of bio-inspired metaheuristics for […]
Aug, 23

A Comprehensive Survey on Various Evolutionary Algorithms on GPU

This paper presents a comprehensive survey on parallelizing computations involved in optimization problem on Graphics Processing Unit (GPU) using CUDA (Compute Unified Design Architecture). GPU have multithread cores with high memory bandwidth which allow for greater ease of use and also more radially support a layer body of applications. Many researchers have reported significant speedups […]
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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

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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