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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 […]
Aug, 23

Exploiting Parallel Processing Power of GPU for High Speed Frequent Pattern Mining

Frequent pattern mining is one of the widely used data mining techniques for discovering trends or patterns from databases. As data is growing in exponential pace, data mining activities need more powerful computing. Fortunately modern GPUs (Graphics Processing Units) have specialized electronic circuits and support parallel processing. GPUs are capable of processing huge amount of […]
Aug, 21

GPGPU Programming for Games and Science

An In-Depth, Practical Guide to GPGPU Programming Using Direct3D 11. GPGPU Programming for Games and Science demonstrates how to achieve the following requirements to tackle practical problems in computer science and software engineering: Robustness, Accuracy, Speed, Quality source code that is easily maintained, reusable, and readable. The book primarily addresses programming on a graphics processing […]
Aug, 21

GPU accelerated rendering of vector based maps on iOS

Digital maps can be represented as either raster (bitmap images) or vector data. Vector maps are often preferable as they can be stored more efficiently and rendered irrespective of screen resolution. Vector map rendering on demand can be a computationally intensive task and has to be implemented in an efficient manner to ensure good performance […]
Aug, 21

A Computational Realization of a Semi-Lagrangian Method for Solving the Advection Equation

A parallel implementation of a method of the semi-Lagrangian type for the advection equation on a hybrid architecture com-putation system is discussed. The difference scheme with variable stencil is constructed on the base of an integral equality between the neighboring time levels. The proposed approach allows one to avoid the Courant-Friedrichs-Lewy restriction on the relation […]
Aug, 21

Volumetric Rendering Techniques for Scientific Visualization

Direct volume rendering is widely used in many applications where the inside of a transparent or a partially transparent material should be visualized. We have explored several aspects of the problem. First, we proposed a view-dependent selective refinement scheme in order to reduce the high computational requirements without affecting the image quality significantly. Then, we […]
Aug, 21

Error Resilience Evaluation on GPGPU Applications

While graphics processing units (GPUs) have gained wide adoption as accelerators for general-purpose applications (GPGPU), the end-to-end reliability implications of their use have not been quantified. Fault injection is a widely used method for evaluating the reliability of applications. However, building a fault injector for GPGPU applications is challenging due to their massive parallelism, which […]
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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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