Jan, 19

Hybrid Multicore Algorithms for Some Semi-Numerical Applications and Graphs

The computing industry has undergone several paradigm shifts in the last few decades. Fueled by the need of faster computing, larger data and real time processing needs parallel computing has emerged as one of the dominant paradigms. Motivated by the success achieved in distributed computing models and the limitations faced by single core processors, parallel […]
Jan, 19

Indexing of Spatiotemporal Trajectories for Efficient Distance Threshold Similarity Searches on the GPU

Applications in many domains search moving object trajectory databases. The distance threshold search finds all trajectories within a given distance of a query trajectory. We develop three GPU distance threshold search implementations that use indexing techniques significantly different from those used in CPU implementations. We determine experimentally under which conditions each approach performs well using […]
Jan, 16

CURFIL: Random Forests for Image Labeling on GPU

Random forests are popular classifiers for computer vision tasks such as image labeling or object detection. Learning random forests on large datasets, however, is computationally demanding. Slow learning impedes model selection and scientific research on image features. We present an open-source implementation that significantly accelerates both random forest learning and prediction for image labeling of […]
Jan, 16

SW#db: GPU-accelerated exact sequence similarity database search

The deluge of next-generation sequencing (NGS) data and expanding database poses higher requirements for protein similarity search. State-of-the-art tools such as BLAST are not fast enough to cope with these requirements. Because of that it is necessary to create new algorithms that will be faster while keeping similar sensitivity levels. The majority of protein similarity […]
Jan, 16

Parallel Algorithms for Counting Problems on Graphs Using Graphics Processing Units

The availability of Graphics Processing Units (GPUs) with multicore architecture have enabled parallel computations using extensive multi-threading. Recent advancements in computer hardware have led to the usage of graphics processors for solving general purpose problems. Using GPUs for computation is a highly efficient and low-cost alternative as compared to currently available multicore Central Processing Units […]
Jan, 16

GPU Processing for UAS-Based LFM-CW Stripmap SAR

Unmanned air systems (UAS) provide an excellent platform for synthetic aperture radar (SAR), enabling surveillance and research over areas too difficult, dangerous, or costly to reach using manned aircraft. However, the nimble nature of the small UAS makes them more susceptible to external forces, thus requiring significant motion compensation in order for SAR images to […]
Jan, 16

Parallel Implementation of the Finite Element Method on Graphics Processors for the Solution of Incompressible Flows

In recent years clock speeds and memory bandwidths of Graphics Processing Units (GPUs) increased dramatically compared to CPUs. Also GPU vendors developed and freely released new programming tools to make scientific computing on GPUs easier. With these recent developments the use of GPUs for general purpose computing becomes a popular research field. Researchers previously demonstrated […]
Jan, 15

A Novel Computational Model for GPUs with Applications to Efficient Algorithms

We propose a novel computational model for GPUs. Known parallel computational models such as the PRAM model are not appropriate for evaluating GPU-based algorithms. Our model, called AGPU, abstracts the essence of current GPU architectures such as global and shared memory, memory coalescing and bank conflicts. Using our model, we can evaluate asymptotic behavior of […]
Jan, 15

Identification and Elimination of Platform-Specific Code Smells in High Performance Computing Applications

A code smell is a code pattern that might indicate a code or design problem, which makes the application code hard to evolve and maintain. Automatic detection of code smells has been studied to help users find which parts of their application codes should be refactored. However, code smells have not been defined in a […]
Jan, 15

Reducing overheads of dynamic scheduling on heterogeneous chips

In recent processor development, we have witnessed the integration of GPU and CPUs into a single chip. The result of this integration is a reduction of the data communication overheads. This enables an efficient collaboration of both devices in the execution of parallel workloads. In this work, we focus on the problem of efficiently scheduling […]
Jan, 15

Batched Matrix Computations on Hardware Accelerators Based on GPUs

Scientific applications require solvers that work on many small size problems that are independent from each other. At the same time, the high-end hardware evolves rapidly and becomes ever more throughput-oriented and thus there is an increasing need for effective approach to develop energy efficient, high-performance codes for these small matrix problems that we call […]
Jan, 15

High Performance GPU-based Fourier Volume Rendering

FVR (Fourier volume rendering) is a significant visualization technique that has been used widely in digital radiography. As a results of its O(N^2logN) time complexity, it provides a faster alternative to spatial domain volume rendering algorithms that are O(N^3) computationally complex. Relying on the Fourier projection-slice theorem, this technique operates on the spectral representation of […]
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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 12.2
  • SDK: 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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