Oct, 8

Movement Tracking in Terrain Conditions Accelerated with CUDA

The paper presents a solution to the problem of movement tracking in images acquired from video cameras monitoring outside terrain. The solution is resistant to such adverse factors as: leaves fluttering, grass waving, smoke or fog, movement of clouds etc. The presented solution is based on well known image processing methods, nevertheless the key was […]
Oct, 8

KBLAS: An Optimized Library for Dense Matrix-Vector Multiplication on GPU Accelerators

KBLAS is a new open source high performance library that provides optimized kernels for a subset of Level 2 BLAS functionalities on CUDA-enabled GPUs. Since performance of dense matrix-vector multiplication is hindered by the overhead of memory accesses, a double-buffering optimization technique is employed to overlap data motion with computation. After identifying a proper set […]
Oct, 8

A Framework for the Volumetric Integration of Depth Images

Volumetric models have become a popular representation for 3D scenes in recent years. One of the breakthroughs leading to their popularity was KinectFusion, where the focus is on 3D reconstruction using RGB-D sensors. However, monocular SLAM has since also been tackled with very similar approaches. Representing the reconstruction volumetrically as a truncated signed distance function […]
Oct, 8

A new ray-tracing scheme for 3D diffuse radiation transfer on highly parallel architectures

We present a new numerical scheme to solve the transfer of diffuse radiation on three-dimensional mesh grids which is efficient on processors with highly parallel architecture such as recently popular GPUs and CPUs with multi- and many-core architectures. The scheme is based on the ray-tracing method and the computational cost is proportional to N^5/3_m where […]
Oct, 8

Redução de Complexidade de Tempo em GPUs

Este artigo aborda a questão da construção de algoritmos paralelos e avaliação dos resultados a partir da redução de complexidade obtida pelo emprego massivo do paralelismo, em contraponto a obtenção de speedups como delineadores da construção de algoritmos paralelos. Mostra-se que, em um problema simples de pesquisa em um vetor, é mais proveitosa.
Oct, 6

International Conference on Computer and Information Technology, ICCIT 2015

Submission Deadline: 2015-02-10 Publications: Accepted papers will be published in the one of the following Journal with ISSN. *International Journal of Computer Theory and Engineering (IJCTE) (ISSN: 1793-8201) Abstracting/Indexing: Index Copernicus, Electronic Journals Library, EBSCO, Engineering & Technology Digital Library, Google Scholar, Ulrich’s Periodicals Directory, Crossref, ProQuest, WorldCat, and EI (INSPEC, IET), Cabell’s Directories. *International […]
Oct, 6

Using Graphics Processing Unit to Accelerate Database Query Execution

One of the major problems in database management systems is handling large amounts of data while providing short response time. Problem is not only proper manner of storing records but also efficient way of processing them. In the meantime GPUs developed computational power many times greater than that offered by comparable CPUs. In our research […]
Oct, 6

Real-time Multi-view Depth Generation Using CUDA Multi-GPU

In this paper, we propose a real-time multi-view depth generation method using compute unified device architecture (CUDA) multi-graphics processing units (GPU). The objective is to generate multi-view depth maps in real-time. We employ eight color cameras and three depth cameras. After capturing multi-view color and depth data, we warp the depth information to color camera […]
Oct, 6

Accelerating NTRU Encryption with Graphics Processing Units

Lattice based cryptography is attractive for its quantum computing resistance and efficient encryption/ decryption process. However, the Big Data issue has perplexed most lattice based cryptographic systems since the overall processing is slowed down too much. This paper intends to analyze one of the major lattice-based cryptographic systems, Nth-degree truncated polynomial ring (NTRU), and accelerate […]
Oct, 6

Load Balancing in Data Warehouse – Evolution and Perspectives

The problem of load balancing is one of the crucial features in distributed data warehouse systems. In this article original load balancing algorithms are presented. The Adaptive Load Balancing Algorithms for Queries (ALBQ) and the algorithms that use grammars and learning machines in managing the ETL process. These two algorithms base the load balancing on […]
Oct, 6

Embedding GPU Computations in Hadoop

As the size of high performance applications increases, four major challenges including heterogeneity, programmability, fault resilience, and energy efficiency have arisen in the underlying distributed systems. To tackle with all of them without sacrificing performance, traditional approaches in resource utilization, task scheduling and programming paradigm should be reconsidered. While Hadoop has handled data-intensive applications well […]
Oct, 4

Deep Dynamic Neural Networks for Gesture Segmentation and Recognition

The purpose of this paper is to describe a novel method called Deep Dynamic Neural Networks(DDNN) for the Track 3 of the Chalearn Looking at People 2014 challenge [1]. A generalised semi-supervised hierarchical dynamic framework is proposed for simultaneous gesture segmentation and recognition taking both skeleton and depth images as input modules. First, Deep Belief […]
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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
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  • 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

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