12914

Posts

Oct, 10

Introducing SLAMBench, a performance and accuracy benchmarking methodology for SLAM

Real-time dense computer vision and SLAM offer great potential for a new level of scene modelling, tracking and real environmental interaction for many types of robot, but their high computational requirements mean that use on mass market embedded platforms is challenging. Meanwhile, trends in low-cost, low-power processing are towards massive parallelism and heterogeneity, making it […]
Oct, 10

Code Refinement of Stencil Codes

A straightforward implementation of an algorithm in a general-purpose programming language does usually not deliver peak performance: Compilers often fail to automatically tune the code for certain hardware peculiarities like memory hierarchy or vector execution units. Manually tuning the code is firstly error-prone as well as time-consuming and secondly taints the code by exposing those […]
Oct, 10

Parallel implementation of linear repetitive processes identification using subspace algorithms

This paper presents a new parallel approach to identification of linear repetitive processes based on subspace algorithms. Parallel realizations of these algorithms are tested on various graphic cards that use NVIDIA CUDA technology. The paper describes implementation of subspace identification algorithms and their parallel speedup, efficiency, throughput, and delay. The parallel approach to the identification […]
Oct, 10

Accelerating Protein Coordinate Conversion using GPUs

For modeling proteins in conformational states, two methods of representation are used: internal coordinates and Cartesian coordinates. Each of these representations contain a large amount of structural and simulation information. Different processing steps require one or the other representation. Our goal is to rapidly translate between these coordinate spaces so that a scientist can choose […]
Oct, 10

FDTD on Distributed Heterogeneous Multi-GPU Systems

Finite-Difference Time-Domain (FDTD) is a popular technique for modeling computational electrodynamics, and is used within many research areas, such as the development of antennas, ultrasound imaging, and seismic wave propagation. Simulating large domains can however be very compute and memory demanding, which has motivated the use of cluster computing, and lately also the use of […]
Oct, 8

cuDNN: Efficient Primitives for Deep Learning

We present a library that provides optimized implementations for deep learning primitives. Deep learning workloads are computationally intensive, and optimizing the kernels of deep learning workloads is difficult and time-consuming. As parallel architectures evolve, kernels must be reoptimized for new processors, which makes maintaining codebases difficult over time. Similar issues have long been addressed in […]
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 […]
Page 4 of 761« First...23456...102030...Last »

* * *

* * *

Like us on Facebook

HGPU group

167 people like HGPU on Facebook

Follow us on Twitter

HGPU group

1275 peoples are following HGPU @twitter

* * *

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.

The information send to hgpu.org will be treated according to our Privacy Policy

HGPU group © 2010-2014 hgpu.org

All rights belong to the respective authors

Contact us: