Programming
Tags: Computational Complexity, CUDA, Data parallelism, FEM, Finite element method, Image processing, nVidia, Tesla C2070
Tags: Algorithms, ATI, ATI Radeon HD 5870, Compression, Computational Complexity, H.264/AVC, Image processing, OpenCL
Tags: Algorithms, Compression, Computational Complexity, CUDA, Discrete Wavelet Transform, GTX 260, nVidia
Tags: Algorithms, Computational Complexity, Heterogeneous systems, Image processing, nVidia, nVidia GeForce GTX 590, Tesla C1060
Tags: Algorithms, Computational Complexity, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Tesla C1060
Tags: Artificial intelligence, Computational Complexity, Computer science, Computer vision, CUDA, Neural networks, nVidia, nVidia GeForce GTX 580
Tags: Algorithms, ATI, ATI FireStream 9370, Computational Complexity, Computer science, nVidia, OpenCL, OpenMP, Security, Tesla M2090
Tags: Algorithms, Computational Complexity, Computer science, CUDA, nVidia, nVidia GeForce GTX 260, Sorting
Tags: Compression, Computational Complexity, CUDA, H.264/AVC, Heterogeneous systems, Image processing, nVidia, nVidia GeForce GTX 480
Tags: Computational Complexity, Computer science, CUDA, nVidia, nVidia GeForce GTX 580, Package, Speech recognition, Tesla M2090
Tags: Acoustics, Computational Complexity, CUDA, nVidia, nVidia GeForce GT 540 M, nVidia GeForce GTX 580, Physics, Raytracing, Thesis
Most viewed papers (last 30 days)
- Graphics Programming on the Web WebCL Course Notes
- Use NVIDIA CUDA technology to create genetic algorithms with extensive population
- Simulating the universe with GPU-accelerated supercomputers: n-body methods, tests, and examples
- Implementations of the FFT algorithm on GPU
- Secrets from the GPU
- GPU Scripting and Code Generation with PyCUDA
- A General-Purpose GPU Reservoir Computer
- One OpenCL to Rule Them All?
- Fluid Motion Modelling Using Vortex Particle Method on GPU
- Adding GPU Computing to Computer Organization Courses
Rating
Duality based optical flow algorithms with applications
Adaptive Dynamic Load Balancing in Heterogeneous Multiple GPUs-CPUs Distributed Setting: Case Study of B&B Tree Search
Graphics Programming on the Web WebCL Course Notes
Automatic Compilation for Heterogeneous Architectures with Single Assignment C
Mr. Scan: Extreme Scale Density-Based Clustering using a Tree-Based Network of GPGPU Nodes
Comprehensive Analysis of High-Performance Computing Methods for Filtered Back-Projection
A parallel decoding algorithm of LDPC codes using CUDA
Optimizing MapReduce for GPUs with effective shared memory usage
Kernelet: High-Throughput GPU Kernel Executions with Dynamic Slicing and Scheduling
CUDA implementation of the algorithm for simulating the epidemic spreading over large networks
Recent source codes
Events
October 1-4, 2013 Lyon, France The 2013 International Workshop on Embedded Multicore Systems, ICPP-EMS 2013 |
November 13-15, 2013 Zhangjiajie, China 3rd International Workshop on Embedded Multi-core Computing and Applications, EMCA 2013 |
February 2-6, 2014 San Francisco, USA |
February 12-14, 2014 Turin, Italy |
November 11-14, 2013 San Jose, California, USA |
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
- 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
- HDD: 2TB, Raid-0
- OS: OpenSUSE 11.4
- SDK: AMD APP SDK 2.8
- 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
- HDD: 2TB, Raid-0
- OS: OpenSUSE 12.2
- SDK: nVidia CUDA Toolkit 5.0.35, AMD APP SDK 2.8
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 © 2010-2013 hgpu.org
All rights belong to the respective authors
Contact information:
contact@hgpu.org




