Applications
Tags: 3D Graphics and Realism, Algorithms, Computer science, CUDA, Image generation, nVidia, nVidia GeForce GTX 580
Tags: CUDA, Finance, Mixed precision, nVidia, Tesla K20
Tags: Computational Complexity, CUDA, Data parallelism, FEM, Finite element method, Image processing, nVidia, Tesla C2070
Tags: Computer science, CUDA, Databases, nVidia, nVidia Quadro FX 6000
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Tags: CUDA, Differential equations, nVidia, nVidia GeForce GT 640 M, ODEs, Ordinary differential equations, Physics, Thesis
Tags: Computer science, CUDA, Differential equations, Mathematics, nVidia, nVidia GeForce GTX 560 Ti, Partial differential equations, PDEs, Tesla K20, Thesis
Tags: CUDA, nVidia, nVidia GeForce GTX 580, Performance
Tags: Computational Physics, CUBLAS, CUDA, Education, FFT, Mathematical Software, nVidia, nVidia GeForce GTX 320 M, Physics, Tesla C2050
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
- GPU Scripting and Code Generation with PyCUDA
- Secrets from the GPU
- 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
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
A parallel decoding algorithm of LDPC codes using CUDA
Mr. Scan: Extreme Scale Density-Based Clustering using a Tree-Based Network of GPGPU Nodes
Optimizing MapReduce for GPUs with effective shared memory usage
Comprehensive Analysis of High-Performance Computing Methods for Filtered Back-Projection
Kernelet: High-Throughput GPU Kernel Executions with Dynamic Slicing and Scheduling
CUDA implementation of the algorithm for simulating the epidemic spreading over large networks
Stencil-Aware GPU Optimization of Iterative Solvers
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.
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