Programming
Tags: Algorithms, CUDA, Hadronic Colliders, High Energy Physics - Experiment, nVidia, Physics, Tesla K20
Tags: CUDA, Differential equations, nVidia, nVidia GeForce GT 640 M, ODEs, Ordinary differential equations, Physics, Thesis
Tags: Computational Physics, CUBLAS, CUDA, Education, FFT, Mathematical Software, nVidia, nVidia GeForce GTX 320 M, Physics, Tesla C2050
Tags: Algorithms, CUDA, High Energy Physics - Lattice, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580, nVidia Quadro FX 4000, Package, Physics, QCD, Tesla C2070
Tags: Boltzmann equation, Fluid dynamics, Lattice Boltzmann model, nVidia, nVidia GeForce GTX 280, Physics
Tags: Computational Physics, CUDA, Fortran, Monte Carlo integration, nVidia, Physics, Pseudo-random number generators, QCD, Tesla C2075
Tags: Algorithms, Computational Physics, CUDA, nVidia, Physics, Quantum Physics, Spin Systems, Statistical Mechanics, Tesla C2070
Tags: CUDA, Data parallelism, Heterogeneous systems, Monte Carlo simulation, MPI, nVidia, Physics, Tesla M2070, Thesis
Tags: CUDA, Image reconstruction, Medical Physics, Medicine, nVidia, Physics, Tesla C1060, Tesla C2050, Tomography
Tags: Chemistry, CUDA, Liquid crystals, nVidia, nVidia GeForce GTX 680, Physics, Statistical Mechanics, Thesis
Most viewed papers (last 30 days)
- Graphics Programming on the Web WebCL Course Notes
- Simulating the universe with GPU-accelerated supercomputers: n-body methods, tests, and examples
- Secrets from the GPU
- Implementations of the FFT algorithm on GPU
- Fluid Motion Modelling Using Vortex Particle Method on GPU
- Adding GPU Computing to Computer Organization Courses
- libWater: Heterogeneous Distributed Computing Made Easy
- Fast Implementation of Scale Invariant Feature Transform Based on CUDA
- Faster Upper Body Pose Estimation and Recognition Using CUDA
- Analyzing Locality of Memory References in GPU Architectures
Rating
Optimizing a Biomedical Imaging Orientation Score Framework
Graphics Programming on the Web WebCL Course Notes
Adaptive Dynamic Load Balancing in Heterogeneous Multiple GPUs-CPUs Distributed Setting: Case Study of B&B Tree Search
Duality based optical flow algorithms with applications
In-Place Recursive Approach for All-Pairs Shortest Paths Problem Using OpenCL
A parallel decoding algorithm of LDPC codes using CUDA
Optimizing MapReduce for GPUs with effective shared memory usage
OpenCL parallel Processing using General Purpose Graphical Processing units - TiViPE software development
Kernelet: High-Throughput GPU Kernel Executions with Dynamic Slicing and Scheduling
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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