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Jie Shen, Diego Vela, Ankita Singh, Kexing Song, Guoshang Zhang, Bradon LaFreniere, Hao Chen
In this paper CUDA (Compute Unified Device Architecture) programming and OpenMP (Open Multi-Processing) are used for the GPU (Graphics Processing Unit) and CPU (Central Processing Unit) parallel computation of material damage. The material damage is evaluated by a multilevel finite element analysis within material domains reconstructed from a high-resolution micro-focus X-ray computed tomography system. An […]
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Kamal Chandra Reddy, Ankit Tharwani, Ch.Vamshi Krishna, Lakshminarayanan.V
Firewalls use a rule database to decide which packets will be allowed from one network onto another thereby implementing a security policy. In high-speed networks as the inter-arrival rate of packets decreases, the latency incurred by a firewall increases. In such a scenario, a single firewall become a bottleneck and reduces the overall throughput of […]
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Lalith Suresh, Navaneeth Rameshan, M. S. Gaur, Mark Zwolinski, Vijay Laxmi
Logic simulation of a VLSI chip is a computationally intensive process. There exists an urgent need to map functional validation algorithms onto parallel architectures to aid hardware designers in meeting time-to-market constraints. In this paper, we propose three novel methods for logic simulation of combinational circuits on GPGPUs. Initial experiments run on two methods using […]
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Teemu Nylanden, Janne Janhunen, Olli Silven, Markku J. Juntti
Two real-valued signal models based on selective spanning with fast enumeration (SSFE) and layered orthogonal lattice detector (LORD) algorithms are implemented on a Nvidia graphics processing unit (GPU). A 2×2 multiple-input multiple-output (MIMO) antenna system with 16-quadrature amplitude modulation (16-QAM) is assumed. The chosen level update vector for SSFE is based on computer simulation results […]

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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
  • 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

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