6134
Wu Jiun-Yu, Wang Yao-Tsung, Shiau Steven, Wang Hui Ching
In this paper, an energy efficient architecture for Build Energy Efficient GPU and CPU Cluster Using DRBL is proposed. This architecture helps administrator not only to quickly deploy and manage GPU and CPU Cluster environment, but also bring benefit of energy efficiency in scientific computing. The experiment simulates 3 cases to prove energy efficiency. We […]
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Hari Sundararajan
After many years of focusing on "faster" computers, people have started taking notice of the fact that the race for "speed" has had the unfortunate side effect of increasing the total power consumed, thereby increasing the total cost of ownership of these machines. The heat produced has required expensive cooling facilities. As a result, it […]
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Aalap Tripathy, Suneil Mohan, Rabi Mahapatra
Emerging semantic search techniques require fast comparison of large "concept trees". This paper addresses the challenges involved in fast computation of similarity between two large concept trees using a CUDA-enabled GPGPU co-processor. We propose efficient techniques for the same using fast hash computations, membership tests using Bloom Filters and parallel reduction. We show how a […]
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Susanne Albers
Algorithmic solutions can help reduce energy consumption in computing environs. Energy conservation is a major concern today. Federal programs provide incentives to save energy and promote the use of renewable energy resources. Individuals, companies, and organizations seek energyefficient products as the energy cost to run equipment has grown to be a major factor.
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T. Scogland, H. Lin, W. Feng
The graphics processing unit (GPU) has evolved from a single-purpose graphics accelerator to a tool that can greatly accelerate the performance of high-performance computing (HPC) applications. Previous studies have shown that discrete GPUs, while energy efficient for compute-intensive scientific applications, consume very high power. In fact, a compute-capable discrete GPU can draw more than 200 […]

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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

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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