8118
Miao Xin, Hao Li, Joan Lu
MapReduce is an efficient distributed computing model on large data sets. The data processing is fully distributed on huge amount of nodes, and a MapReduce cluster is of highly scalable. However, single-node performance is gradually to be a bottleneck in computeintensive jobs, which makes it difficult to extend the MapReduce model to wider application fields […]
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W. Feng, H. Lin, T. Scogland, J. Zhang
In the past, evaluating the architectural innovation of parallel computing devices relied on a benchmark suite based on existing programs, e.g., EEMBC or SPEC. However, with the growing ubiquity of parallel computing devices, we argue that it is unclear how best to express parallel computation, and hence, a need exists to identify a higher level […]
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Taneem Ahmed
With the availability of multi-core processors, high capacity FPGAs, and GPUs, a heterogeneous platform with tremendous raw computing capacity can be constructed consisting of any number of these computing elements. However, one of the major challenges for constructing such a platform is the lack of a standardized framework under which an application’s computational task and […]
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Mayank Daga, Ashwin M. Aji, Wu-chun Feng
The graphics processing unit (GPU) has made significant strides as an accelerator in parallel computing. However, because the GPU has resided out on PCIe as a discrete device, the performance of GPU applications can be bottlenecked by data transfers between the CPU and GPU over PCIe. Emerging heterogeneous computing architectures that "fuse" the functionality of […]
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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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