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CBench: Analyzing Compute Performance for Modern NVIDIA and AMD GPUs

Varun Sampath
University of Pennsylvania
University of Pennsylvania, 2011

@article{sampath2011cbench,

   title={CBench: Analyzing Compute Performance for Modern NVIDIA and AMD GPUs},

   author={Sampath, V.},

   year={2011}

}

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General purpose GPU computation is a fast growing ?eld with a variety of applications. For maximum performance, though, mapping high-level parallel algorithms to vendor hardware requires a solid grasp of both the algorithm’s computational requirements and the microarchitectural limitations of the GPU. This work aims to explore the performance of high and low arithmetic intensity workloads on the latest NVIDIA and AMD GPU hardware, codenamed Fermi and Barts, respectively. A summed area table generator and a Black-Scholes option pricer were used as benchmarks to analyze performance for compute- and bandwidth-bound algorithms. It was found that the AMD Barts GPU provided a 50% performance boost on the Black-Scholes compute-bound workload, whereas Fermi excelled at the more memory-bound summed area table computation.
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