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An experimental approach to performance measurement of heterogeneous parallel applications using CUDA

Allen D. Malony,Scott Biersdorff,Wyatt Spear,Shangkar Mayanglambam
University of Oregon, Eugene, OR
Proceedings of the 24th ACM International Conference on Supercomputing (2010), Publisher: ACM, Pages: 127-136

@conference{malony2010experimental,

   title={An experimental approach to performance measurement of heterogeneous parallel applications using CUDA},

   author={Malony, A.D. and Biersdorff, S. and Spear, W. and Mayanglambam, S.},

   booktitle={Proceedings of the 24th ACM International Conference on Supercomputing},

   pages={127–136},

   year={2010},

   organization={ACM}

}

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Heterogeneous parallel systems using GPU devices for application acceleration have garnered significant attention in the supercomputing community. However, to realize the full potential of GPU computing, application developers will require tools to measure and analyze accelerator performance with respect to the parallel execution as a whole. A performance measurement technology for the NVIDIA CUDA platform has been developed and integrated with the TAU parallel performance system. The design of the TAUcuda package is based on an experimental NVIDIA CUDA driver and associated runtime and device libraries. In any environment where the CUDA experimental driver is installed, TAUcuda can provide detailed performance information regarding the execution of GPU kernels and the interactions with the parallel program without any modification to the program source or executable code. The paper describes the TAUcuda technology and how it is integrated with the TAU measurement framework to provide integrated performance views. Various examples of TAUcuda use are presented, including CUDA SDK examples, a GPU version of the Linpack benchmark, and a scalable molecular dynamics application, NAMD.
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