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Energy prediction of CUDA application instances using dynamic regression models

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Abstract

GPGPUs no longer seem to be an inconsequential component of supercomputing architectures, and a section of HPC application developers no longer refrain from utilizing GPGPUs. CUDA, in general, has remained a successful computing platform for those architectures. Thousands of scientific applications from various domains, such, as bio-informatics, HEP, and so forth, have been accelerated using CUDA in the past few years. In fact, the energy consumption issue still remains a serious challenge for the HPC and GPGPU communities. This paper proposes energy prediction approaches using dynamic regression models, such as parallel dynamic random forest modeling (P-DynRFM), dynamic random forest modeling (DynRFM), dynamic support vector machines (DynSVM), and dynamic linear regression modeling (DynLRM). These models identify energy efficient CUDA application instances while considering the block size, grid size, and the other tunable parameters, such as problem size. The predictions of CUDA application instances have been attained by executing a few CUDA application instances and predicting the other CUDA application instances based on the performance metrics of applications, such as number of instructions, memory issues, and so forth. The proposed energy prediction mechanisms were evaluated with CUDA applications such as Nbody and Particle Simulations on two GPGPU machines. The proposed dynamic prediction mechanisms achieved a 50.26 to 61.23 percentage of energy/performance prediction improvements when compared to the classical prediction models; and, the parallel implementation of the dynamic RFM (P-DynRFM) recorded over 83 percentage points of prediction time improvements.

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Acknowledgements

This work is funded by the Department of Science and Technology of India under the Indo-Austrian PPP Scheme - Engineering Sciences division. The authors thank DST (India) and FWF (Austria) for funding this research work. In addition, they thank the reviewers of this paper for providing constructive comments.

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Authors and Affiliations

  1. HPCCLoud Research Laboratory, SXCCE, Anna University, Chennai, India

    R. S. Rejitha, Shajulin Benedict, Suja A. Alex & Shany Infanto

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  1. R. S. Rejitha
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  2. Shajulin Benedict
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  3. Suja A. Alex
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Correspondence to R. S. Rejitha.

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This work is partially funded by the Indo-Austrian project scheme of FWF-DST (India): DST No. INT/AUA/FWF/P-02/2013.

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Rejitha, R.S., Benedict, S., Alex, S.A. et al. Energy prediction of CUDA application instances using dynamic regression models. Computing 99, 765–790 (2017). https://doi.org/10.1007/s00607-016-0534-5

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