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Zamani, H., Liu, Y.L., Tripathy, D., Bhuyan, L. and Chen, Z.Z. (2019) Greenmm: Energy Efficient GPU Matrix Multiplication through Undervolting. Proceedings of the ACM International Conference on Supercomputing (ICS ’19), Phoenix, 26-28 June 2019, 308-318.
https://doi.org/10.1145/3330345.3330373

has been cited by the following article:

• TITLE: Runtime Power Allocation Based on Multi-GPU Utilization in GAMESS

  AUTHORS: Masha Sosonkina, Vaibhav Sundriyal, Jorge Luis Galvez Vallejo

  KEYWORDS: Time Series Forecasting, ARIMA, Power Allocation, Performance Modeling, GAMESS, GPU Utilization

  JOURNAL NAME: Journal of Computer and Communications, Vol.10 No.9, September 22, 2022

  ABSTRACT: To improve the power consumption of parallel applications at the runtime, modern processors provide frequency scaling and power limiting capabilities. In this work, a runtime strategy is proposed to maximize performance under a given power budget by distributing the available power according to the relative GPU utilization. Time series forecasting methods were used to develop workload prediction models that provide accurate prediction of GPU utilization during application execution. Experiments were performed on a multi-GPU computing platform DGX-1 equipped with eight NVIDIA V100 GPUs used for quantum chemistry calculations in the GAMESS package. For a limited power budget, the proposed strategy may deliver as much as hundred times better GAMESS performance than that obtained when the power is distributed equally among all the GPUs.