Department of Computer Science Seminar
2020 Series

Performance and Power Modeling of GPU Systems with Dynamic Voltage and Frequency Scaling

Mr. Qiang Wang
PhD Candidate
Department of Computer Science
Hong Kong Baptist University

Date: June 30, 2020 (Tuesday)
Time: 2:00 - 3:00 pm
Venue: Zoom ID: 950 8063 5267
(The password and direct link will only be provided to registrants)

Registration: https://bit.ly/sem-zm
(Deadline: 2:00pm, 29 June 2020)

To address the ever-increasing demand for computing capacities, more and more heterogeneous systems have been designed to use both general-purpose and special-purpose processors. The huge energy consumption of them raises new environmental concerns and challenges. Besides performance, energy efficiency is another key factor to be considered by system designers and consumers. In particular, contemporary graphics processing units (GPUs) support dynamic voltage and frequency scaling (DVFS) to balance computational performance and energy consumption. However, accurate and straightforward performance and power estimation for a given GPU kernel under different frequency settings is still lacking for real hardware, which is essential to determine the best frequency configuration for energy saving. In this talk, we investigate how to improve the energy efficiency of GPU systems by accurately modeling the effects of GPU DVFS on the target GPU kernel. First, we reveal a fine-grained analytical model to estimate the execution time of GPU kernels with both core and memory frequency scaling. Compared to the cycle-level simulators, which are too slow to apply on real hardware, our model only needs one-off micro-benchmarks to extract a set of hardware parameters and kernel performance counters, and achieve much better accuracy than the existing methods. Second, we explore the usage of machine learning methods to predict the execution time and runtime power of a GPU kernel under different voltage and frequency settings.

Wang Qiang is a PhD candidate at the Department of Computer Science, Hong Kong Baptist University. He received his B.Eng. degree in Computer Science and Technology from South China University of Technology, China, in 2014 and was an awardee of Hong Kong PhD Fellowship in 2015. His research interests includes GPU Computing, Parallel Computing, Energy Efficiency of GPU, Deep Learning System and Stereo Matching. Over the past few years, he has published over ten papers in top conferences and journals, including ICRA, TPDS, ICDCS and INFOCOM.

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(For enquiry, please contact Computer Science Department at 3411 2385)