sponsored byACMIEEE The International Conference for High Performance 
Computing, Networking, Storage and Analysis
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SCHEDULE: NOV 15-20, 2015

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Modeling the Impact of Thread Configuration on Power and Performance of GPUs

SESSION: Regular & ACM Student Research Competition Poster Reception

EVENT TYPE: Posters, Receptions, ACM Student Research Competition

EVENT TAG(S): HPC Beginner Friendly, ACM Student Research Competition Poster

TIME: 5:15PM - 7:00PM

SESSION CHAIR(S): Michela Becchi, Manish Parashar, Dorian C. Arnold

AUTHOR(S):Tiffany A. Connors

ROOM:Level 4 - Lobby


Because graphics processing units (GPUs) are a low-cost option for achieving high computational power, they have become widely used in high-performance computing. However, GPUs can consume large amounts of power. Due to the associated energy costs, improving energy-efficiency has become a growing concern. By evaluating the impact of thread configuration on performance and power trade-off, energy-efficient solutions can be identified.
The impact that a thread configuration will have on the performance and power trade-off of a GPU kernel can be accurately predicted using machine learning. Using dynamic features of a GPU kernel as input, a machine learning model can be used to assist in the selection of thread configurations which will improve performance and minimize power consumption.

Chair/Author Details:

Michela Becchi, Manish Parashar, Dorian C. Arnold (Chair) - University of Missouri|Rutgers University|University of New Mexico|

Tiffany A. Connors - Texas State University

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