EVeREST: An Effective and Versatile Runtime Energy Saving Tool for GPUs

Amid conflicting demands for ever-improving performance and maximizing energy savings, it is important to have a tool that automatically identifies opportunities to save power/energy at runtime without compromising performance. GPUs in particular present challenges due to (1) reduced savings available from memory bound applications, and (2) limited availability of low overhead performance counters. Thus, a successful tool must address these issues while still tackling the challenges of dynamic application characterization, versatility across processors from different vendors, and effectiveness at making the right power-performance tradeoffs for desired energy savings.

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Publication Details

Published
2025-02-28
DOI
https://doi.org/10.1145/3710848.3710875
Citations
1
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
2.27
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article

EVeREST: An Effective and Versatile Runtime Energy Saving Tool for GPUs

Anna Yue, Pen-Chung Yew, Sanyam Mehta
1 citations
Parallel Computing and Optimization Techniques
2.27
article

EVeREST: An Effective and Versatile Runtime Energy Saving Tool for GPUs

Anna Yue, Pen-Chung Yew, Sanyam Mehta
article en
1 citations

Abstract

Amid conflicting demands for ever-improving performance and maximizing energy savings, it is important to have a tool that automatically identifies opportunities to save power/energy at runtime without compromising performance. GPUs in particular present challenges due to (1) reduced savings available from memory bound applications, and (2) limited availability of low overhead performance counters. Thus, a successful tool must address these issues while still tackling the challenges of dynamic application characterization, versatility across processors from different vendors, and effectiveness at making the right power-performance tradeoffs for desired energy savings.

University of Minnesota (US), Hewlett-Packard (United States) (US)
Affordable and clean energy
Openalex Percentile: Top 18%
Parallel Computing and Optimization Techniques
2.27
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