Using Application-Level Thread Progress Information to Manage Power and Performance
Name
2017.threadbeats.iccd.pdf
Description
Accepted version
Size
272.18 KB
Format
Adobe PDF
Checksum (MD5)
2d570f90f987124d62fb4cfb06083259
Author(s)
Neuman, Sabrina M
Miller, Jason E
Sanchez, Daniel
Devadas, Srinivas
Date Issued
2017
Journal
Proceedings - 35th IEEE International Conference on Computer Design, ICCD 2017
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Neuman, Sabrina M, Miller, Jason E, Sanchez, Daniel and Devadas, Srinivas. 2017. "Using Application-Level Thread Progress Information to Manage Power and Performance." Proceedings - 35th IEEE International Conference on Computer Design, ICCD 2017.
Version
Author's final manuscript
Abstract
© 2017 IEEE. Power and thermal limitations make it impossible to run all cores on a multicore system at their maximum frequency. Therefore, modern systems require careful power management. These systems must manage complex tradeoffs between energy, power, and frequency, choosing which cores to accelerate to achieve good performance while maintaining energy efficiency or operating under a power budget. Navigating these tradeoffs is especially hard with multi-threaded applications, where performance depends on the relative progress of parallel worker threads between synchronization points. Prior work on chip-level power management for multi-threaded applications has largely relied on indirect heuristics and metrics calculated from low-level performance counters to estimate each thread's progress. However, these indirect metrics are often inaccurate. Instead, we propose to gather progress information directly from software itself. We present ThreadBeats, a simple application-level annotation framework that directly and accurately conveys thread progress information to hardware. We design DVFS controllers that exploit ThreadBeats information for two purposes: (i) improving performance by equalizing thread progress and (ii) minimizing runtime under a power budget constraint. These controllers reduce wait time at barriers by 77% on average and improve energy-delay product under a power budget by 23% over prior work.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/ICCD.2017.87
