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Exploring Computational Sprinting in New Domains

Saravanan, Indrajeet
http://rave.ohiolink.edu/etdc/view?acc_num=osu1555586869706602

Abstract Details

2019, Master of Science, Ohio State University, Computer Science and Engineering.
The dawn of dark silicon and utilization wall are the main issues that current processors face. Moore's law is virtually dead due to the breakdown of Dennard scaling. An array of novel approaches have been proposed to tackle the above-mentioned issues and computational sprinting is the latest one to be advocated. Computational sprinting is a set of management techniques that selectively speed up the execution of cores for short intervals of time followed up idle periods to achieve improved performance. This is physically feasible due to the inherent thermal capacitance that absorbs the heat generated by a rise in operating frequency or voltage. In our paper, we explore multiple avenues on how and where computational sprinting can be used. Firstly, we apply the core scaling method to sprint web queries which eventually makes page loads faster. We observe a 5.86% and 12.6% decrease in average load time for average-case and best-case scenarios respectively. Likewise, the number of page loads increase by 12.12% (average-case) and 21.88% (best-case). Secondly, we explore the Intel Cache Allocation Technology Tool to enable sprinting for server workloads. Since toggling L3 cache capacity for workloads introduces interference and uncertain consequences, we study the impact of cache stealing from co-located workloads. With base and polluted cache state being 4 MB and 1 MB respectively, we observe an average increase of 41.37 seconds in the runtime of Jacobi workload for every 20% increase in interruption from other workloads. We propose a machine learning approach for future work. Finally, we study SLOs in practice to analyze the realities and myths surrounding their design and use. We learn that single-digit response goals are challenging, extreme percentiles for complex software is prohibited by black swans, and find the parameters of importance for evaluating infrastructure and complex cloud services.
Christopher Stewart (Advisor)
Radu Teodorescu (Committee Member)
63 p.
Computer Science
Intel Cache Allocation Technology; Service Level Objectives; Browsers; Computational Sprinting; Dark Silicon

Recommended Citations

Citations

  • Saravanan, I. (2019). Exploring Computational Sprinting in New Domains [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1555586869706602

    APA Style (7th edition)

  • Saravanan, Indrajeet. Exploring Computational Sprinting in New Domains. 2019. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1555586869706602.

    MLA Style (8th edition)

  • Saravanan, Indrajeet. "Exploring Computational Sprinting in New Domains." Master's thesis, Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1555586869706602

    Chicago Manual of Style (17th edition)

osu1555586869706602
288
© 2019, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.