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Controlling the Runtime Overhead of Python Monitoring with Selective Instrumentation.

, , and . ProTools@SC, page 17-25. IEEE, (2021)

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Power measurement techniques for energy-efficient computing: reconciling scalability, resolution, and accuracy., , , , , , and . SICS Softw.-Intensive Cyber Phys. Syst., 34 (1): 45-52 (2019)The shift from processor power consumption to performance variations: fundamental implications at scale., , , , , and . Comput. Sci. Res. Dev., 31 (4): 197-205 (2016)Wake-up latencies for processor idle states on current x86 processors., , and . Comput. Sci. Res. Dev., 30 (2): 219-227 (2015)Energy Efficiency Aspects of the AMD Zen 2 Architecture., , , , , and . CLUSTER, page 562-571. IEEE, (2021)A New Approach for Automated Feature Selection., , and . IEEE BigData, page 4915-4920. IEEE, (2018)MetricQ: A Scalable Infrastructure for Processing High-Resolution Time Series Data., , , , and . DAAC@SC, page 7-12. IEEE, (2019)Investigating the Cause and Effect of an AMD Zen Energy Management Anomaly., , , , and . ICPE (Companion), page 103-106. ACM, (2021)FIRESTARTER 2: Dynamic Code Generation for Processor Stress Tests., , , and . CLUSTER, page 582-590. IEEE, (2021)Memory Performance at Reduced CPU Clock Speeds: An Analysis of Current x86\_64 Processors, , and . Proceedings of the 2012 USENIX conference on Power-Aware Computing and Systems, (October 2012)Integrating performance analysis and energy efficiency optimizations in a unified environment., and . Comput. Sci. Res. Dev., 29 (3-4): 231-239 (2014)