Evaluating energy and power profiling techniques for HPC workloads

Grant, Ryan E.; Laros, James H.; Levenhagen, Michael J.; Olivier, Stephen Lecler; Pedretti, Kevin; Ward, Lee; Younge, Andrew J

doi:10.1109/igcc.2017.8323587

Cited by 4 publications

(1 citation statement)

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“…Table 1 presents the specification of both hosts. To measure the power consumption of a server, two approaches are found in literature: software-based metering (i.e., in-band) and hardware-based metering (i.e., out-of-band) [2,32]. Software-based meters estimate the power consumption of some server components by consulting internal counters [33].…”

Section: Multicore Hosts and Power Monitoring Setupmentioning

confidence: 99%

Empirical power consumption characterization and energy aware scheduling in data centers

Muraña

2021

CLEIej

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Energy-efficient management is key to reduce operational cost and environmental contamination in modern data centers. Energy management and renewable energy utilization are strategies to optimize energy consumption in high-performance computing. In any case, understanding the power consumption behavior of physical servers in datacenter is fundamental to implement energy-aware policies effectively. These policies should deal with possible performance degradation of applications to ensure quality of service. This manuscript presents an empirical evaluation of power consumption for scientific computing applications in multicore systems. Three types of applications are studied, in single and combined executions on Intel and AMD servers, for evaluating the overall power consumption of each application. The main results indicate that power consumption behavior has a strong dependency with the type of application. Additional performance analysis shows that the best load of the server regarding energy efficiency depends on the type of the applications, with efficiency decreasing in heavily loaded situations. These results allow formulating models to characterize applications according to power consumption, efficiency, and resource sharing, which provide useful information for resource management and scheduling policies. Several scheduling strategies are evaluated using the proposed energy model over realistic scientific computing workloads. Results confirm that strategies that maximize host utilization provide the best energy efficiency.

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Section: Multicore Hosts and Power Monitoring Setupmentioning

confidence: 99%