MuMMI: Multiple Metrics Modeling Infrastructure

Xingfu Wu, Valerie Taylor (), Charles Lively, Hung-Ching Chang, Bo Li, Kirk Cameron, Dan Terpstra and Shirley Moore
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Xingfu Wu: Texas A&M University, Department of Computer Science & Engineering
Valerie Taylor: Texas A&M University, Department of Computer Science & Engineering
Charles Lively: Texas A&M University, Department of Computer Science & Engineering
Hung-Ching Chang: Virginia Tech, Department of Computer Science
Bo Li: Virginia Tech, Department of Computer Science
Kirk Cameron: Virginia Tech, Department of Computer Science
Dan Terpstra: University of Tennessee, Innovative Computing Lab
Shirley Moore: University of Texas at El Paso, Department of Computer Science

Chapter Chapter 5 in Tools for High Performance Computing 2013, 2014, pp 53-65 from Springer

Abstract: Abstract MuMMI (Multiple Metrics Modeling Infrastructure) is an infrastructure that facilitates systematic measurement, modeling, and prediction of performance and power consumption, and performance-power tradeoffs and optimization for parallel systems. MuMMI builds upon three existing frameworks: Prophesy for performance modeling and prediction of parallel applications, PAPI for hardware performance counter monitoring, and PowerPack for power measurement and profiling. In this paper, we present the MuMMI framework, which consists of an Instrumentor, Databases and Analyzer. The MuMMI Instrumentor provides automatic performance and power data collection and storage with low overhead. The MuMMI Databases store performance, power and energy consumption and hardware performance counters’ data with different CPU frequency settings for modeling and comparison. The MuMMI Analyzer entails performance and power modeling and performance-power tradeoff and optimizations. For case studies, we apply MuMMI to a parallel earthquake simulation to illustrate building performance and power models of the application and optimizing its performance and power for energy efficiency.

Keywords: Power Consumption; Average Error Rate; Application Execution; Performance Counter; OpenMP Thread (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-08144-1_5

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DOI: 10.1007/978-3-319-08144-1_5

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