DocumentCode :
3033043
Title :
MuMMI: Multiple Metrics Modeling Infrastructure
Author :
Xingfu Wu ; Lively, Charles ; Taylor, Valerie ; Hung-Ching Chang ; Chun-Yi Su ; Cameron, Katherine ; Moore, Steven ; Terpstra, Dan ; Weaver, Vince
Author_Institution :
Dept. of Comput. Sci. & Eng., Texas A&M Univ., College Station, TX, USA
fYear :
2013
fDate :
1-3 July 2013
Firstpage :
289
Lastpage :
295
Abstract :
The MuMMI (Multiple Metrics Modeling Infrastructure) project is an infrastructure that facilitates systematic measurement, modeling, and prediction of performance, power consumption and performance-power tradeoffs for parallel systems. In this paper, we present the MuMMI framework, which consists of an Instrument or, Databases and Analyzer. The MuMMI instrument or provides for 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. The MuMMI Analyzer entails performance and power modeling and performance-power tradeoff and optimizations. As part of the MuMMI project, we mainly focus on discussing the design and development of a MuMMI Instrument or to provide automatic performance and power data collection and storage with low overhead on multicore systems in detail, then utilize the MuMMI Instrument or to collect performance and power data for a hybrid MPI/OpenMP earthquake application to discuss application performance-power trade-off and optimizations. Our experimental results show that we reduce up to 8.5% the application execution time and lower up to 18.35% the energy consumption by applying Dynamic Voltage and Frequency Scaling (DVFS), Dynamic Concurrency Throttling (DCT) and loop optimizations.
Keywords :
message passing; multiprocessing systems; parallel processing; power aware computing; power consumption; DCT; DVFS; MuMMI analyzer; MuMMI databases store performance; MuMMI instrument; MuMMI project; application execution time; dynamic concurrency throttling; dynamic voltage and frequency scaling; energy consumption; hardware performance counters data; hybrid MPI/OpenMP earthquake application; loop optimizations; multicore systems; multiple metrics modeling infrastructure; parallel systems; performance prediction; performance-power tradeoffs; power consumption; power data collection; power data storage; power modeling; systematic measurement; systematic modeling; Data collection; Databases; Hardware; Instruments; Kernel; Optimization; Radiation detectors; Dynamic Con- currency Throttling (DCT); Dynamic Voltage and Frequency Scaling (DVFS); Performance measurement; Performance-Power optimization; Performance-power tradeoff; Power measurement;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), 2013 14th ACIS International Conference on
Conference_Location :
Honolulu, HI
Type :
conf
DOI :
10.1109/SNPD.2013.73
Filename :
6598479
Link To Document :
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