Energy Consumption Analysis of HPC Applications Using NoSQL Database Feature of EnergyAnalyzer

Benedict, Shajulin; Rejitha, R. S.; Bright, C. Bency

doi:10.1007/978-3-319-19848-4_7

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…EnergyAnalyzer consists of three major entities [17,18], namely, SSTranslator, Semantic-based Analysis Agents, and Monitoring Manager, as shown in Figure 1. These entities are responsible for accomplishing the following activities:…”

Section: Energyanalyzer Entitiesmentioning

confidence: 99%

Threshold acceptance algorithm based energy tuning of scientific applications using EnergyAnalyzer

Benedict

2014

Proceedings of the 7th India Software Engineering Conference

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Energy tuning of scientific applications is emerging as an important research agenda among HPC application developer and performance analysis tool developer communities as it is a critical and ponderous task to deal with, especially when exa-scale computing machines were concerned. The energy tuning research has deepened its thrust among researchers due to the necessity of addressing an energy consumption issue of applications which is considered as one of the global challenge. Although a few efforts were carried out in the direction of automatic energy tuning of scientific applications, the working researchers encounter challenges, such as, finding an optimal solution within a short time frame among a wide set of optimization search space, generating fully automatic optimized versions of scientific applications, considering the heterogeneous nature of future generation compute machine architectures, and so forth. This paper discusses about an online based energy consumption tuning mechanism of EnergyAnalyzer tool using Threshold Acceptance (TA) algorithm. In addition, the paper demonstrates the speedy process of finding a near-optimal optimized code versions of a scientific application among a huge set of optimization search space using EnergyAnalyzer. EnergyAnalyzer is an online-based energy consumption analysis tool which is under development at the HPCCLoud Research laboratory of our premise. The proposed algorithm and the energy tuning mechanism could be of an interest to energy conscious scientific application developers and tool developers.

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Section: Energyanalyzer Entitiesmentioning

confidence: 99%

Threshold acceptance algorithm based energy tuning of scientific applications using EnergyAnalyzer

Benedict

2014

Proceedings of the 7th India Software Engineering Conference

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“…Another approach is to utilise online performance analysis tools for identifying and manually rectifying the performance concerns of applications (Benedict et al, 2015;Benedict and Gerndt, 2012;Geimer et al, 2012). There exist several online performance analysis tools such as, periscope, scalasca, TAU, and so forth.…”

Section: Run Time Oriented Pi Approachmentioning

confidence: 99%

“…There exist several online performance analysis tools such as, periscope, scalasca, TAU, and so forth. And, the energy specific analysis of applications could be accomplished using a few tools such as, powertop, score-E, energy analyser (Benedict et al, 2015) and so forth (Hahnel et al, 2015).…”

Section: Run Time Oriented Pi Approachmentioning

confidence: 99%

Performance Improvement Options of Scientific Applications on XeonPhi KNL Architectures

Benedict

2018

IJKEDM

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Intel's recent manycore processor KNights Landing (KNL) promises high performance for scientific applications. Careful tuning for the complex chip architecture is required to efficiently exploit the chip's hardware resources. This paper describes performance improvement techniques and demonstrates their effectiveness for scientific applications. Experiments were conducted with some of the National Aeronautics and Space Administration (NASA's) advanced supercomputing (NAS) parallel benchmarks, and the effectiveness of: 1) advanced vector extensions (AVX-512) vectorisation support; 2) manycore threading support; 3) the utilisation of thread affinities for different KNL modes, was analysed.

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Performance Issues and Monitoring Mechanisms for Serverless IoT Applications—An Exploratory Study

Benedict

2021

Smart Computing Techniques and Applications

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Energy Consumption Analysis of HPC Applications Using NoSQL Database Feature of EnergyAnalyzer

Cited by 4 publications

References 18 publications

Threshold acceptance algorithm based energy tuning of scientific applications using EnergyAnalyzer

Threshold acceptance algorithm based energy tuning of scientific applications using EnergyAnalyzer

Performance Improvement Options of Scientific Applications on XeonPhi KNL Architectures

Performance Issues and Monitoring Mechanisms for Serverless IoT Applications—An Exploratory Study

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