Node variability in large-scale power measurements

Author:

Scogland Thomas1,Azose Jonathan2,Rohr David3,Rivoire Suzanne4,Bates Natalie5,Hackenberg Daniel6

Affiliation:

1. Lawrence Livermore National Laboratory

2. University of Washington, Seattle, WA

3. Goethe University, Frankfurt

4. Sonoma State University, Rohnert Park, CA

5. Energy Efficient HPC Working Group

6. TU Dresden

Publisher

ACM

Reference22 articles.

1. Rodinia: A benchmark suite for heterogeneous computing

2. Energy Efficient High Performance Computing Working Group (EE-HPC-WG). Energy efficient high performance computing power measurement methodology version 1.2rc2. http://www.green500.org/sites/default/files/eehpcwg/EEHPCWG_PowerMeasurementMethodology.pdf. Energy Efficient High Performance Computing Working Group (EE-HPC-WG). Energy efficient high performance computing power measurement methodology version 1.2rc2. http://www.green500.org/sites/default/files/eehpcwg/EEHPCWG_PowerMeasurementMethodology.pdf.

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1. Characterizing Variability in Heterogeneous Edge Systems: A Methodology & Case Study;2022 IEEE/ACM 7th Symposium on Edge Computing (SEC);2022-12

2. Not All GPUs Are Created Equal: Characterizing Variability in Large-Scale, Accelerator-Rich Systems;SC22: International Conference for High Performance Computing, Networking, Storage and Analysis;2022-11

3. Prediction of job characteristics for intelligent resource allocation in HPC systems: a survey and future directions;Frontiers of Computer Science;2022-05-23

4. Job Characteristics on Large-Scale Systems: Long-Term Analysis, Quantification, and Implications;SC20: International Conference for High Performance Computing, Networking, Storage and Analysis;2020-11

5. What does Power Consumption Behavior of HPC Jobs Reveal? : Demystifying, Quantifying, and Predicting Power Consumption Characteristics;2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS);2020-05

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