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Cited by 57Parallel Computing and Optimization TechniquesEnergy Efficiency in ComputingCloud Computing and Resource Management

The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.

Energy-Aware High-Performance Computing: Survey of State-of-the-Art Tools, Techniques, and Environments

Open access
Czarnul, Pawel, Proficz, Jerzy, Krzywaniak, Adam·2019·Scientific Programming
DOI 10.1155/2019/8348791
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Abstract

The paper presents state of the art of energy-aware high-performance computing (HPC), in particular identification and classification of approaches by system and device types, optimization metrics, and energy/power control methods. System types include single device, clusters, grids, and clouds while considered device types include CPUs, GPUs, multiprocessor, and hybrid systems. Optimization goals include various combinations of metrics such as execution time, energy consumption, and temperature with consideration of imposed power limits. Control methods include scheduling, DVFS/DFS/DCT, power capping with programmatic APIs such as Intel RAPL, NVIDIA NVML, as well as application optimizations, and hybrid methods. We discuss tools and APIs for energy/power management as well as tools and environments for prediction and/or simulation of energy/power consumption in modern HPC systems. Finally, programming examples, i.e., applications and benchmarks used in particular works are discussed. Based on our review, we identified a set of open areas and important up-to-date problems concerning methods and tools for modern HPC systems allowing energy-aware processing.

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Citations

Cites

Research works printed but not linked: 5

  1. 2013, Transition of HPC towards exascale computing, 24
  2. 2013, International Journal of Computer Science and Mobile Computing, 2, 46
  3. 2010, Collecting performance data with PAPI-C, 157
  4. 2014, Optimization of execution time under power consumption constraints in a heterogeneous parallel system with gpus and cpus, 66
  5. 2015, Energy-aware gpu-raid scheduling for reducing energy consumption in cloud storage systems, 705

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Metadata

Authors
Czarnul, P., Proficz, J., & Krzywaniak, A.
Affiliations
  • Dept. of Computer Architecture, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdańsk, Poland
  • Gdańsk University of Technology (PL)
ORCID
Venue
Scientific Programming
Publisher
Wiley
Published
2019-04-24
Volume
2019
Pages
1-19
ISSN
  • 1058-9244 (ISSN-L)
  • 1875-919X
DOI
10.1155/2019/8348791
OpenAlex
W2941845317
Cited by
57
References
43
Citations by year
2026: 3 · 2025: 11 · 2024: 8 · 2023: 9 · 2022: 8 · 2021: 9 · 2020: 8 · 2019: 1
FWCI
6.59
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Programming (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
  • Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
  • Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
Keywords
energy-aware computing, high-performance computing, energy management, power management, dynamic voltage and frequency scaling, dynamic thermal management, power capping, load forecasting, energy consumption simulation, HPC clusters, GPU computing, scheduling, temperature control

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