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 accessAbstract
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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“New cluster systems are planned to be no more than 20 MW in power [30], with the goal of attaining exascale performance as quickly as possible.”
From Energy efficiency in cloud computing data centers: a survey on software technologies (2023) · cites this paper as [30]
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Cites
Calheiros, R. N., Ranjan, R. K., Beloglazov, A., Rose, C. A. F. D., & Buyya, R. (2010) · Software Practice and Experience · doi:10.1002/spe.995 · Cited by 4,961
Beloglazov, A., Abawajy, J., & Buyya, R. (2011) · Future Generation Computer Systems · doi:10.1016/j.future.2011.04.017 · Cited by 2,865
A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems
Beloglazov, A., Buyya, R., Lee, Y. C., & Zomaya, A. (2011) · Advances in Computers · doi:10.1016/b978-0-12-385512-1.00003-7 · Cited by 777
GreenCloud: a packet-level simulator of energy-aware cloud computing data centers
Kliazovich, D., Bouvry, P., & Khan, S. U. (2010) · The Journal of Supercomputing · doi:10.1007/s11227-010-0504-1 · Cited by 556
Versatile, scalable, and accurate simulation of distributed applications and platforms
Casanova, H., Giersch, A., Legrand, A., Quinson, M., & Suter, F. (2014) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2014.06.008 · Cited by 423
A survey on techniques for improving the energy efficiency of large-scale distributed systems
Orgerie, A., de Assunção, M. D., & Lefèvre, L. (2014) · ACM Computing Surveys · doi:10.1145/2532637 · Cited by 362
Scheduling of scientific workflows in the ASKALON grid environment
Wieczorek, M., Prodan, R., & Fahringer, T. (2005) · ACM SIGMOD Record · doi:10.1145/1084805.1084816 · Cited by 324
Mastelić, T., Oleksiak, A., Claußen, H., Brandić, I., Pierson, J., & Vasilakos, A. V. (2014) · ACM Computing Surveys · doi:10.1145/2656204 · Cited by 301
RAPL in Action: Experiences in Using RAPL for Power Measurements
Khan, K. N., Hirki, M., Niemi, T., Nurminen, J. K., & Ou, Z. (2018) · ACM Transactions on Modeling and Performance Evaluation of Computing Systems · doi:10.1145/3177754 · Cited by 298
Measuring energy consumption for short code paths using RAPL
Hähnel, M., Döbel, B., Völp, M., et al. (2012) · ACM SIGMETRICS Performance Evaluation Review · doi:10.1145/2425248.2425252 · Cited by 273
Research works printed but not linked: 5
- 2013, Transition of HPC towards exascale computing, 24
- 2013, International Journal of Computer Science and Mobile Computing, 2, 46
- 2010, Collecting performance data with PAPI-C, 157
- 2014, Optimization of execution time under power consumption constraints in a heterogeneous parallel system with gpus and cpus, 66
- 2015, Energy-aware gpu-raid scheduling for reducing energy consumption in cloud storage systems, 705
Cited by
Energy efficiency in cloud computing data centers: a survey on software technologies
Katal, A., Dahiya, S., & Choudhury, T. (2023) · Cluster Computing · doi:10.1007/s10586-022-03713-0 · Cited by 516
“New cluster systems are planned to be no more than 20 MW in power [30], with the goal of attaining exascale performance as quickly as possible.”
Background
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)
- Venue
- Scientific Programming
- Publisher
- Wiley
- Published
- 2019-04-24
- Volume
- 2019
- Pages
- 1-19
- ISSN
- 1058-9244 (ISSN-L)
- 1875-919X
- 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
- hybrid
- doi.org
- 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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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
