The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Bayesian Optimization of HPC Systems for Energy Efficiency
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The authors in [33] utilized Bayesian optimization (BO) to optimise a graphics processing unit (GPU) cluster system for the Green500 list, a prominent energy-efficiency rating of supercomputers.”
From Energy efficiency in cloud computing data centers: a survey on software technologies (2023) · cites this paper as [33]
Citations
Cites
Storn, R., & Price, K. (1997) · Journal of Global Optimization · doi:10.1023/a:1008202821328 · Cited by 29,569
TensorFlow: A system for large-scale machine learningPreprint
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.5555/3026877.3026899 · Cited by 16,548
Practical Bayesian Optimization of Machine Learning Algorithms
Snoek, J., Larochelle, H., & Adams, R. P. (2012) · arXiv · doi:10.48550/arxiv.1206.2944 · Cited by 6,691
Large Sample Properties of Simulations Using Latin Hypercube Sampling
Stein, M. L. (1987) · Technometrics · doi:10.1080/00401706.1987.10488205 · Cited by 2,036
Large Sample Properties of Simulations Using Latin Hypercube Sampling
Stein, M. L. (1987) · Technometrics · doi:10.2307/1269769 · Cited by 1,320
A New Method of Locating the Maximum Point of an Arbitrary Multipeak Curve in the Presence of Noise
Kushner, H. J. (1964) · Journal of Basic Engineering · doi:10.1115/1.3653121 · Cited by 1,152
The LINPACK Benchmark: past, present and future
Dongarra, J. J., Luszczek, P., & Petitet, A. (2003) · Concurrency and Computation: Practice and Experience · doi:10.1002/cpe.728 · Cited by 813
Ansel, J., Kamil, S. A., Veeramachaneni, K. K., Ragan‐Kelley, J., Bosboom, J., O’Reilly, U.-M., & Amarasinghe, S. P. (2014) · International Conference on Parallel Architectures and Compilation Techniques (PACT) · doi:10.1145/2628071.2628092 · Cited by 531
Martínez-Cantín, R. (2014) · Zaguan (Universidad de Zaragoza) · doi:10.48550/arxiv.1405.7430 · Cited by 248
Slice sampling covariance hyperparameters of latent Gaussian modelsPreprint
Murray, I., & Adams, R. P. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1006.0868 · Cited by 163
Research works printed but not linked: 4
- Močkus, J., Tiesis, V., Zilinskas, A.: The application of bayesian methods for seeking the extremum. Towar. Glob. Optim. 2, 117–129 (1978)
- Rasmussen, C.E.: Gaussian Processes for Machine Learning (2006) · DOI · Paper page
- Srinivas, N., Krause, A., Kakade, S.M., Seeger, M.: Gaussian process optimization in the bandit setting: no regret and experimental design. arXiv preprint arXiv:0912.3995 (2009) · arXiv
- Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25, pp. 2951–2959. Curran Associates, Inc. (2012)
Cited by
Sorted by citations
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
“The authors in [33] utilized Bayesian optimization (BO) to optimise a graphics processing unit (GPU) cluster system for the Green500 list, a prominent energy-efficiency rating of supercomputers.”
Method
Metadata
- Authors
- Miyazaki, T., Sato, I., & Shimizu, N.
- Affiliations
- Yahoo Japan Corp. (Japan) (JP)
- The University of Tokyo (JP)
- Corresponding author
- Miyazaki, Takashi
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 44-62
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2805864713
- Cited by
- 27
- References
- 12
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 7 · 2023: 2 · 2022: 3 · 2021: 3 · 2020: 4 · 2019: 2
- FWCI
- 1.93
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- 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
- Bayesian optimization, energy efficiency, Green500, supercomputers, data centers, power-performance tradeoff, benchmarking
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Bayesian Optimization of HPC Systems for Energy Efficiency. https://app.cochat.ai/papers/10.1007/978-3-319-92040-5_3
- The paper
- Miyazaki, T., Sato, I., & Shimizu, N. (2018). Bayesian Optimization of HPC Systems for Energy Efficiency. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-319-92040-5_3
