HybridTuner: Tuning with Hybrid Derivative-Free Optimization Initialization Strategies-Reference-Cited by-同舟云学术

HybridTuner: Tuning with Hybrid Derivative-Free Optimization Initialization Strategies

Published:2021 Issue: Volume: Page:379-393
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
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Author:

Sauk Benjamin^ORCID,Sahinidis Nikolaos V.^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-92121-7_29

Reference45 articles.

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3. Ansel, J., et al.: OpenTuner: an extensible framework for program autotuning. In: Proceedings of the 23rd International Conference on Parallel Architectures and Compilation, pp. 303–316. Association for Computing Machinery, New York (2014)

4. Ashouri, A., Mariani, G., Palermo, G., Park, E., Cavazos, J., Silvano, C.: COBAYN: compiler autotuning framework using Bayesian networks. ACM Trans. Archit. Code Optim. (TACO) 13, 1–26 (2016)

5. Audet, C., Dang, C.-K., Orban, D.: Algorithmic parameter optimization of the DFO method with the OPAL framework. In: Suda, R., Naono, K., Teranishi, K., Cavazos, J. (eds.) Software Automatic Tuning, pp. 255–274. Springer, New York (2011). https://doi.org/10.1007/978-1-4419-6935-4_15

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