A methodology for comparing optimization algorithms for auto-tuning-Reference-Cited by-同舟云学术

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A methodology for comparing optimization algorithms for auto-tuning

Published:2024-10 Issue: Volume:159 Page:489-504
ISSN:0167-739X
Container-title:Future Generation Computer Systems
language:en
Short-container-title:Future Generation Computer Systems

Author:

Willemsen Floris-Jan^ORCID,Schoonhoven Richard^ORCID,Filipovič Jiří^ORCID,Tørring Jacob O.^ORCID,van Nieuwpoort Rob^ORCID,van Werkhoven Ben^ORCID

Publisher

Elsevier BV

Reference57 articles.

1. The landscape of exascale research: A data-driven literature analysis;Heldens;ACM Comput. Surv.,2021

2. Optimization techniques for GPU programming;Hijma;ACM Comput. Surv.,2022

3. A. Sclocco, H.E. Bal, J. Hessels, J. van Leeuwen, R.V. van Nieuwpoort, Auto-tuning dedispersion for many-core accelerators, in: 2014 IEEE 28th International Parallel and Distributed Processing Symposium, 2014, pp. 952–961.

4. Benchmarking optimization algorithms for auto-tuning GPU kernels;Schoonhoven;IEEE Trans. Evol. Computat.,2022

5. Kernel Tuner: A search-optimizing GPU code auto-tuner;Van Werkhoven;Future Gener. Comput. Syst.,2019

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. FAIR Sharing of Data in Autotuning Research (Vision Paper);Companion of the 15th ACM/SPEC International Conference on Performance Engineering;2024-05-07

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