A comparative study of black‐box optimization heuristics for online tuning of high performance computing I/O accelerators-Reference-Cited by-同舟云学术

A comparative study of black‐box optimization heuristics for online tuning of high performance computing I/O accelerators

Published:2021-03-25 Issue:16 Volume:33 Page:
ISSN:1532-0626
Container-title:Concurrency and Computation: Practice and Experience
language:en
Short-container-title:Concurrency Computat Pract Exper

Author:

Robert Sophie¹²,Zertal Soraya²^ORCID,Vaumourin Grégory¹,Couvée Philippe¹

Affiliation:

1. Data Management Division Atos Échirolles France

2. Li‐PaRAD University of Versailles Guyancourt France

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.6274

Reference37 articles.

1. NabeMVincentL GoretG. Self‐optimization strategy for io accelerator parameterization. In International Conference on High Performance Computing pages 157–170 2018.

2. BehzadB LuuHVT HuchetteJ et al. Taming parallel i/o complexity with auto‐tuning. Paper presented at: Proceedings of the International Conference on High Performance Computing Networking Storage and Analysis SC '13 Denver‐CO;2013:68:1‐68:12.

3. Autotuning in High-Performance Computing Applications

4. S.Robert S.Zertal andG.Goret. Auto‐tuning of io accelerators using black‐box optimization. Paper presented at: Proceedings of the International Conference on High Performance Computing & Simulation (HPCS) Dublin Ireland;2019.

5. Handbook of Metaheuristics

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