HPC Storage Service Autotuning Using Variational- Autoencoder -Guided Asynchronous Bayesian Optimization

Distributed data storage services tailored to specific applications have grown popular in the high-performance computing (HPC) community as a way to address I/O and storage challenges. These services offer a variety of specific interfaces, semantics, and data representations. They also expose many t...

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Bibliographic Details
Published in:Proceedings / IEEE International Conference on Cluster Computing pp. 381 - 393
Main Authors: Dorier, Matthieu, Egele, Romain, Balaprakash, Prasanna, Koo, Jaehoon, Madireddy, Sandeep, Ramesh, Srinivasan, Malony, Allen D., Ross, Rob
Format: Conference Proceeding
Language:English
Published: IEEE 01.09.2022
Subjects:
Autotuning
Bayes methods
Bayesian Optimization
DeepHyper
HPC
I/O
Mochi
Probability distribution
Resource management
Semantics
Storage
Supercomputers
Systematics
Transfer learning
ISSN:2168-9253
Online Access:Get full text
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Summary:Distributed data storage services tailored to specific applications have grown popular in the high-performance computing (HPC) community as a way to address I/O and storage challenges. These services offer a variety of specific interfaces, semantics, and data representations. They also expose many tuning parameters, making it difficult for their users to find the best configuration for a given workload and platform. To address this issue, we develop a novel variational-autoencoder-guided asynchronous Bayesian optimization method to tune HPC storage service parameters. Our approach uses transfer learning to leverage prior tuning results and use a dynamically updated surrogate model to explore the large parameter search space in a systematic way. We implement our approach within the DeepHyper open-source framework, and apply it to the autotuning of a high-energy physics workflow on Argonne's Theta supercomputer. We show that our transfer-learning approach enables a more than 40 x search speedup over random search, compared with a 2.5 x to 10 x speedup when not using transfer learning. Additionally, we show that our approach is on par with state-of-the-art autotuning frameworks in speed and outperforms them in resource utilization and parallelization capabilities.
ISSN:2168-9253
DOI:10.1109/CLUSTER51413.2022.00049