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

arXiv: https://arxiv.org/abs/2210.00798 · HTML: https://mlsanity.com/paper/2210.00798/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 52 references that are in the map.
- First posted on arXiv 3 Oct 2022.

## Builds on (most cited first, 8 of 8)

- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning](https://mlsanity.com/paper/1909.12552/) (2019)
- [Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior](https://mlsanity.com/paper/1811.09558/) (2018)
- [Practical Transfer Learning for Bayesian Optimization](https://mlsanity.com/paper/1802.02219/) (2018)
- [mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions](https://mlsanity.com/paper/1703.03373/) (2017)
- [Transfer Learning for Improving Model Predictions in Highly Configurable Software](https://mlsanity.com/paper/1704.00234/) (2017)
- [Multitask and Transfer Learning for Autotuning Exascale Applications](https://mlsanity.com/paper/1908.05792/) (2019)

## Built on by (most cited first, 4 of 4)

- [BaCO: A Fast and Portable Bayesian Compiler Optimization Framework](https://mlsanity.com/paper/2212.11142/) (2022)
- [Performance Tuning for GPU-Embedded Systems: Machine-Learning-based and Analytical Model-driven Tuning Methodologies](https://mlsanity.com/paper/2310.16214/) (2023)
- [Cost-Effective Methodology for Complex Tuning Searches in HPC: Navigating Interdependencies and Dimensionality](https://mlsanity.com/paper/2403.08131/) (2024)
- [Attention-Informed Surrogates for Navigating Power-Performance Trade-offs in HPC](https://mlsanity.com/paper/2601.15399/) (2026)

## Abstract (arXiv)

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× search speedup over random search, compared with a 2.5× to 10× 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.