# Scalable3-BO: Big Data meets HPC - A scalable asynchronous parallel high-dimensional Bayesian optimization framework on supercomputers

arXiv: https://arxiv.org/abs/2108.05969 · HTML: https://mlsanity.com/paper/2108.05969/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 72 references that are in the map.
- First posted on arXiv 12 Aug 2021.

## Builds on (most cited first, 13 of 13)

- [Scalable Global Optimization via Local Bayesian Optimization](https://mlsanity.com/paper/1910.01739/) (2019)
- [When Gaussian Process Meets Big Data: A Review of Scalable GPs](https://mlsanity.com/paper/1807.01065/) (2018)
- [Bayesian Optimization in a Billion Dimensions via Random Embeddings](https://mlsanity.com/paper/1301.1942/) (2013)
- [Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization](https://mlsanity.com/paper/2001.11659/) (2020)
- [Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Exploration](https://mlsanity.com/paper/1304.5350/) (2013)
- [High Dimensional Bayesian Optimization Using Dropout](https://mlsanity.com/paper/1802.05400/) (2018)
- [Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloys](https://mlsanity.com/paper/2006.00139/) (2020)
- [Lecture Notes on Randomized Linear Algebra](https://mlsanity.com/paper/1608.04481/) (2016)
- [Asynchronous Parallel Bayesian Optimisation via Thompson Sampling](https://mlsanity.com/paper/1705.09236/) (2017)
- [Solving stochastic inverse problems for property-structure linkages using data-consistent inversion and machine learning](https://mlsanity.com/paper/2010.03603/) (2020)
- [Dimension Reduction via Gaussian Ridge Functions](https://mlsanity.com/paper/1802.00515/) (2018)
- [A Fully Bayesian Gradient-Free Supervised Dimension Reduction Method using Gaussian Processes](https://mlsanity.com/paper/2008.03534/) (2020)
- [aphBO-2GP-3B: A budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecture](https://mlsanity.com/paper/2003.09436/) (2020)

## Built on by (most cited first, 1 of 1)

- [Attention-Informed Surrogates for Navigating Power-Performance Trade-offs in HPC](https://mlsanity.com/paper/2601.15399/) (2026)

## Abstract (arXiv)

Bayesian optimization (BO) is a flexible and powerful framework that is suitable for computationally expensive simulation-based applications and guarantees statistical convergence to the global optimum. While remaining as one of the most popular optimization methods, its capability is hindered by the size of data, the dimensionality of the considered problem, and the nature of sequential optimization. These scalability issues are intertwined with each other and must be tackled simultaneously. In this work, we propose the Scalable3-BO framework, which employs sparse GP as the underlying surrogate model to scope with Big Data and is equipped with a random embedding to efficiently optimize high-dimensional problems with low effective dimensionality. The Scalable3-BO framework is further leveraged with asynchronous parallelization feature, which fully exploits the computational resource on HPC within a computational budget. As a result, the proposed Scalable3-BO framework is scalable in three independent perspectives: with respect to data size, dimensionality, and computational resource on HPC. The goal of this work is to push the frontiers of BO beyond its well-known scalability issues and minimize the wall-clock waiting time for optimizing high-dimensional computationally expensive applications. We demonstrate the capability of Scalable3-BO with 1 million data points, 10,000-dimensional problems, with 20 concurrent workers in an HPC environment.