# HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach

arXiv: https://arxiv.org/abs/2501.01057 · HTML: https://mlsanity.com/paper/2501.01057/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 50 references that are in the map.
- First posted on arXiv 2 Jan 2025.

## Builds on (most cited first, 10 of 10)

- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Non-stochastic Best Arm Identification and Hyperparameter Optimization](https://mlsanity.com/paper/1502.07943/) (2015)
- [Input Warping for Bayesian Optimization of Non-stationary Functions](https://mlsanity.com/paper/1402.0929/) (2014)
- [Simple regret for infinitely many armed bandits](https://mlsanity.com/paper/1505.04627/) (2015)
- [BestConfig: Tapping the Performance Potential of Systems via Automatic Configuration Tuning](https://mlsanity.com/paper/1710.03439/) (2017)
- [Multitask and Transfer Learning for Autotuning Exascale Applications](https://mlsanity.com/paper/1908.05792/) (2019)
- [LOCAT: Low-Overhead Online Configuration Auto-Tuning of Spark SQL Applications](https://mlsanity.com/paper/2203.14889/) (2022)
- [ConEx: Efficient Exploration of Big-Data System Configurations for Better Performance](https://mlsanity.com/paper/1910.09644/) (2019)
- [Automated Reasoning and Detection of Specious Configuration in Large Systems with Symbolic Execution](https://mlsanity.com/paper/2010.06356/) (2020)

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

- [Enhanced Intrusion Detection in IIoT Networks: A Lightweight Approach with Autoencoder-Based Feature Learning](https://mlsanity.com/paper/2501.15266/) (2025)

## Abstract (arXiv)

The growing necessity for enhanced processing capabilities in edge devices with limited resources has led us to develop effective methods for improving high-performance computing (HPC) applications. In this paper, we introduce LASP (Lightweight Autotuning of Scientific Application Parameters), a novel strategy designed to address the parameter search space challenge in edge devices. Our strategy employs a multi-armed bandit (MAB) technique focused on online exploration and exploitation. Notably, LASP takes a dynamic approach, adapting seamlessly to changing environments. We tested LASP with four HPC applications: Lulesh, Kripke, Clomp, and Hypre. Its lightweight nature makes it particularly well-suited for resource-constrained edge devices. By employing the MAB framework to efficiently navigate the search space, we achieved significant performance improvements while adhering to the stringent computational limits of edge devices. Our experimental results demonstrate the effectiveness of LASP in optimizing parameter search on edge devices.