Work

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

Year: 2024

Type: conference-paper

Abstract: 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.

Source: IEEE International Conference on High Performance Computing, Data, and Analytics

DOI: 10.1109/hipc62374.2024.00011

Authors: Abrar Hossain, Abdel-Hameed A. Badawy, Mohammad A. Islam, Tapasya Patki, Kishwar Ahmed

Institutions: University of Toledo, New Mexico State University, The University of Texas at Arlington, Lawrence Livermore National Laboratory

Language: English

Topic: Parallel Computing and Optimization Techniques

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, U.S. Department of Energy, Division of Electrical, Communications and Cyber Systems, Lawrence Livermore National Laboratory