用强化学习优化边缘设备上科学计算的参数，省资源还高效。
HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach
- 用多臂赌博机动态搜索最优参数配置
- 在4个HPC应用上实现显著性能提升
- 适合资源受限的边缘计算场景
随着边缘设备对高性能计算能力的需求增加，但其资源有限，我们提出LASP（轻量级科学应用参数自动调优）策略，以应对边缘设备中的参数搜索空间挑战。该方法采用多臂赌博机（MAB）技术，聚焦在线探索与利用。LASP具有动态适应性，可无缝应对环境变化。我们在四个HPC应用（Lulesh、Kripke、Clomp、Hypre）上测试了该方法，其轻量化设计特别适用于资源受限的边缘设备。通过在搜索空间中高效导航，实现了显著的性能提升，同时满足边缘设备严格的计算限制。实验结果验证了LASP在边缘设备上优化参数搜索的有效性。
原文摘要 · Abstract (English)
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.
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