基于注意力的替代方案用于在高性能计算中导航性能-功耗权衡
Attention-Informed Surrogates for Navigating Power-Performance Trade-offs in HPC
AI总结：
本文提出了一种基于注意力机制的替代方案辅助多目标贝叶斯优化框架，用于优化HPC调度中的性能与功耗权衡。
摘要：
高性能计算（HPC）调度器必须在用户性能与整个设施的资源约束之间取得平衡。这项任务归结为为给定任务选择最优的节点数量。我们提出了一种替代方案辅助的多目标贝叶斯优化（MOBO）框架，以自动化这个复杂的决策。我们的核心假设是，通过基于注意力的嵌入式作业 telemetry 的替代模型可以比标准回归技术更有效地捕捉性能动态。我们将其与一种智能样本获取策略相结合，以确保该方法的数据效率。在两个生产HPC数据集上，我们的嵌入式方法一致地识别出比基线更高的质量运行时间-功耗权衡的帕累托前沿。此外，我们的智能数据采样策略大大降低了训练成本，同时提高了结果的稳定性。据我们所知，这是首次成功将基于嵌入的替代方案应用于MOBO框架中的HPC调度问题，共同优化性能和功耗。
英文摘要：
High-Performance Computing (HPC) schedulers must balance user performance with facility-wide resource constraints. The task boils down to selecting the optimal number of nodes for a given job. We present a surrogate-assisted multi-objective Bayesian optimization (MOBO) framework to automate this complex decision. Our core hypothesis is that surrogate models informed by attention-based embeddings of job telemetry can capture performance dynamics more effectively than standard regression techniques. We pair this with an intelligent sample acquisition strategy to ensure the approach is data-efficient. On two production HPC datasets, our embedding-informed method consistently identified higher-quality Pareto fronts of runtime-power trade-offs compared to baselines. Furthermore, our intelligent data sampling strategy drastically reduced training costs while improving the stability of the results. To our knowledge, this is the first work to successfully apply embedding-informed surrogates in a MOBO framework to the HPC scheduling problem, jointly optimizing for performance and power on production workloads.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
