HPC systems expose many configuration parameters that jointly drive competing objectives. Existing tools such as autotuners recommend good configurations but do not identify minimal changes for a near-miss configuration to meet a performance objective, and they often ignore domain-specific constraints. To address this gap, we introduce COMPASS -- a modular, programmable engine that uses operational traces to generate HPC configuration recommendations and guide tuning decisions. This paper: (1) formalizes configuration questions into query patterns; (2) develops an interactive decision-making engine that formulates these queries as Machine Learning (ML) tasks; (3) quantifies the trustworthiness of its recommendations by providing evidence and quantifying uncertainty, and -- when confidence is low -- provides guidance on which configurations to run next. We validate COMPASS using analytical ground truth, reconstruction accuracy, reproduction of published findings, and when possible, running on real hardware. When integrated with an open-source HPC scheduling simulator, COMPASS cuts average job turnaround time by 65.93% and node usage by 80.93% relative to the state-of-the-art. Moreover, COMPASS achieves up to 100x faster training and 80x faster inference than state-of-the-art generative methods, and scales to traces with 1.3B samples and 126GB of data.

翻译：高性能计算系统暴露了大量共同驱动竞争性目标的配置参数。现有工具（如自动调优器）虽能推荐良好的配置，但无法对接近目标的次优配置（near-miss configuration）识别最小变更以满足性能目标，且常忽略领域特定约束。为填补这一空白，我们提出 COMPASS——一个模块化、可编程的引擎，利用运行轨迹生成高性能计算配置推荐并指导调优决策。本文：(1) 将配置问题形式化为查询模式；(2) 开发交互式决策引擎，将此类查询构建为机器学习任务；(3) 通过提供证据和量化不确定性来评估推荐的可信度，并在置信度较低时指导下一步应运行的配置。我们通过解析正确性、重构精度、已发表结果复现性以及（条件允许时）实际硬件运行验证了 COMPASS。当与开源高性能计算调度模拟器集成时，相较于现有最优方法，COMPASS 将平均作业周转时间降低 65.93%，节点使用量减少 80.93%。此外，相比于现有最优生成方法，COMPASS 训练速度提升高达 100 倍，推理速度提升高达 80 倍，并可扩展至包含 13 亿样本、126GB 数据的运行轨迹。