COMPASS: A Unified Decision-Intelligence System for Navigating Performance Trade-off in HPC

TL;DR

COMPASS is a decision-engine that uses machine learning on HPC operational traces to optimize configurations, improve performance, and provide trustworthy recommendations with uncertainty quantification.

Contribution

It formalizes HPC configuration questions into query patterns and develops an interactive ML-based engine that guides tuning with evidence and uncertainty measures.

Findings

Cuts average job turnaround time by 65.93%

Reduces node usage by 80.93%

Achieves up to 100x faster training and 80x faster inference than existing methods

Abstract

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.