THAC: Unlocking Performance in Parallel HPC Applications via UQ-Aware Automated Approximation

While approximate computing offers a promising paradigm for performance gains, significant technical challenges obstruct its practical adoption in High Performance Computing (HPC). The process of manually identifying approximable regions, quantifying the risk of cascading errors, and navigating the vast combinatorial tuning space is intricate and error-prone. To overcome these obstacles, we present Tianhe Approximate Computing (THAC), a framework that establishes a systematic, automated methodology. THAC implements a principled, three-stage workflow: (1) a hybrid AI-driven synthesizer that automatically identifies potential approximation candidates; (2) a principled Uncertainty Quantification screen that rigorously validates and prunes high-risk options through a principled risk-aware screening stage; and (3) a novel hierarchical Bayesian optimizer that efficiently navigates the search space. Although demonstrated on OpenMP as a representative case study, the framework is designed with extensibility for generic parallel patterns. Evaluation on a diverse suite of scientific benchmarks (including computational fluid dynamics and hydrodynamics) demonstrates the effectiveness of THAC, achieving a geometric mean speedup of 2.1× under strict quality budgets. These results confirm THAC as a robust solution that makes approximate computing in HPC practical and reliable.