# GRACE: Cluster-Specific Sequence Reuse for Compiler Auto-Tuning

arXiv: https://arxiv.org/abs/2510.13176 · HTML: https://mlsanity.com/paper/2510.13176/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2025.

## Built on by (most cited first, 1 of 1)

- [ECCO: Evidence-Driven Causal Reasoning for Compiler Optimization](https://mlsanity.com/paper/2602.00087/) (2026)

## Abstract (arXiv)

Compiler auto-tuning aims to improve optimization quality beyond fixed compiler heuristics, but existing approaches often face a trade-off between effectiveness and deployability. Iterative compilation can discover strong program-specific optimization sequences, yet its search cost is often prohibitive for practical reuse. Learning-based methods reduce tuning overhead, but their effectiveness depends on how well optimization knowledge transfers to unseen programs. Recent coreset-based methods improve this trade-off, but they typically either still rely on relatively large test-time search or assume that a single global coreset can serve all programs well. We present GRACE, a compiler auto-tuning framework based on cluster-specific sequence reuse. GRACE constructs a small reusable sequence coreset for each group of similar programs by combining global pass synergy analysis, optimization-response-guided program organization, and cluster-specific evolutionary search. At deployment time, it evaluates a small coreset on the target program and optionally performs lightweight refinement within a restricted search space, yielding bounded overhead. We evaluate GRACE on seven benchmark datasets using LLVM 10.0.0 and LLVM 18.1.6. For code-size optimization, GRACE reduces LLVM IR instruction count by 9.92% and 10.30% on average relative to textttopt -Oz, while requiring less than 1,s tuning time per program at deployment. Under an execution-oriented objective, GRACE reduces estimated cycle counts by 26.84% and 27.54% on average relative to textttopt -O3, and also yields measurable end-to-end speedups on runnable cBench and polybench programs. These results suggest that offline-constructed, cluster-specific sequence coresets provide a practical balance between optimization quality and cost.