Abstract: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 \emph{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 \texttt{opt -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 \texttt{opt -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.
From: Haolin Pan [view email]
[v1]
Wed, 15 Oct 2025 06:01:19 UTC (4,098 KB)
[v2]
Fri, 31 Jul 2026 08:50:43 UTC (8,887 KB)
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