{"ID":5438683,"CreatedAt":"2026-07-01T01:17:58.482524686Z","UpdatedAt":"2026-07-03T07:00:11.61005204Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.31238","arxiv_id":"2606.31238","title":"A Multi-Dimensional, Per-Pass Empirical Study of the LLVM Optimization Pipeline","abstract":"Quantifying the marginal impact of individual optimization passes underpins phase ordering, pass selection, optimization design, and analysis of pass/hardware interactions. In LLVM -- the standard backend for C/C++, Rust, and ML stacks via MLIR -- interactions among optimization passes, measurement noise, and pipeline scale make this difficult. We present a systematic, empirical study of the LLVM -O3 optimization pipeline. We decompose the pipeline into cumulative per-pass prefixes. We then measure execution time, compile time, binary size, hardware counters, and RAPL energy across 84,750 measurements covering 113 cumulative prefixes of the -O3 pipeline evaluated on 30 PolyBench/C kernels under rigorous noise mitigation. On these compute-bound affine kernels, the pipeline is non-monotone (6.6-9.7% of transitions regress) and strongly back-loaded (the median non-regressing kernel needs 84.8% of the pipeline for 80% of its speedup). Most gains are driven by a small Pareto-dominant core of passes, while the final -O3 configuration is Pareto-dominated on (size, speedup) for 29 of 30 kernels. We further show that IR instruction count is an unreliable predictor of runtime, that runtime-targeted passes are de facto energy-targeted (30-60% savings), and that the search-free idealized-additive upper bound on losses due to phase interference is 46.35%. These findings enable more informed pass pruning, cost-model calibration, and autotuning.","short_abstract":"Quantifying the marginal impact of individual optimization passes underpins phase ordering, pass selection, optimization design, and analysis of pass/hardware interactions. In LLVM -- the standard backend for C/C++, Rust, and ML stacks via MLIR -- interactions among optimization passes, measurement noise, and pipeline...","url_abs":"https://arxiv.org/abs/2606.31238","url_pdf":"https://arxiv.org/pdf/2606.31238v1","authors":"[\"Federico Bruzzone\",\"Walter Cazzola\"]","published":"2026-06-30T07:10:48Z","proceeding":"cs.SE","tasks":"[\"cs.SE\",\"cs.PF\",\"cs.PL\"]","methods":"[]","has_code":false}