Where Does the Performance Go? Understanding and Recovering Performance Regressions in C-to-Rust Translation

Key points

• Preserving behavior, however, does not guarantee preserving performance.1
• We study this gap on 20 real-world C projects and their \crust{} translations.1
• By comparing execution-cost profiles, optimized LLVM IR, and compiler optimization remarks across each C--Rust pair, we trace these slowdowns to nine recurring performance issues, spanning residual runtime work, missed compiler optimizations, and C-style source patterns, and derive an optimization direction for each.1
• We then present \ourtool{}, an LLM-based approach that encodes these issues as reusable optimization rules to recover the performance of translated Rust. \ourtool{} profiles each project to locate hot functions and matches them against rule-specific patterns.1

Sources (1)

• [1]Where Does the Performance Go? Understanding and Recovering Performance Regressions in C-to-Rust Translation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 07:22 AM

  Rust is increasingly adopted as a memory-safe successor to C, and transpilers such as \crust{} can migrate entire C projects while preserving their behavior.

  Preserving behavior, however, does not guarantee preserving performance.

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