How to speed up the Rust compiler in September 2026
Nicholas Nethercote's report details a 4.57 percent mean wall-time reduction in the Rust compiler over two months, driven by algorithmic fixes and LLVM updates.
summary is enough A useful status report on compiler performance wins, though it largely catalogs individual pull requests rather than deep-diving code.
summary
Nicholas Nethercote outlines recent performance progress in the Rust compiler, highlighting a 4.57 percent mean wall-time reduction across benchmarks over a two-month period. Key drivers include LLVM updates, PGO for Clippy, and algorithmic improvements like a new dataflow analysis traversal that drastically cut iterations for large functions. Ongoing work also targets performance regressions introduced by upcoming features like the Polonius borrow checker and the new trait solver.
key points
- A new dataflow analysis CFG traversal algorithm reduced fixpoint iteration calls from 1.5 million to 90,000 for a massive cranelift function, cutting check-build wall time by ~30 percent.
- Upgrading to LLVM 23 and enabling PGO for Clippy yielded broad performance wins, achieving up to 18 percent improvement on Clippy benchmarks.
- Engineers are actively mitigating performance regressions from upcoming nightly features like the Polonius borrow checker and the new trait solver.
who should read it
Rust developers and systems engineers interested in compiler internals and performance optimization.
discussion positive
consensus
Commenters agree that Rust compilation speed remains a major bottleneck, especially when scaling up parallel AI agent workflows, prompting some to use Go or optimize crate boundaries and caching.
pushback
Some argue that complex static analysis and rich safety guarantees inherently demand more compile-time work, making direct comparisons to simpler languages unfair.
The EverInitializedPlaces example really stands out. Going from ~1.5M to ~90K apply_effects_in_block calls by changing the CFG traversal is a reminder that the biggest compiler optimizations often come from changing the algorithm, not optimizing the hot loop itself.
It also seems like the new Polonius/trait-solver work is pushing compiler performance toward a more interesting problem: doing expensive analysis only when it is actually needed.
4.57% mean wall-time reduction across 629 benchmarks in two months is pretty remarkable. Great progress.
Points out that the massive gains came from fundamental algorithmic changes rather than micro-optimizations, highlighting the dataflow analysis example.
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