[ogoffart]

We are computing the display column of a position by decoding every
char from the start of its line for the location of every possible panic,
so a file with very long lines takes quadratic time.
(This is the case for a generated file that can get very long lines)

For most code (ascii without tabs) the column is the byte index.
So optimize the fast path.

On a ~5 MB generated single line file, the build goes from 161s to 15s

LLM disclosure: an AI agent was used to diagnose the issue and first iteration, but the patch was then iterated and simplified by me.

[rustbot]

r? @JohnTitor

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The reviewer was selected based on:

• Owners of files modified in this PR: compiler
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[ogoffart]

I considered trying to do this in analyze_source_file::analyze_source_file doing that at the same time as breaking lines and detecting non-ascii, but that is more involved and i hope contains is optimized enough already.

View changes since the review

[panstromek]

I assume you want to measure this?

@bors try @rust-timer queue

Also note that this likely interacts with LLM policy: https://forge.rust-lang.org/policies/llm-usage.html

[rust-bors]

☀️ Try build successful (CI)
Build commit: b0e2506 (b0e2506816d5b90bd35fe35a8f05b58d1fae4d2a)
Base parent: 2b548e4 (2b548e49fcdf52bed4446b3e245563681f5bc45d)

[rust-timer]

Finished benchmarking commit (b0e2506): comparison URL.

Overall result: ❌✅ regressions and improvements - please read:

Benchmarking means the PR may be perf-sensitive. It's automatically marked not fit for rolling up. Overriding is possible but disadvised: it risks changing compiler perf.

Next, please: If you can, justify the regressions found in this try perf run in writing along with @rustbot label: +perf-regression-triaged. If not, fix the regressions and do another perf run. Neutral or positive results will clear the label automatically.

@bors rollup=never rustc-perf
@rustbot label: -S-waiting-on-perf +perf-regression

Instruction count

Our most reliable metric. Used to determine the overall result above. However, even this metric can be noisy.

	mean	range	count
Regressions ❌ (primary)	0.2%	[0.2%, 0.3%]	2
Regressions ❌ (secondary)	1.3%	[0.1%, 4.6%]	24
Improvements ✅ (primary)	-0.4%	[-1.0%, -0.2%]	39
Improvements ✅ (secondary)	-1.0%	[-3.7%, -0.1%]	16
All ❌✅ (primary)	-0.4%	[-1.0%, 0.3%]	41

Max RSS (memory usage)

Results (primary 0.4%, secondary 1.7%)

A less reliable metric. May be of interest, but not used to determine the overall result above.

	mean	range	count
Regressions ❌ (primary)	1.1%	[0.5%, 2.0%]	10
Regressions ❌ (secondary)	1.9%	[0.4%, 4.3%]	19
Improvements ✅ (primary)	-1.2%	[-1.6%, -0.5%]	4
Improvements ✅ (secondary)	-1.7%	[-1.7%, -1.7%]	1
All ❌✅ (primary)	0.4%	[-1.6%, 2.0%]	14

Cycles

Results (primary -0.5%, secondary -0.1%)

A less reliable metric. May be of interest, but not used to determine the overall result above.

	mean	range	count
Regressions ❌ (primary)	0.8%	[0.5%, 1.2%]	3
Regressions ❌ (secondary)	1.0%	[0.7%, 1.4%]	4
Improvements ✅ (primary)	-0.9%	[-1.7%, -0.4%]	11
Improvements ✅ (secondary)	-0.9%	[-1.5%, -0.5%]	5
All ❌✅ (primary)	-0.5%	[-1.7%, 1.2%]	14

Binary size

This perf run didn't have relevant results for this metric.

Bootstrap: 487.289s -> 487.926s (0.13%)
Artifact size: 406.27 MiB -> 406.31 MiB (0.01%)