The profiling campaign ranks functions on instruction count (Ir) only,
so callgrind's cache simulation is pure overhead — it roughly doubles
each run's wall time. Pass `--cache-sim=no` via the gungraun
`LibraryBenchmarkConfig` to halve the measure->optimize loop's latency.
Adds benches/compare_profile.rs: a gungraun library_benchmark that runs
the entire `compare` workload once at seed 0 under callgrind. It path-
includes the shared examples/_shared/compare_workload.rs module and calls
the new profile_workload() entry point, which invokes all 82 algorithm
runners and folds every result into a checksum so nothing is elided.
gungraun reports the whole-program instruction count and diffs it against
the previous run; the saved callgrind.out
(target/gungraun/heuropt/compare_profile/.../callgrind.full_compare_workload.out)
carries the per-function breakdown for callgrind_annotate. This is the
measurement harness for the function-level optimization campaign.
Round-0 baseline: 357,060,633,544 Ir. The shared module also carries the
example's presentation layer, so the bench gets a commented
`#![allow(dead_code)]` -- but the runner functions are deliberately not
allow-listed, so a runner missing from profile_workload still warns (that
check already caught one omission).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
swaits
