The ray tracer is compute-bound and the three existing benches only cover
alloc / megamorphic-dispatch / collections. Add three axes the epic needs to
judge itself holistically:
- mono-dispatch: monomorphic protocol dispatch. Its jolt/JVM ratio (~110x) is
*worse* than megamorphic (~76x) — the JVM inline-caches a runtime-monomorphic
call site to near-free while jolt does a full registry dispatch (devirt only
fires on statically-proven receivers). Points at the call-site inline cache.
- mandelbrot: pure float compute, no alloc/dispatch. The floor at ~15x — native
arith already gets close to the JVM.
- fib: recursion, call + integer-arith overhead.
run.sh gains JVM=1, which runs each bench on JVM Clojure too and prints the
jolt/JVM ratio. collections sized up now that the map is a HAMT (jolt-684u).
README documents the axes and the current scorecard.
Co-authored-by: Yogthos <yogthos@gmail.com>
The ray tracer is float-compute-bound (devirt, alloc removal, type-proving all
measured flat on it), so it can't validate the optimization passes. Add a small
cross-language suite (AWFY + CLBG style, portable Clojure) isolating the axes it
misses:
binary-trees allocation / GC pressure (escaping short-lived records)
dispatch megamorphic protocol dispatch (~1M dispatches/s; WP can't devirt)
collections persistent map/vector churn
bench/run.sh runs them; bench/README.md maps each to the pass it exercises.
collections immediately surfaced jolt-684u: the persistent hash map is O(n) per
assoc (flat copy-on-write bucket array, not a HAMT) — n=4000 assocs take 50s.
Invisible to the ray tracer (no maps).
Co-authored-by: Yogthos <yogthos@gmail.com>
