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>
68 lines
3.7 KiB
Markdown
68 lines
3.7 KiB
Markdown
# jolt benchmark suite
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Benchmarks that isolate the workload axes jolt's optimizing passes target. The
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ray tracer (`examples/ray-tracer`) is **float-compute-bound** — its time is
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irreducible algorithmic math (hit-testing + transcendentals), and devirt,
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allocation removal, and type-proving all measured **flat** on it. So it can't
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tell us whether those passes work. These benchmarks make each pass's target
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workload the *dominant* cost.
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Reference: the cross-language suites these draw from —
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[Are We Fast Yet?](https://github.com/smarr/are-we-fast-yet) (Marr et al., DLS '16)
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and the [Computer Language Benchmarks Game](https://benchmarksgame-team.pages.debian.net/benchmarksgame/).
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The benchmarks are portable Clojure, so they also run on JVM Clojure for an
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absolute reference.
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## Benchmarks
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| Benchmark | Axis | Pass it exercises | Source |
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|---|---|---|---|
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| `binary-trees` | allocation / GC pressure (escaping short-lived records) | jolt-15jq scalar-replace, jolt-8flj escape analysis | CLBG |
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| `dispatch` | polymorphic (**megamorphic**) protocol dispatch | jolt-41m devirt, inline-cache | AWFY-style |
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| `mono-dispatch` | **monomorphic** protocol dispatch (devirt/inline-cache *can* fire) | jolt-41m devirt, jolt-ez5h inline-cache | AWFY-style |
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| `collections` | persistent map/vector churn (HAMT / 32-way tries) | persistent structures (jolt-684u/0hbr), transients | CLBG k-nucleotide-style |
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| `mandelbrot` | pure float compute (tight arith loops, no alloc/dispatch) | jolt-3pl native arith, loop codegen | CLBG |
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| `fib` | recursion: function-call + integer-arith overhead | jolt-3pl native arith, jolt-826 small-fn inlining | CLBG |
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What the ray tracer does **not** capture and these do: allocation as the
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bottleneck (~7% there), megamorphic *and* monomorphic dispatch (its dispatch is
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monomorphic and cheap), persistent-collection throughput (it uses fixed records,
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no collections in the hot loop), and isolated compute/call overhead.
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Planned additions: Richards / DeltaBlue (heavier OO dispatch), NBody (float
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control with record state), k-nucleotide proper.
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## Holistic scorecard
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`JVM=1 bench/run.sh` runs each benchmark on jolt **and** JVM Clojure and prints
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the jolt/JVM ratio — the epic's (jolt-ffn) absolute-reference scorecard. As of
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the broadening (2026-06-16), ratios cluster by axis:
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- **pure compute** (`mandelbrot`) is the floor, ~15× — native arith (jolt-3pl)
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already gets jolt closest to the JVM.
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- **collections** ~28×, **fib** ~37×.
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- **dispatch** ~75× (megamorphic), and `mono-dispatch` is *worse* (~110×): the
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JVM inline-caches a runtime-monomorphic call site to near-free, while jolt does
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a full registry dispatch regardless (devirt only fires on *statically* proven
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receivers, which `reduce` over a vector doesn't give). This is the signal for
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the call-site inline cache (jolt-ez5h).
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- **allocation** (`binary-trees`) is the widest gap — but also the most inflated
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by host memory pressure, so read it as "alloc is the worst axis," not a precise
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multiple. Numbers are machine-specific; regenerate with `JVM=1 bench/run.sh`.
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## Running
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```sh
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jpm build && export PATH="$PWD/build:$PATH"
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bench/run.sh # whole-program optimization on (default)
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JOLT_WHOLE_PROGRAM=0 bench/run.sh # WP off, to measure what WP buys
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bench/run.sh binary-trees 16 # one benchmark, custom size
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```
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## A/B against a change
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To measure a pass, run the suite on `main`, then on the branch, back to back
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(same machine, quiet) — the protocol used for `test/bench/core-bench.janet` and
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the ray tracer. Each benchmark prints `runs: [...]` and `mean: N ms`; compare
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the means. A pass is worth landing when it moves a benchmark whose axis it
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targets, even if the ray tracer stays flat.
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