jolt/bench

jolt benchmark suite

Benchmarks that isolate the workload axes jolt's optimizing passes target. The ray tracer (examples/ray-tracer) is float-compute-bound — its time is irreducible algorithmic math (hit-testing + transcendentals), and devirt, allocation removal, and type-proving all measured flat on it. So it can't tell us whether those passes work. These benchmarks make each pass's target workload the dominant cost.

Reference: the cross-language suites these draw from — Are We Fast Yet? (Marr et al., DLS '16) and the Computer Language Benchmarks Game. The benchmarks are portable Clojure, so they also run on JVM Clojure for an absolute reference.

Benchmarks

Benchmark	Axis	Pass it exercises	Source
binary-trees	allocation / GC pressure (escaping short-lived records)	scalar-replace, escape analysis	CLBG
dispatch	polymorphic (megamorphic) protocol dispatch	devirt, inline-cache	AWFY-style
mono-dispatch	monomorphic protocol dispatch (devirt/inline-cache can fire)	devirt, inline-cache	AWFY-style
collections	persistent map/vector churn (HAMT / 32-way tries)	persistent structures, transients	CLBG k-nucleotide-style
mandelbrot	pure float compute (tight arith loops, no alloc/dispatch)	native arith, loop codegen	CLBG
fib	recursion: function-call + integer-arith overhead	native arith, small-fn inlining	CLBG

What the ray tracer does not capture and these do: allocation as the bottleneck (~7% there), megamorphic and monomorphic dispatch (its dispatch is monomorphic and cheap), persistent-collection throughput (it uses fixed records, no collections in the hot loop), and isolated compute/call overhead.

Planned additions: Richards / DeltaBlue (heavier OO dispatch), NBody (float control with record state), k-nucleotide proper.

Holistic scorecard

bench/run.sh compiles each benchmark to an optimized AOT binary (joltc build --direct-link --opt) and times it against JVM Clojure running the same portable source — the jolt/JVM scorecard. jolt's optimizing passes fire only in a build; joltc run -m is unoptimized, so the harness always builds.

Indicative ratios (M-series, single isolated run — numbers are machine-specific, regenerate locally), ascending:

benchmark	ratio	axis
fib	~0.6×	call + integer arith
collections	~3.5×	persistent map/vector churn
mandelbrot	~7.5×	pure float compute
binary-trees	~10×	escaping short-lived records (allocation/GC)
dispatch	~12×	megamorphic protocol dispatch
mono-dispatch	~15×	monomorphic protocol dispatch

• Compute (~0.6–7.5×) is the substrate floor: Chez is a native-compiling AOT Scheme, not a profiling JIT. With native arith + direct-linking + inlining jolt is at parity here — fib runs faster than JVM Clojure (no JIT warmup over a short run), collections is within ~3.5×, and mandelbrot (~7.5×) is the pure-tight-loop float ceiling that only native codegen moves further.
• Dispatch & allocation (~10–15×) are the remaining architectural gaps, though the type-proving / native-record / bare-field-read work has collapsed them by an order of magnitude (binary-trees ~140×→~10×, mono-dispatch ~330×→~15×). On a statically proven monomorphic receiver — which whole-program inference now gives for a record iterated out of a vector — devirt resolves the impl and a per-site inline cache holds it (resolved once, not per call), so mono-dispatch is no longer worse than megamorphic. The remaining lever is dispatch: a megamorphic site has no static type, so it pays a full protocol-registry lookup every call where the JVM uses a polymorphic inline cache — a runtime (receiver-type-keyed) cache is the missing piece. binary-trees nodes escape into the tree, so scalar-replace can't remove them — residual GC pressure.

Running

bench/run.sh                 # full suite + JVM scorecard
bench/run.sh fib             # one benchmark, default size
bench/run.sh fib 32          # one benchmark, custom size
NO_JVM=1 bench/run.sh        # jolt only (skip the JVM reference)

Needs Chez's kernel dev files (libkernel.a + scheme.h) and cc for the build, like jolt build; set JOLT_CHEZ_CSV to override the detected csv dir.

A/B against a change

To measure a pass, run the suite on main, then on the branch, back to back (same machine, quiet). Each benchmark prints runs: [...] and mean: N ms; compare the means. A pass is worth landing when it moves a benchmark whose axis it targets, even if the ray tracer stays flat.