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These are measured numbers from the benchmark suite in the repo, not marketing figures. Unless noted, they were measured on an Apple M4 Pro (14 cores), release build, and are reproducible with cargo bench.

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Parallel ECS throughput

The headline: a 1,000,000-entity world stepped through a 5-system parallel pipeline runs in ~2.69 ms/tick (median). The same workload run sequentially is ~30.6 ms, so the rayon-parallel scheduler delivers an ~11.4× speedup on this machine. For frame-budget intuition: at 60 FPS a frame is 16.6 ms, so a million entities of ECS work fit in roughly a sixth of a frame.
WorkloadTime/tickNotes
1M entities · 5-system pipeline · parallel2.69 ms11.4× over the 30.6 ms sequential run
250K entities · 5-system pipeline · parallel1.36 ms
10K entities · headless physics + transform10.1 msfull gameplay tick, not pure ECS
10K entities · physics step9.78 ms
Dataset extract + digest · 1K entities~0.8–1.05 msthe reproducibility/answer-key path
Scope, stated honestly: the 1M-entity figure is pure ECS (component iteration + the scheduler), not a full render+physics frame. The 10K-entity numbers are full headless gameplay ticks. The render benches in the repo measure CPU-side preparation, not GPU frame time.

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Where the speed comes from

The ECS internals are built for this. Individual optimizations, measured on Apple Silicon (release) at the commits that introduced them — re-run with cargo bench to reproduce on your hardware:
  • spawn_batch — mass spawning 1M entities: 397 ms → 24.1 ms (~16×).
  • Chunked parallel par_for_each (multi-component, 1M): 6.59 ms → 1.05 ms (~6.3×).
  • Chunked single-component read (1M): 2.79 ms → 290 µs (~9.6×); mutate: 6.58 ms → 517 µs (~12.7×).

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Reproduce it

The benchmarks live in benches/ (the parallel-pipeline stress test) and crates/*/benches/ (physics, dataset, math SIMD, animation, nav, scene, particle). To run them: 
# The parallel-ECS stress benchmarks (the 1M / 250K pipeline numbers)
cargo bench -p euca-ecs

# A specific crate's benches, e.g. physics or the dataset/digest path
cargo bench -p euca-physics
cargo bench -p euca-dataset
Criterion writes full reports (median, confidence intervals) to target/criterion/. The canonical provenance for the headline stats — what was measured, when, and on what hardware — is STATS.md at the repo root.