Performance over time

How ComposableTuringIDModels's benchmark suites have moved across recent revisions: an overall summary across the package first, then one section per suite.

Summary

Each benchmark suite's headline timing across recent revisions.

Suite	Median ratio	Trend	Status
AD gradients	0.98	→	ok
Model evaluation	1.01	→	ok
Sampling	1.03	↗	ok
time_to_load	1.0	→	ok

Ratio: latest vs oldest shown revision (1.00 = no change, higher = slower/larger). ⚠ reg = at/above the regression threshold.

Tables below show the most recent 4 revisions, columns labelled by commit date.

AD gradients

Time

Benchmark	v0.1.2	v0.1.1	v0.1.0	2026-09-07
AR latent logjoint/ForwardDiff	11.8 ± 21 μs	14.1 ± 21 μs	16.1 ± 21 μs	12.9 ± 21 μs
AR latent logjoint/Mooncake reverse	8.07 ± 2.2 μs	7.91 ± 2 μs	7.91 ± 2.2 μs	7.93 ± 2 μs
AR latent logjoint/ReverseDiff (tape)	0.0466 ± 0.0019 ms	0.0505 ± 0.009 ms	0.0509 ± 0.0082 ms	0.0466 ± 0.0016 ms
DirectInfections+Poisson posterior/Enzyme reverse	5.9 ± 0.49 μs	0.0708 ± 0.00078 ms	0.0764 ± 0.00094 ms	5.92 ± 0.5 μs
DirectInfections+Poisson posterior/ForwardDiff	19 ± 24 μs	0.214 ± 0.026 ms	0.217 ± 0.027 ms	18.6 ± 23 μs
DirectInfections+Poisson posterior/Mooncake reverse	9.44 ± 0.44 μs	0.0786 ± 0.00097 ms	0.0788 ± 0.001 ms	9.45 ± 0.5 μs
DirectInfections+Poisson posterior/ReverseDiff (tape)	0.122 ± 0.023 ms	0.175 ± 0.024 ms	0.175 ± 0.024 ms	0.123 ± 0.023 ms

Memory

Benchmark	v0.1.2	v0.1.1	v0.1.0	2026-09-07
AR latent logjoint/ForwardDiff	0.059 k allocs: 0.0512 MB	0.056 k allocs: 0.0508 MB	0.056 k allocs: 0.0508 MB	0.059 k allocs: 0.0512 MB
AR latent logjoint/Mooncake reverse	0.043 k allocs: 4.64 kB	0.036 k allocs: 4.42 kB	0.036 k allocs: 4.42 kB	0.043 k allocs: 4.64 kB
AR latent logjoint/ReverseDiff (tape)	0.738 k allocs: 30.6 kB	0.775 k allocs: 0.0319 MB	0.775 k allocs: 0.0319 MB	0.738 k allocs: 30.6 kB
DirectInfections+Poisson posterior/Enzyme reverse	0.034 k allocs: 4.36 kB	0.242 k allocs: 12.3 kB	0.242 k allocs: 12.3 kB	0.035 k allocs: 4.39 kB
DirectInfections+Poisson posterior/ForwardDiff	0.068 k allocs: 0.0591 MB	0.68 k allocs: 0.0835 MB	0.68 k allocs: 0.0835 MB	0.068 k allocs: 0.0591 MB
DirectInfections+Poisson posterior/Mooncake reverse	0.038 k allocs: 4.83 kB	0.316 k allocs: 14.9 kB	0.316 k allocs: 14.9 kB	0.038 k allocs: 4.83 kB
DirectInfections+Poisson posterior/ReverseDiff (tape)	1.7 k allocs: 0.0643 MB	1.65 k allocs: 0.0654 MB	1.65 k allocs: 0.0654 MB	1.7 k allocs: 0.0643 MB

Model evaluation

Time

Benchmark	v0.1.2	v0.1.1	v0.1.0	2026-09-07
AR latent/forward	0.665 ± 1 μs	0.672 ± 0.13 μs	0.654 ± 0.16 μs	0.657 ± 1 μs
AR latent/rand	1.94 ± 1.2 μs	1.87 ± 1.2 μs	0.873 ± 1.2 μs	1.95 ± 1.2 μs
DirectInfections+Poisson/forward	2.27 ± 0.97 μs	0.0676 ± 0.00089 ms	0.068 ± 0.00085 ms	2.29 ± 0.96 μs
DirectInfections+Poisson/rand	1.98 ± 1.1 μs	0.0666 ± 0.00082 ms	0.0666 ± 0.00082 ms	2 ± 1.2 μs
RandomWalk latent/forward	1.28 ± 0.7 μs	1.27 ± 0.71 μs	1.25 ± 0.7 μs	1.3 ± 0.64 μs
RandomWalk latent/rand	1.44 ± 0.93 μs	1.46 ± 0.95 μs	1.45 ± 0.93 μs	1.46 ± 0.95 μs
Renewal+NegativeBinomial/forward	7.68 ± 2.9 μs	0.0728 ± 0.00097 ms	0.0726 ± 0.00094 ms	7.59 ± 2.8 μs
Renewal+NegativeBinomial/rand	5.21 ± 3 μs	0.0698 ± 0.0012 ms	0.0702 ± 0.0012 ms	5.19 ± 3.1 μs

Memory

Benchmark	v0.1.2	v0.1.1	v0.1.0	2026-09-07
AR latent/forward	21 allocs: 2.44 kB	20 allocs: 2.41 kB	20 allocs: 2.41 kB	21 allocs: 2.44 kB
AR latent/rand	23 allocs: 2.86 kB	22 allocs: 2.83 kB	22 allocs: 2.83 kB	23 allocs: 2.86 kB
DirectInfections+Poisson/forward	23 allocs: 2.52 kB	0.35 k allocs: 15.8 kB	0.35 k allocs: 15.8 kB	23 allocs: 2.52 kB
DirectInfections+Poisson/rand	20 allocs: 2.67 kB	0.349 k allocs: 15.1 kB	0.349 k allocs: 15.1 kB	20 allocs: 2.67 kB
RandomWalk latent/forward	17 allocs: 1.86 kB	16 allocs: 1.83 kB	16 allocs: 1.83 kB	17 allocs: 1.86 kB
RandomWalk latent/rand	16 allocs: 2.08 kB	15 allocs: 2.05 kB	15 allocs: 2.05 kB	16 allocs: 2.08 kB
Renewal+NegativeBinomial/forward	0.153 k allocs: 8.28 kB	0.57 k allocs: 23.7 kB	0.57 k allocs: 23.7 kB	0.153 k allocs: 8.28 kB
Renewal+NegativeBinomial/rand	0.148 k allocs: 8.38 kB	0.567 k allocs: 23 kB	0.567 k allocs: 23 kB	0.148 k allocs: 8.38 kB

Sampling

Time

Benchmark	v0.1.2	v0.1.1	v0.1.0	2026-09-07
NUTS (DirectInfections+Poisson, 50 draws)	0.128 ± 0.014 s	0.962 ± 0.041 s	0.973 ± 0.033 s	0.132 ± 0.0056 s

Memory

Benchmark	v0.1.2	v0.1.1	v0.1.0	2026-09-07
NUTS (DirectInfections+Poisson, 50 draws)	0.479 M allocs: 0.269 GB	2.99 M allocs: 0.371 GB	2.99 M allocs: 0.371 GB	0.479 M allocs: 0.269 GB

time_to_load

Time

Benchmark	v0.1.2	v0.1.1	v0.1.0	2026-09-07
time_to_load	4.91 ± 0.11 s	4.91 ± 0.051 s	5.12 ± 0.014 s	4.91 ± 0.17 s

Memory

Benchmark	v0.1.2	v0.1.1	v0.1.0	2026-09-07
time_to_load	0.149 k allocs: 11.2 kB	0.149 k allocs: 11.2 kB	0.149 k allocs: 11.2 kB	0.149 k allocs: 11.2 kB

Per-benchmark timelines

<details> <summary>Show 1 plot</summary>

</details>

About these benchmarks

ComposableTuringIDModels tracks the performance of representative modelling operations over time. The suite is a prototype: it covers a small set of representative models rather than exhaustively measuring every component, and the numbers are indicative rather than a guarantee.

Benchmarking reuses the shared tooling in EpiAwarePackageTools.Benchmarks rather than re-implementing a runner or a comparison report. The package owns the suite definition (benchmark/benchmarks.jl); the kit owns running it and turning results into a legible pull-request comment.

What is measured

The suite is a BenchmarkTools.BenchmarkGroup named SUITE, defined in benchmark/benchmarks.jl, with three groups.

• Model evaluation — building and evaluating representative models. For each model the suite times a prior draw (rand, which samples every random variable) and the forward pass (model(), which returns the generated quantities). The models are two latent processes (AR, RandomWalk) and two composed IDModels (DirectInfections with PoissonError, and Renewal with NegativeBinomialError), each turned into a Turing model via as_turing_model.

• Sampling — a short NUTS run (50 draws) on a composed DirectInfections + PoissonError model conditioned on data simulated from its own prior.

• AD gradients — the gradient of a representative log-density across automatic-differentiation backends (ForwardDiff, ReverseDiff, Mooncake, and Enzyme where supported). Results are keyed by scenario and backend so the comparison report folds them into a per-(scenario × backend) matrix.

Running the suite locally

The benchmark/ directory is its own Julia environment. Run the whole suite and save the results with the managed runner, which calls EpiAwarePackageTools.Benchmarks.run_suite:

julia --project=benchmark benchmark/run.jl results.json

run_suite uses a short per-benchmark time budget so a full run stays affordable while the minimum-time estimator used in the comparison stays stable. To compare two result files and write a Markdown report, use the managed comparison script, which calls EpiAwarePackageTools.Benchmarks.compare_comment:

julia --project=benchmark benchmark/compare.jl pr.json base.json comment.md

Continuous integration

Two workflows drive benchmarking in CI, both building on the shared kit.

• benchmark.yaml runs on pull requests. It benchmarks the pull-request head and the base branch in separate jobs, then posts (and updates) a single comparison comment: a bucketed summary plus collapsed per-benchmark tables split into evaluation and AD-gradient groups.

• benchmark-history.yaml runs on pushes to main and on tags. It benchmarks the recent tagged releases plus the current commit with AirspeedVelocity and publishes a timeline to the repository's benchmarks branch. The kit's asv_comment / flatten_asv helpers read the same AirspeedVelocity result format when a report is needed.