How does nanobench compare with Google Benchmark, Catch2, and other C++ benchmark libraries?

Updated

nanobench is a single-header C++ benchmarking library that integrates by direct inclusion or CMake, runs each benchmark through a warmup and multiple timed epochs, and reports results as a table, CSV, JSON, HTML box plot, or pyperf data. It is a good fit when you want a low-friction, header-only tool with built-in comparison of alternatives and significance testing. Google Benchmark, Catch2, nonius, Picobench, Celero, and others differ mainly in integration cost, output formats, and how they treat measurement noise. The nanobench documentation publishes side-by-side source code and results for these libraries, so the comparison below follows the dimensions that documentation exposes.

Integration footprint

Library Integration model (per nanobench docs) Practical consequence
nanobench Header-only; direct inclusion or CMake Drop one header in, no separate build target
Google Benchmark Separate library with its own source and results shown Build and link a dependency
Catch2 Benchmarking inside a test framework; source and results shown Benchmark code lives alongside tests
nonius Separate library; source and results shown Build and link a dependency
Picobench Separate library; source and results shown Build and link a dependency
Celero Separate library; source and results shown Build and link a dependency
moodycamel::microbench, sltbench, folly Benchmark Each with source and results shown Each carries its own setup

The header-only design is the clearest differentiator: nanobench adds no link step and no external build target. If your constraint is "benchmark this function without restructuring the build," that matters more than any single performance number.

How results are produced and reported

nanobench's model, as documented, is: run a warmup, then run multiple epochs, reduce each round to one number, and report statistics. It warns when results are unstable and lets you suppress those warnings with NANOBENCH_SUPPRESS_WARNINGS. Output is column-based: you choose which columns appear (showColumn, hideColumn, contextColumn), and the same run can be rendered as a table, CSV, JSON, HTML box plot, or pyperf module output via render() and the templates:: helpers (templates::csv, templates::json, templates::htmlBoxplot, templates::pyperf).

The documentation is explicit that warmup is not a fix for serial correlation, and it discusses what was rejected in designing the experiment. That is a useful signal: the library treats measurement methodology as part of its job, not just timing loops.

Comparison and significance

nanobench has a first-class comparison path: Bench::compare() produces a CompareResult with size(), operator[], rounds(), comparisons(), isSignificant(), and fastest(). In other words, "is A faster than B, and is the difference significant?" is answered by the library rather than by eyeballing two tables. The docs also ask "How precise is a comparison?" — a reminder that a comparison result is only as trustworthy as the run's stability. If you mainly need to rank alternatives, this is the feature to evaluate first.

Choosing by measurement need

  • Minimal integration, quick comparisons: nanobench, because of header-only inclusion and built-in compare() with significance.
  • You already build against Google Benchmark: stay there if its existing harness and reporting fit your workflow; the nanobench docs show its source and results so you can check whether the numbers you care about differ.
  • Benchmarks belong with your test suite: Catch2's benchmarking support keeps them in the same framework, at the cost of coupling benchmarks to tests.
  • You need a specific output format: check nanobench's templates (CSV, JSON, HTML box plot, pyperf) against what your tooling consumes before switching; format support is often the deciding factor, not raw timing.

No library is universally better. The documentation's own framing — comparing source code and results across implementations — supports choosing on integration cost, comparison semantics, and output format for your specific task, then verifying with a benchmark of your own workload rather than trusting a cross-library ranking.

Practical check before committing

Write one benchmark of a function you actually care about in nanobench (include the header, define a Bench, call run()), render it as CSV and as an HTML box plot, and run a compare() against a known-slower variant. If the significance result and the output format both fit your workflow, the integration cost is already settled. If you need a feature the docs do not list, that absence is the answer — the reference covers the API surface, and unlisted capabilities should not be assumed.