mitata

benchmark tooling that loves you ❤️

### Install `bun add mitata` `npm install mitata` try mitata in browser with ai assistant at [https://bolt.new/~/mitata](https://bolt.new/~/mitata) ## Recommendations - use dedicated hardware for running benchmarks - read [writing good benchmarks](#writing-good-benchmarks) & [LLVM benchmarking tips](https://llvm.org/docs/Benchmarking.html) - run with manual garbage collection enabled (e.g. `node --expose-gc ...`) - install optional [hardware counters](#hardware-counters) extension to see cpu stats like IPC (instructions per cycle) - make sure your runtime has high-resolution timers and other relevant options/permissions enabled ## Quick Start

javascript	c++ single header
```js import { run, bench, boxplot, summary } from 'mitata'; function fibonacci(n) { if (n <= 1) return n; return fibonacci(n - 1) + fibonacci(n - 2); } bench('fibonacci(40)', () => fibonacci(40)); boxplot(() => { summary(() => { bench('Array.from($size)', function* (state) { const size = state.get('size'); yield () => Array.from({ length: size }); }).range('size', 1, 1024); }); }); await run(); ```	```cpp #include "src/mitata.hpp" int fibonacci(int n) { if (n <= 1) return n; return fibonacci(n - 1) + fibonacci(n - 2); } int main() { mitata::runner runner; runner.bench("noop", []() { }); runner.summary([&]() { runner.bench("empty fn", []() { }); runner.bench("fibonacci", []() { fibonacci(20); }); }); auto stats = runner.run(); } ```

javascript

c++ single header

```js import { run, bench, boxplot, summary } from 'mitata'; function fibonacci(n) { if (n <= 1) return n; return fibonacci(n - 1) + fibonacci(n - 2); } bench('fibonacci(40)', () => fibonacci(40)); boxplot(() => { summary(() => { bench('Array.from($size)', function* (state) { const size = state.get('size'); yield () => Array.from({ length: size }); }).range('size', 1, 1024); }); }); await run(); ```

```cpp #include "src/mitata.hpp" int fibonacci(int n) { if (n <= 1) return n; return fibonacci(n - 1) + fibonacci(n - 2); } int main() { mitata::runner runner; runner.bench("noop", []() { }); runner.summary([&]() { runner.bench("empty fn", []() { }); runner.bench("fibonacci", []() { fibonacci(20); }); }); auto stats = runner.run(); } ```

## configure your experience ```js import { run } from 'mitata'; await run({ format: 'json' }) // output json await run({ filter: /new Array.*/ }) // only run benchmarks that match regex filter await run({ throw: true }); // will immediately throw instead of handling error quietly await run({ format: { mitata: { name: 'fixed' } } }); // benchmarks name column is fixed length // c++ auto stats = runner.run({ .colors = true, .format = "json", .filter = std::regex(".*") }); ``` ## garbage collection By default, on runtimes with exposed manual gc (like bun or node with `--expose-gc`), mitata runs garbage collection once after each benchmark warmup. This behavior can be customized using `gc(mode)` method on benchmarks: ```js bench('lots of allocations', () => { Array.from({ length: 1024 }, () => Array.from({ length: 1024 }, () => new Array(1024))); }) // mode: false | 'once' (default) | 'inner' // once mode runs gc after warmup // inner mode runs gc after warmup and before each (batch-)iteration .gc('inner'); ``` ### gc impact and memory usage For runtimes that provide manual garbage collection or offer access to javscript vm heap usage metrics, additional row will be shown with garbage collection timings or/and estimated heap usage. ```js ------------------------------------------- ------------------------------- new Array(512) 509.42 ns/iter 536.53 ns ▅▃█ ▂ (449.52 ns … 632.54 ns) 609.34 ns ███ ▃▅▆█▇ ( 0.00 b … 24.00 kb) 1.61 kb ▆████▅▄██████▅▅▅█▅▄▂▂ Array.from(512) 1.29 µs/iter 1.30 µs ▂▆█ (1.27 µs … 1.48 µs) 1.40 µs ▂███▇▆▃▃▂▁▁▂▁▁▁▁▁▁▁▁▁ gc(457.25 µs … 760.54 µs) 512.32 b ( 0.00 b… 84.00 kb) ``` ## universal compatibility Out of box mitata can detect engine/runtime it's running on and fall back to using [alternative](https://github.com/evanwashere/mitata/blob/master/src/lib.mjs#L51) non-standard I/O functions. If your engine or runtime is missing support, open an issue or pr requesting for support. ### how to use mitata with engine CLIs like d8, jsc, graaljs, spidermonkey ```bash $ xs bench.mjs $ quickjs bench.mjs $ d8 --expose-gc bench.mjs $ spidermonkey -m bench.mjs $ graaljs --js.timer-resolution=1 bench.mjs $ /System/Library/Frameworks/JavaScriptCore.framework/Versions/Current/Helpers/jsc bench.mjs ``` ```js // bench.mjs import { print } from './src/lib.mjs'; import { run, bench } from './src/main.mjs'; // git clone import { run, bench } from './node_modules/mitata/src/main.mjs'; // npm install print('hello world'); // works on every engine ``` ## adding arguments and parameters to your benchmarks has never been so easy With other benchmarking libraries, often it's quite hard to easily make benchmarks that go over a range or run the same function with different arguments without writing spaghetti code, but now with mitata converting your benchmark to use arguments is just a function call away. ```js import { bench } from 'mitata'; bench(function* look_mom_no_spaghetti(state) { const len = state.get('len'); const len2 = state.get('len2'); yield () => new Array(len * len2); }) .args('len', [1, 2, 3]) .range('len', 1, 1024) // 1, 8, 64, 512... .dense_range('len', 1, 100) // 1, 2, 3 ... 99, 100 .args({ len: [1, 2, 3], len2: ['4', '5', '6'] }) // every possible combination ``` ### computed parameters For cases where you need unique copy of value for each iteration, mitata supports creating computed parameters that do not count towards benchmark results *(note: there is no guarantee of recompute time, order, or call count)*: ```js bench('deleting $keys from object', function* (state) { const keys = state.get('keys'); const obj = {}; for (let i = 0; i < keys; i++) obj[i] = i; yield { [0]() { return { ...obj }; }, bench(p0) { for (let i = 0; i < keys; i++) delete p0[i]; }, }; }).args('keys', [1, 10, 100]); ``` ### concurrency `concurrency` option enables transparent concurrent execution of asynchronous benchmark, providing insights into: - scalability of async functions - potential bottlenecks in parallel code - performance under different levels of concurrency *(note: concurrent benchmarks may have higher variance due to scheduling, contention, event loop and async overhead)* ```js bench('sleepAsync(1000) x $concurrency', function* () { // concurrency inherited from arguments yield async () => await sleepAsync(1000); }).args('concurrency', [1, 5, 10]); bench('sleepAsync(1000) x 5', function* () { yield { // concurrency is set manually concurrency: 5, async bench() { await sleepAsync(1000); }, }; }); ``` ## hardware counters `bun add @mitata/counters` `npm install @mitata/counters` supported on: `macos (apple silicon) | linux (amd64, aarch64)` linux: - `/proc/sys/kernel/perf_event_paranoid` has to be set to `2` or lower - on some vm systems pmu is disabled by hypervisor (usually when cpu core is shared across vms) macos: - [Apple Silicon CPU optimization guide/handbook](https://developer.apple.com/documentation/apple-silicon/cpu-optimization-guide) - Xcode must be installed for complete cpu counters support - Instruments.app (CPU Counters) has to be closed during benchmarking - Corrupted install of Xcode/Command Line Tools can result in kernel panic (requires Xcode/Command Line Tools reinstall) By installing `@mitata/counters` package you can enable collection and displaying of hardware counters for benchmarks. ```rust ------------------------------------------- ------------------------------- new Array(1024) 332.67 ns/iter 337.90 ns █ (295.63 ns … 507.93 ns) 455.66 ns ▂██▇▄▂▂▂▁▂▁▃▃▃▂▂▁▁▁▁▁ 2.41 ipc ( 48.66% stalls) 37.89% L1 data cache 1.11k cycles 2.69k instructions 33.09% retired LD/ST ( 888.96) new URL(google.com) 246.40 ns/iter 245.10 ns █▃ (206.01 ns … 841.23 ns) 302.39 ns ▁▁▁▁▂███▇▃▂▂▂▂▂▂▂▁▁▁▁ 4.12 ipc ( 1.05% stalls) 98.88% L1 data cache 856.49 cycles 3.53k instructions 28.65% retired LD/ST ( 1.01k) ``` ## helpful warnings For those who love doing micro-benchmarks, mitata can automatically detect and inform you about optimization passes like dead code elimination without requiring any special engine flags. ```rust -------------------------------------- ------------------------------- 1 + 1 318.63 ps/iter 325.37 ps ▇ █ ! (267.92 ps … 14.28 ns) 382.81 ps ▁▁▁▁▁▁▁█▁▁█▁▁▁▁▁▁▁▁▁▁ empty function 319.36 ps/iter 325.37 ps █ ▅ ! (248.62 ps … 46.61 ns) 382.81 ps ▁▁▁▁▁▁▃▁▁█▁█▇▁▁▁▁▁▁▁▁ ! = benchmark was likely optimized out (dead code elimination) ``` ## powerful visualizations right in your terminal With mitata’s ascii rendering capabilities, now you can easily visualize samples in barplots, boxplots, lineplots, histograms, and get clear summaries without any additional tools or dependencies. ```js import { summary, barplot, boxplot, lineplot } from 'mitata'; // wrap bench() calls in visualization scope barplot(() => { bench(...) }); ┌ ┐ 1 + 1 ┤■ 318.11 ps Date.now() ┤■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 27.69 ns └ ┘ // scopes can be async await boxplot(async () => { // ... }); ┌ ┐ ╷┌─┬─┐ ╷ Bubble Sort ├┤ │ ├───────────────────────┤ ╵└─┴─┘ ╵ ┬ ╷ Quick Sort │───┤ ┴ ╵ ┬ Native Sort │ ┴ └ ┘ 90.88 µs 2.43 ms 4.77 ms // can combine multiple visualizations lineplot(() => { summary(() => { // ... }); // bench() calls here wont be part of summary }); summary new Array($len) 5.42…8.33x faster than Array.from($len) ┌ ┐ Array.from($size) ⢠⠊ new Array($size) ⢀⠔⠁ ⡠⠃ ⢀⠎ ⡔⠁ ⡠⠊ ⢀⠜ ⡠⠃ ⡔⠁ ⢀⠎ ⡠⠃ ⢀⠜ ⢠⠊ ⣀⣀⠤⠤⠒ ⡰⠁ ⣀⡠⠤⠔⠒⠊⠉ ⣀⣀⣀⠤⠜ ⣀⡠⠤⠒⠊⠉ ⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣔⣒⣒⣊⣉⠭⠤⠤⠤⠤⠤⠒⠊⠉ └ ┘ ``` ## give your own code power of mitata In case you don’t need all the fluff that comes with mitata or just need raw results, mitata exports its fundamental building blocks to allow you to easily build your own tooling and wrappers without losing any core benefits of using mitata. ```cpp #include "src/mitata.hpp" int main() { auto stats = mitata::lib::fn([]() { /***/ }) } ``` ```js import { B, measure } from 'mitata'; // lowest level for power users const stats = await measure(function* (state) { const size = state.get('x'); yield { [0]() { return size; }, bench(size) { return new Array(size); }, }; }, { args: { x: 1 }, batch_samples: 5 * 1024, min_cpu_time: 1000 * 1e6, }); // explore how magic happens console.log(stats.debug) // -> jit optimized source code of benchmark // higher level api that includes mitata's argument and range features const b = new B('new Array($x)', function* (state) { const size = state.get('x'); yield () => new Array(size); }).args('x', [1, 5, 10]); const trial = await b.run(); ``` ## accuracy down to picoseconds By leveraging the power of javascript JIT compilation, mitata is able to generate zero-overhead measurement loops that provide picoseconds precision in timing measurements. These loops are so precise that they can even be reused to provide additional features like CPU clock frequency estimation and dead code elimination detection, all while staying inside javascript vm sandbox. With [computed parameters](#computed-parameters) and [garbage collection tuning](#garbage-collection), you can tap into mitata's code generation capabilities to further refine the accuracy of your benchmarks. Using computed parameters ensures that parameters computation is moved outside the benchmark, thereby preventing the javascript JIT from performing loop invariant code motion optimization. ```rust // node --expose-gc --allow-natives-syntax tools/compare.mjs clk: ~2.71 GHz cpu: Apple M2 Pro runtime: node 23.3.0 (arm64-darwin) benchmark avg (min … max) p75 p99 (min … top 1%) ------------------------------------------- ------------------------------- a / b 4.59 ns/iter 4.44 ns █ (4.33 ns … 25.86 ns) 6.91 ns ██▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁ 6.70 ipc ( 2.17% stalls) NaN% L1 data cache 16.80 cycles 112.52 instructions 0.00% retired LD/ST ( 0.00) a / b (computed) 4.23 ns/iter 4.10 ns ▇█ (3.88 ns … 30.03 ns) 7.26 ns ██▅▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁ 6.40 ipc ( 2.10% stalls) NaN% L1 data cache 15.70 cycles 100.53 instructions 0.00% retired LD/ST ( 0.00) 4.59 ns/iter - https://npmjs.com/mitata // vs other libraries a / b x 90,954,882 ops/sec ±2.13% (92 runs sampled) 10.99 ns/iter - https://npmjs.com/benchmark ┌─────────┬───────────┬──────────────────────┬─────────────────────┬────────────────────────────┬───────────────────────────┬──────────┐ │ (index) │ Task name │ Latency average (ns) │ Latency median (ns) │ Throughput average (ops/s) │ Throughput median (ops/s) │ Samples │ ├─────────┼───────────┼──────────────────────┼─────────────────────┼────────────────────────────┼───────────────────────────┼──────────┤ │ 0 │ 'a / b' │ '27.71 ± 0.09%' │ '41.00' │ '28239766 ± 0.01%' │ '24390243' │ 36092096 │ └─────────┴───────────┴──────────────────────┴─────────────────────┴────────────────────────────┴───────────────────────────┴──────────┘ 27.71 ns/iter - vitest bench / https://npmjs.com/tinybench a / b x 86,937,932 ops/sec (11 runs sampled) v8-never-optimize=true min..max=(11.32ns...11.62ns) 11.51 ns/iter - https://npmjs.com/bench-node ╔══════════════╤═════════╤════════════════════╤═══════════╗ ║ Slower tests │ Samples │ Result │ Tolerance ║ ╟──────────────┼─────────┼────────────────────┼───────────╢ ║ Fastest test │ Samples │ Result │ Tolerance ║ ╟──────────────┼─────────┼────────────────────┼───────────╢ ║ a / b │ 10000 │ 14449822.99 op/sec │ ± 4.04 % ║ ╚══════════════╧═════════╧════════════════════╧═══════════╝ 69.20 ns/iter - https://npmjs.com/cronometro ```

same test with v8 jit compiler disabled:

```rust // node --expose-gc --allow-natives-syntax --jitless tools/compare.mjs clk: ~0.06 GHz cpu: Apple M2 Pro runtime: node 23.3.0 (arm64-darwin) benchmark avg (min … max) p75 p99 (min … top 1%) ------------------------------------------- ------------------------------- a / b 74.52 ns/iter 75.53 ns █ (71.96 ns … 104.94 ns) 92.01 ns █▅▇▅▅▃▃▂▁▁▁▁▁▁▁▁▁▁▁▁▁ 5.78 ipc ( 0.51% stalls) NaN% L1 data cache 261.51 cycles 1.51k instructions 0.00% retired LD/ST ( 0.00) a / b (computed) 56.05 ns/iter 57.20 ns █ (53.62 ns … 84.69 ns) 73.21 ns █▅▆▅▅▃▃▂▂▁▁▁▁▁▁▁▁▁▁▁▁ 5.65 ipc ( 0.59% stalls) NaN% L1 data cache 197.74 cycles 1.12k instructions 0.00% retired LD/ST ( 0.00) 74.52 ns/iter - https://npmjs.com/mitata // vs other libraries a / b x 11,232,032 ops/sec ±0.50% (99 runs sampled) 89.03 ns/iter - https://npmjs.com/benchmark ┌─────────┬───────────┬──────────────────────┬─────────────────────┬────────────────────────────┬───────────────────────────┬─────────┐ │ (index) │ Task name │ Latency average (ns) │ Latency median (ns) │ Throughput average (ops/s) │ Throughput median (ops/s) │ Samples │ ├─────────┼───────────┼──────────────────────┼─────────────────────┼────────────────────────────┼───────────────────────────┼─────────┤ │ 0 │ 'a / b' │ '215.53 ± 0.08%' │ '208.00' │ '4786095 ± 0.01%' │ '4807692' │ 4639738 │ └─────────┴───────────┴──────────────────────┴─────────────────────┴────────────────────────────┴───────────────────────────┴─────────┘ 215.53 ns/iter - vitest bench / https://npmjs.com/tinybench a / b x 10,311,999 ops/sec (11 runs sampled) v8-never-optimize=true min..max=(95.66ns...97.51ns) 96.86 ns/iter - https://npmjs.com/bench-node ╔══════════════╤═════════╤═══════════════════╤═══════════╗ ║ Slower tests │ Samples │ Result │ Tolerance ║ ╟──────────────┼─────────┼───────────────────┼───────────╢ ║ Fastest test │ Samples │ Result │ Tolerance ║ ╟──────────────┼─────────┼───────────────────┼───────────╢ ║ a / b │ 2000 │ 4664908.00 op/sec │ ± 0.94 % ║ ╚══════════════╧═════════╧═══════════════════╧═══════════╝ 214.37 ns/iter - https://npmjs.com/cronometro ```

## writing good benchmarks Creating accurate and meaningful benchmarks requires careful attention to how modern JavaScript engines optimize code. This covers essential concepts and best practices to ensure your benchmarks measure actual performance characteristics rather than optimization artifacts. ### examples - [readme gif](/examples/gif.js) - [cpu cache line size](/examples/cacheline.js) - [holey vs packed arrays](/examples/holey_array.js) ### dead code elimination JIT can detect and eliminate code that has no observable effects. To ensure your benchmark code executes as intended, you must create observable side effects. ```js import { do_not_optimize } from 'mitata'; bench(function* () { // ❌ Bad: jit can see that function has zero side-effects yield () => new Array(0); // will get optimized to: /* yield () => {}; */ // ✅ Good: do_not_optimize(value) emits code that causes side-effects yield () => do_not_optimize(new Array(0)); }); ``` ### garbage collection pressure For benchmarks involving significant memory allocations, controlling garbage collection frequency can improve results consistency. ```js // ❌ Bad: unpredictable gc pauses bench(() => { const bigArray = new Array(1000000); }); // ✅ Good: gc before each (batch-)iteration bench(() => { const bigArray = new Array(1000000); }).gc('inner'); // run gc before each iteration ``` ### loop invariant code motion optimization JavaScript engines can optimize away repeated computations by hoisting them out of loops or caching results. Use computed parameters to prevent loop invariant code motion optimization. ```js bench(function* (ctx) { const str = 'abc'; // ❌ Bad: JIT sees that both str and 'c' search value are constants/comptime-known yield () => str.includes('c'); // will get optimized to: /* yield () => true; */ // ❌ Bad: JIT sees that computation doesn't depend on anything inside loop const substr = ctx.get('substr'); yield () => str.includes(substr); // will get optimized to: /* const $0 = str.includes(substr); yield () => $0; */ // ✅ Good: using computed parameters prevents jit from performing any loop optimizations yield { [0]() { return str; }, [1]() { return substr; }, bench(str, substr) { return do_not_optimize(str.includes(substr)); }, }; }).args('substr', ['c']); ``` ## License MIT © [evanwashere](https://github.com/evanwashere)