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Set up a performance testing pipeline
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| # Compile-Eval-Render Pipeline for Intervals | ||
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| > Summary of the render process that happens on every tick (pan/zoom) in the graph. | ||
| As of v1.24.0 the render pipeline consists of: | ||
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| - Compile + Eval | ||
| - compile `fn` to a *-eval function (see [interval-arithmetic-eval](https://github.com/mauriciopoppe/interval-arithmetic-eval) | ||
| or [built-in-math-eval](https://github.com/mauriciopoppe/built-in-math-eval)) | ||
| - The [above is cached]() so that when `functionPlot` is invoked it isn't | ||
| the compilation isn't run many times. | ||
| - [use `nSamples`](https://github.com/mauriciopoppe/function-plot/blob/b46e07c3281bce5b6bff00050ba3d6a16795a483/src/evaluate.ts#L40) | ||
| to create [`nSamples` equally distinct points](https://github.com/mauriciopoppe/function-plot/blob/b46e07c3281bce5b6bff00050ba3d6a16795a483/src/samplers/interval.ts#L17), | ||
| e.g. `[x_0, x_1, ..., x_{n_samples - 1}]` | ||
| - In `O(n)`, iterate over all the points and evaluate each them against the compiled function evaluator (created in the preparation stage), | ||
| return a data structure that encodes the result (`Array<Array<[Interval, Interval]> | null>`) | ||
| - Render | ||
| - In `O(n)` iterate over all the results and create a [`<path d={rectanglePaint} />`](https://github.com/mauriciopoppe/function-plot/blob/b46e07c3281bce5b6bff00050ba3d6a16795a483/src/graph-types/interval.ts#L96) | ||
| where `rectanglePaint` is a [series of commands](https://github.com/mauriciopoppe/function-plot/blob/b46e07c3281bce5b6bff00050ba3d6a16795a483/src/graph-types/interval.ts#L68) | ||
| of the form `M <x> <y> v <width>` which move to `(x, y)` and paint a rectangle of width `width`. | ||
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| ## Perf stats | ||
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| Using `npm run perf:pipeline`: | ||
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| ``` | ||
| compile and eval 1000 x 1,163 ops/sec ±0.26% (98 runs sampled) | ||
| compile and eval 1000 x 1,172 ops/sec ±0.46% (97 runs sampled) | ||
| compile and eval 1000 x 1,164 ops/sec ±0.23% (96 runs sampled) | ||
| drawPath 1000 x 8,398 ops/sec ±0.58% (99 runs sampled) | ||
| drawPath 1000 x 8,392 ops/sec ±0.53% (97 runs sampled) | ||
| drawPath 1000 x 8,266 ops/sec ±0.55% (96 runs sampled) | ||
| ``` |
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| /** | ||
| * interval-pipeline evaluates the performance of the compile, eval, render pipeline, | ||
| * the design is at /design/pipeline.md | ||
| */ | ||
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| // @ts-ignore | ||
| import Benchmark from 'benchmark' | ||
| import { scaleLinear } from 'd3-scale' | ||
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| import { FunctionPlotDatum, FunctionPlotOptionsAxis } from '../types' | ||
| import { createPathD } from '../graph-types/interval' | ||
| import interval from '../samplers/interval' | ||
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| function createData(nSamples: number) { | ||
| const width = 500 | ||
| const height = 300 | ||
| const xDomain: [number, number] = [-5, 5] | ||
| const yDomain: [number, number] = [-5, 5] | ||
| const xScale = scaleLinear().domain(xDomain).range([0, width]) | ||
| const yScale = scaleLinear().domain(yDomain).range([height, 0]) | ||
| const d: FunctionPlotDatum = { | ||
| fn: '1/x', | ||
| fnType: 'linear' | ||
| } | ||
| const xAxis: FunctionPlotOptionsAxis = { type: 'linear' } | ||
| const yAxis: FunctionPlotOptionsAxis = { type: 'linear' } | ||
| const samplerParams = { | ||
| d, | ||
| range: xDomain, | ||
| xScale, | ||
| yScale, | ||
| xAxis, | ||
| yAxis, | ||
| nSamples | ||
| } | ||
| const data = interval(samplerParams) | ||
| return { data, xScale, yScale } | ||
| } | ||
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| function compileAndEval() { | ||
| const compileAndEval = new Benchmark.Suite() | ||
| const nSamples = 1000 | ||
| compileAndEval | ||
| .add(`compile and eval ${nSamples}`, function () { | ||
| createData(nSamples) | ||
| }) | ||
| // add listeners | ||
| .on('cycle', function (event) { | ||
| console.log(String(event.target)) | ||
| }) | ||
| .on('complete', function () { | ||
| console.log('Fastest is ' + this.filter('fastest').map('name')) | ||
| }) | ||
| .run({ async: false }) | ||
| } | ||
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| function drawPath() { | ||
| const compileAndEval = new Benchmark.Suite() | ||
| const nSamples = 1000 | ||
| const { xScale, yScale, data } = createData(nSamples) | ||
| compileAndEval | ||
| .add(`drawPath ${nSamples}`, function () { | ||
| createPathD(xScale, yScale, 1 /* minWidthHeight, dummy = 1 */, data[0], false /* closed */) | ||
| }) | ||
| // add listeners | ||
| .on('cycle', function (event) { | ||
| console.log(String(event.target)) | ||
| }) | ||
| .on('complete', function () { | ||
| console.log('Fastest is ' + this.filter('fastest').map('name')) | ||
| }) | ||
| .run({ async: false }) | ||
| } | ||
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| function main() { | ||
| compileAndEval() | ||
| drawPath() | ||
| } | ||
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| main() |