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| 1 | +import { EdgePathBundlingGPUFloydWarshall } from '@bachelor/core/edge-path-bundling/floyd-warshall/gpu'; |
| 2 | +import { initWebGPU } from '@bachelor/core/webGpu'; |
| 3 | +import { afterAll, describe, test } from 'vitest'; |
| 4 | +import { average, ITERATIONS, loadDatasets, writeResult, type CSV, type CSVRow } from './utils'; |
| 5 | + |
| 6 | +const datasets = await loadDatasets(); |
| 7 | + |
| 8 | +const steps = [ |
| 9 | + { parameter: 'edgeWeightFactor', value: 1 }, |
| 10 | + { parameter: 'maxDistortion', value: 1 }, |
| 11 | + { parameter: 'edgeWeightFactor', value: 3 }, |
| 12 | + { parameter: 'maxDistortion', value: 3 }, |
| 13 | +] as const; |
| 14 | + |
| 15 | +describe('Interactivity', () => { |
| 16 | + test.each(datasets)('%s', { repeats: ITERATIONS - 1 }, async (dataset, graph, times) => { |
| 17 | + const { device } = await initWebGPU(); |
| 18 | + |
| 19 | + const epb = new EdgePathBundlingGPUFloydWarshall({ |
| 20 | + device, |
| 21 | + graph, |
| 22 | + maxDistortion: 2, |
| 23 | + edgeWeightFactor: 2, |
| 24 | + }); |
| 25 | + |
| 26 | + const start = performance.now(); |
| 27 | + await epb.bundle(); |
| 28 | + const end = performance.now(); |
| 29 | + |
| 30 | + times.push(end - start); |
| 31 | + |
| 32 | + for await (const { parameter, value } of steps) { |
| 33 | + const start = performance.now(); |
| 34 | + epb[parameter] = value; |
| 35 | + await epb.bundle(); |
| 36 | + const end = performance.now(); |
| 37 | + |
| 38 | + times.push(end - start); |
| 39 | + } |
| 40 | + }); |
| 41 | +}); |
| 42 | + |
| 43 | +afterAll(async () => { |
| 44 | + const csv: CSV = []; |
| 45 | + |
| 46 | + const header: CSVRow = ['dataset']; |
| 47 | + |
| 48 | + header.push('average_initial'); |
| 49 | + for (const { parameter, value } of steps) { |
| 50 | + header.push(`average_${parameter}_${value}`); |
| 51 | + } |
| 52 | + |
| 53 | + for (let i = 0; i < ITERATIONS; i++) { |
| 54 | + header.push(`run_${i + 1}_initial`); |
| 55 | + |
| 56 | + for (const { parameter, value } of steps) { |
| 57 | + header.push(`run_${i + 1}_${parameter}_${value}`); |
| 58 | + } |
| 59 | + } |
| 60 | + |
| 61 | + csv.push(header); |
| 62 | + |
| 63 | + datasets.forEach(([dataset, graph, times]) => { |
| 64 | + const row: CSVRow = [dataset]; |
| 65 | + |
| 66 | + // Calculate average for each step |
| 67 | + for (let i = 0; i <= steps.length; i++) { |
| 68 | + const values: number[] = []; |
| 69 | + |
| 70 | + for (let j = i; j < times.length; j += steps.length + 1) { |
| 71 | + console.log({ i, j }); |
| 72 | + values.push(times[j]!); |
| 73 | + } |
| 74 | + |
| 75 | + row.push(average(values)); |
| 76 | + } |
| 77 | + |
| 78 | + times.forEach((time) => { |
| 79 | + row.push(time); |
| 80 | + }); |
| 81 | + |
| 82 | + csv.push(row); |
| 83 | + }); |
| 84 | + |
| 85 | + await writeResult('interactivity', csv); |
| 86 | +}); |
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