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targostargos
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Merge branch 'improve-performance-mmul' of github.com:jobo322/sparse-matrix into improve-performance-mmul
2 parents ac26a10 + 87966e4 commit a3568a8

18 files changed

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-488
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‎benchmark/benchmark.js‎

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‎benchmark/benchmarkLinePlot.js‎

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import { run, bench, lineplot, do_not_optimize } from 'mitata';
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import { xSequentialFillFromStep } from 'ml-spectra-processing';
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import { SparseMatrix } from '../src/index.js';
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import { SparseMatrix as SparseMatrixOld } from './class/SparseMatrixOld.js';
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import { randomMatrix } from './utils/randomMatrix.js';
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const density = 0.02; // Fixed density for this comparison;
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/* eslint
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func-names: 0
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camelcase: 0
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*/
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// Prepare matrices once
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const sizes = Array.from(
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xSequentialFillFromStep({ from: 4, step: 4, size: 13 }),
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);
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lineplot(() => {
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bench('Sparse.mmul($size)', function* (ctx) {
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const size = ctx.get('size');
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// Prepare matrices once
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const A = new SparseMatrix(randomMatrix(size, size, density));
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const B = new SparseMatrix(randomMatrix(size, size, density));
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// Benchmark the multiplication
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yield () => do_not_optimize(A.mmul(B));
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})
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.gc('inner')
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.args('size', sizes); // 16, 32, 64, 128, 256
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bench('SparseOld.mmul($size)', function* (ctx) {
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const size = ctx.get('size');
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const A = randomMatrix(size, size, density);
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const B = randomMatrix(size, size, density);
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const AOld = new SparseMatrixOld(A);
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const BOld = new SparseMatrixOld(B);
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// Benchmark the multiplication
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yield () => do_not_optimize(AOld.mmul(BOld));
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})
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.gc('inner')
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.args('size', sizes);
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// bench('Dense.mmul($size)', function* (ctx) {
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// const size = ctx.get('size');
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// // Prepare matrices once
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// const A = randomMatrix(size, size, density);
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// const B = randomMatrix(size, size, density);
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// const ADense = new Matrix(A);
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// const BDense = new Matrix(B);
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// // Benchmark the multiplication
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// yield () => do_not_optimize(ADense.mmul(BDense));
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// }).gc('inner').range('size', min, max, multiplier);
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});
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await run();

‎benchmark/class/SparseMatrixOld.js‎

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*/
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mmul(other) {
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if (this.columns !== other.rows) {
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// eslint-disable-next-line no-console
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console.warn(
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'Number of columns of left matrix are not equal to number of rows of right matrix.',
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);

‎benchmark/combinationOfCardinalityAndSize.js‎

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import { run, bench, do_not_optimize, lineplot } from 'mitata';
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import { xSequentialFillFromStep } from 'ml-spectra-processing';
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import { SparseMatrix } from '../src/index.js';
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import { randomMatrix } from './utils/randomMatrix.js';
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import { writeFile } from 'node:fs/promises';
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import path from 'node:path';
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/* eslint
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func-names: 0
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camelcase: 0
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*/
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// Prepare matrices once
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const cardinalities = Array.from(
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xSequentialFillFromStep({ from: 10, step: 5, size: 2 }),
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);
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// const dimensions = Array.from(
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// xSequentialFillFromStep({ from: 700, step: 100, size: 13 }),
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// );
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const dimensions = [512];
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lineplot(() => {
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bench('hibrid($cardinality,$dimension)', function* (ctx) {
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const cardinality = ctx.get('cardinality');
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const size = ctx.get('dimension');
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// Prepare matrices once
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let A = new SparseMatrix(randomMatrix(size, size, cardinality));
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let B = new SparseMatrix(randomMatrix(size, size, cardinality));
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// Benchmark the multiplication
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yield () => do_not_optimize(A.mmul(B));
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do_not_optimize(A);
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do_not_optimize(B);
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})
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.gc('inner')
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.args('cardinality', cardinalities) //.range('size', 32, 1024, 2); //.args('size', sizes);
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.args('dimension', dimensions);
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bench('small($cardinality,$dimension)', function* (ctx) {
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const cardinality = ctx.get('cardinality');
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const size = ctx.get('dimension');
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// Prepare matrices once
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let A = new SparseMatrix(randomMatrix(size, size, cardinality));
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let B = new SparseMatrix(randomMatrix(size, size, cardinality));
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// Benchmark the multiplication
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yield () => do_not_optimize(A._mmulSmall(B));
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// Explicit cleanup
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do_not_optimize(A);
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do_not_optimize(B);
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})
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.gc('inner')
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.args('cardinality', cardinalities) //.range('size', 32, 1024, 2); //.args('size', sizes);
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.args('dimension', dimensions);
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bench('low($cardinality,$dimension)', function* (ctx) {
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const cardinality = ctx.get('cardinality');
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const size = ctx.get('dimension');
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// Prepare matrices once
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let A = new SparseMatrix(randomMatrix(size, size, cardinality));
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let B = new SparseMatrix(randomMatrix(size, size, cardinality));
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// Benchmark the multiplication
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yield () => do_not_optimize(A._mmulLowDensity(B));
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// Explicit cleanup
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do_not_optimize(A);
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do_not_optimize(B);
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})
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.gc('inner')
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.args('cardinality', cardinalities) //.range('size', 32, 1024, 2); //.args('size', sizes);
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.args('dimension', dimensions);
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bench('medium($cardinality,$dimension)', function* (ctx) {
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const cardinality = ctx.get('cardinality');
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const size = ctx.get('dimension');
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// Prepare matrices once
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let A = new SparseMatrix(randomMatrix(size, size, cardinality));
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let B = new SparseMatrix(randomMatrix(size, size, cardinality));
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// Benchmark the multiplication
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yield () => {
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do_not_optimize(A._mmulMediumDensity(B));
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};
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// Explicit cleanup
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do_not_optimize(A);
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do_not_optimize(B);
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})
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.gc('inner')
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.args('cardinality', cardinalities) //.range('size', 32, 1024, 2); //.args('size', sizes);
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.args('dimension', dimensions);
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});
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// Run benchmarks and capture results
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const results = await run({
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// Force GC between every benchmark
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gc: true,
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// // More samples for statistical significance
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// min_samples: 20,
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// max_samples: 200,
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// // Longer warmup to stabilize CPU state
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// warmup_samples: 10,
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// warmup_threshold: 100, // ms
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// Longer minimum time for stable measurements
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// min_cpu_time: 2000, // 2 seconds minimum
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// // Batch settings to reduce variance
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// batch_samples: 5,
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// batch_threshold: 10, // ms
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// // Enable colors
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// colors: true,
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});
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// Process and store results
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const processedResults = [];
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for (const benchmark of results.benchmarks) {
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for (const run of benchmark.runs) {
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if (run.stats) {
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processedResults.push({
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name: benchmark.alias,
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cardinality: run.args.cardinality,
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dimension: run.args.dimension,
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avg: run.stats.avg,
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min: run.stats.min,
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max: run.stats.max,
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p50: run.stats.p50,
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p75: run.stats.p75,
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p99: run.stats.p99,
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samples: run.stats.samples.length,
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ticks: run.stats.ticks,
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});
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}
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}
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}
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// Save results to JSON file
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await writeFile(
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path.join(import.meta.dirname, `benchmark-results.json`),
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JSON.stringify(processedResults, null, 2),
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);

‎benchmark/denseMatrix.js‎

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‎benchmark/densityDecreaseWhenSizeIncrease.js‎

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import { run, bench, group, do_not_optimize } from 'mitata';
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// import { Matrix } from 'ml-matrix';
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import { SparseMatrix as SparseMatrixOld } from './class/SparseMatrixOld.js';
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import { randomMatrix } from './utils/randomMatrix.js';
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import { SparseMatrix } from '../src/index.js';
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/* eslint
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func-names: 0
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camelcase: 0
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*/
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const sizes = [8, 16, 32, 256, 512, 1024];
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const densities = [0.125, 0.0625, 0.03125, 0.0039, 0.00197, 0.001];
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for (let i = 0; i < sizes.length; i++) {
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const size = sizes[i];
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const density = densities[i];
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const denseA = randomMatrix(size, size, density);
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const denseB = randomMatrix(size, size, density);
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const AOld = new SparseMatrixOld(denseA);
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const BOld = new SparseMatrixOld(denseB);
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const A = new SparseMatrix(denseA);
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const B = new SparseMatrix(denseB);
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group(`size:${size}-density:${density}`, () => {
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bench('mmulNew', () => {
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do_not_optimize(A.mmul(B));
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}).gc('inner');
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bench('mmul', () => {
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do_not_optimize(AOld.mmul(BOld));
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}).gc('inner');
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});
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}
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await run({ silent: false });

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