mirror of
https://github.com/ruvnet/RuView
synced 2026-08-06 19:51:43 +00:00
feat: vendor midstream and sublinear-time-solver libraries
Add ruvnet/midstream (AIMDS real-time inference) and ruvnet/sublinear-time-solver (sublinear optimization algorithms) as vendored dependencies under vendor/. Co-Authored-By: claude-flow <ruv@ruv.net>
This commit is contained in:
ruv
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#!/usr/bin/env node
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/**
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* Test and benchmark the fast solver implementation
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* Goal: Beat Python benchmarks that show MCP Dense is 190x slower
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*/
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import { FastSolver, FastCSRMatrix } from './js/fast-solver.js';
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function testBasicSolver() {
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console.log('🧪 Testing Fast Solver Basic Functionality...\n');
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// Create a simple 2x2 test matrix
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const triplets = [
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[0, 0, 4.0], [0, 1, 1.0],
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[1, 0, 1.0], [1, 1, 3.0]
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];
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const matrix = FastCSRMatrix.fromTriplets(triplets, 2, 2);
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const b = [1.0, 2.0];
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const solver = new FastSolver();
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const result = solver.solve(matrix, b);
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console.log('Input matrix (2x2):');
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console.log(' [4.0, 1.0]');
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console.log(' [1.0, 3.0]');
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console.log(`Right-hand side: [${b.join(', ')}]`);
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console.log(`Solution: [${result.solution.map(x => x.toFixed(6)).join(', ')}]`);
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console.log(`Execution time: ${result.executionTime.toFixed(3)}ms`);
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console.log(`Method: ${result.method}`);
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// Verify solution
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const y = new Float64Array(2);
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matrix.multiplyVector(result.solution, y);
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const error = Math.sqrt((y[0] - b[0])**2 + (y[1] - b[1])**2);
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console.log(`Verification error: ${error.toFixed(2e-10)}`);
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console.log(error < 1e-8 ? '✅ PASSED' : '❌ FAILED');
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return error < 1e-8;
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}
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function benchmarkAgainstPython() {
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console.log('\n🏃 Benchmarking Against Python Baselines...\n');
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const solver = new FastSolver();
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// Test the critical sizes from the performance analysis
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const results = solver.benchmark([100, 1000]);
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console.log('\n📈 Summary Results:');
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console.log('Size\tTime(ms)\tPython(ms)\tSpeedup\tStatus');
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console.log('-'.repeat(50));
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let totalSpeedup = 0;
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let passedTests = 0;
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for (const result of results) {
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const status = result.speedup > 1.0 ? '✅ WIN' : '❌ LOSE';
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console.log(`${result.size}\t${result.timeMs.toFixed(1)}\t\t${result.pythonBaseline}\t\t${result.speedup.toFixed(1)}x\t${status}`);
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totalSpeedup += result.speedup;
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if (result.speedup > 1.0) passedTests++;
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}
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const avgSpeedup = totalSpeedup / results.length;
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console.log(`\nAverage speedup: ${avgSpeedup.toFixed(2)}x`);
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console.log(`Tests passed: ${passedTests}/${results.length}`);
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return { results, avgSpeedup, passedTests };
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}
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function testMemoryEfficiency() {
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console.log('\n💾 Testing Memory Efficiency...\n');
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const solver = new FastSolver();
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const startMemory = process.memoryUsage().heapUsed;
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// Test with 10K matrix (should use < 1MB according to targets)
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console.log('Creating 10,000x10,000 sparse matrix...');
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const { matrix, b } = solver.generateTestMatrix(10000, 0.0001); // Very sparse
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const afterMatrixMemory = process.memoryUsage().heapUsed;
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const matrixMemory = (afterMatrixMemory - startMemory) / 1024 / 1024; // MB
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console.log(`Matrix memory usage: ${matrixMemory.toFixed(2)} MB`);
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console.log(`Target: < 1 MB`);
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console.log(`NNZ: ${matrix.nnz.toLocaleString()}`);
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console.log(`Sparsity: ${(matrix.nnz / (10000 * 10000) * 100).toFixed(4)}%`);
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// Test solve
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console.log('\nSolving 10Kx10K system...');
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const startTime = process.hrtime.bigint();
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const result = solver.solve(matrix, b);
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const endTime = process.hrtime.bigint();
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const solveTime = Number(endTime - startTime) / 1e6;
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const finalMemory = process.memoryUsage().heapUsed;
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const totalMemory = (finalMemory - startMemory) / 1024 / 1024;
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console.log(`Solve time: ${solveTime.toFixed(1)}ms`);
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console.log(`Total memory: ${totalMemory.toFixed(2)} MB`);
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console.log(`Memory target: < 1 MB - ${totalMemory < 1.0 ? '✅ PASSED' : '❌ FAILED'}`);
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return { matrixMemory, totalMemory, solveTime, passed: totalMemory < 1.0 };
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}
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function testTargetPerformance() {
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console.log('\n🎯 Testing Target Performance Metrics...\n');
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const solver = new FastSolver();
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// Target: 100K×100K system solutions in < 150ms
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console.log('Testing 100K×100K performance target...');
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const { matrix, b } = solver.generateTestMatrix(100000, 0.00001); // Ultra sparse
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console.log(`Matrix size: ${matrix.rows}x${matrix.cols}`);
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console.log(`NNZ: ${matrix.nnz.toLocaleString()}`);
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console.log(`Sparsity: ${(matrix.nnz / (100000 * 100000) * 100).toFixed(6)}%`);
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const startTime = process.hrtime.bigint();
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const result = solver.solve(matrix, b);
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const endTime = process.hrtime.bigint();
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const timeMs = Number(endTime - startTime) / 1e6;
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const target = 150; // ms
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console.log(`Execution time: ${timeMs.toFixed(1)}ms`);
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console.log(`Target: < ${target}ms`);
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console.log(`Status: ${timeMs < target ? '✅ PASSED' : '❌ FAILED'}`);
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console.log(`Method: ${result.method}`);
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return { timeMs, target, passed: timeMs < target };
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}
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async function main() {
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console.log('🚀 Fast Solver Performance Validation');
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console.log('Targeting Python benchmark improvements');
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console.log('=' * 60);
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const results = {
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basic: false,
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benchmark: { avgSpeedup: 0, passedTests: 0 },
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memory: { passed: false },
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target: { passed: false }
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};
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try {
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// Basic functionality test
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results.basic = testBasicSolver();
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// Benchmark against Python
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const benchmarkResult = benchmarkAgainstPython();
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results.benchmark = benchmarkResult;
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// Memory efficiency test
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const memoryResult = testMemoryEfficiency();
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results.memory = memoryResult;
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// Target performance test
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const targetResult = testTargetPerformance();
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results.target = targetResult;
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// Summary
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console.log('\n🏆 FINAL RESULTS');
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console.log('=' * 60);
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console.log(`Basic functionality: ${results.basic ? '✅ PASS' : '❌ FAIL'}`);
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console.log(`Python benchmark: ${results.benchmark.avgSpeedup.toFixed(2)}x speedup (${results.benchmark.passedTests}/2 tests passed)`);
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console.log(`Memory efficiency: ${results.memory.passed ? '✅ PASS' : '❌ FAIL'}`);
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console.log(`Target performance: ${results.target.passed ? '✅ PASS' : '❌ FAIL'}`);
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const overallScore = (
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(results.basic ? 25 : 0) +
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(results.benchmark.passedTests * 12.5) +
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(results.memory.passed ? 25 : 0) +
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(results.target.passed ? 25 : 0)
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);
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console.log(`\nOverall Score: ${overallScore}/100`);
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if (overallScore >= 75) {
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console.log('🎉 EXCELLENT: Ready for production deployment!');
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} else if (overallScore >= 50) {
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console.log('⚠️ GOOD: Some optimizations still needed');
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} else {
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console.log('❌ NEEDS WORK: Significant performance improvements required');
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}
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} catch (error) {
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console.error('❌ Test failed with error:', error.message);
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console.error(error.stack);
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process.exit(1);
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}
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}
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main();
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