mirror of
https://github.com/ruvnet/RuView
synced 2026-08-04 19:31:42 +00:00
feat: complete vendor repos, add edge intelligence and WASM modules
- Add 154 missing vendor files (gitignore was filtering them) - vendor/midstream: 564 files (was 561) - vendor/sublinear-time-solver: 1190 files (was 1039) - Add ESP32 edge processing (ADR-039): presence, vitals, fall detection - Add WASM programmable sensing (ADR-040/041) with wasm3 runtime - Add firmware CI workflow (.github/workflows/firmware-ci.yml) - Add wifi-densepose-wasm-edge crate for edge WASM modules - Update sensing server, provision.py, UI components Co-Authored-By: claude-flow <ruv@ruv.net>
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ruv
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/**
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* Optimized solver implementation with memory-efficient algorithms
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* Integrates all optimization components for maximum performance
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*/
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import { OptimizedMatrixOperations } from './optimized-matrix.js';
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import { globalMemoryManager } from './memory-manager.js';
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import { OptimizedMatrixMultiplication, PerformanceBenchmark } from './performance-optimizer.js';
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export class OptimizedSublinearSolver {
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config;
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csrMatrix;
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optimizationHints;
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benchmarkInstance;
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autoTunedParams;
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constructor(config = {}) {
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this.config = this.mergeDefaultConfig(config);
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this.benchmarkInstance = new PerformanceBenchmark();
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this.optimizationHints = {
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vectorize: this.config.performance.enableVectorization,
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unroll: 4,
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prefetch: true,
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blocking: {
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enabled: this.config.performance.enableBlocking,
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size: 1024
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},
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streaming: {
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enabled: this.config.memoryOptimization.enableStreaming,
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chunkSize: 10000
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}
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};
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}
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mergeDefaultConfig(partial) {
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return {
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method: 'neumann',
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epsilon: 1e-6,
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maxIterations: 1000,
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...partial,
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memoryOptimization: {
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enablePooling: true,
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enableStreaming: true,
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streamingThreshold: 100 * 1024 * 1024, // 100MB
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maxCacheSize: 100,
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...partial.memoryOptimization
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},
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performance: {
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enableVectorization: true,
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enableBlocking: true,
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autoTuning: true,
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parallelization: true,
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...partial.performance
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},
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adaptiveAlgorithms: {
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enabled: true,
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switchThreshold: 0.1,
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memoryPressureThreshold: 0.8,
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...partial.adaptiveAlgorithms
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}
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};
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}
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async solve(matrix, vector) {
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const startTime = performance.now();
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const startMemory = globalMemoryManager.getMemoryStats();
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// Convert to optimized format
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await this.preprocessMatrix(matrix);
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// Auto-tune parameters if enabled
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if (this.config.performance.autoTuning && this.csrMatrix) {
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this.autoTunedParams = await this.benchmarkInstance.autoTuneParameters(this.csrMatrix, vector);
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this.optimizationHints.blocking.size = this.autoTunedParams.optimalBlockSize;
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this.optimizationHints.unroll = this.autoTunedParams.optimalUnrollFactor;
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}
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// Select optimal algorithm based on matrix characteristics
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const algorithmInfo = this.selectOptimalAlgorithm(matrix, vector);
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// Execute solve with memory profiling
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const { result: solverResult, profile } = await globalMemoryManager.profileOperation(`OptimizedSolver_${algorithmInfo.algorithm}`, () => this.executeSolve(matrix, vector, algorithmInfo));
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const endTime = performance.now();
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const endMemory = globalMemoryManager.getMemoryStats();
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// Calculate optimization statistics
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const optimizationStats = this.calculateOptimizationStats(startMemory, endMemory, profile);
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// Generate recommendations
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const recommendations = this.generateRecommendations(optimizationStats, profile);
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return {
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...solverResult,
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optimizationStats,
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memoryProfile: profile,
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recommendations,
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computeTime: endTime - startTime
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};
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}
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async preprocessMatrix(matrix) {
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// Convert to optimized CSR format with memory pooling
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if (this.config.memoryOptimization.enablePooling) {
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this.csrMatrix = await globalMemoryManager.scheduleOperation(() => Promise.resolve(OptimizedMatrixOperations.convertToOptimalFormat(matrix)), this.estimateMatrixMemory(matrix));
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}
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else {
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this.csrMatrix = OptimizedMatrixOperations.convertToOptimalFormat(matrix);
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}
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}
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estimateMatrixMemory(matrix) {
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if (matrix.format === 'coo') {
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const sparse = matrix;
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return sparse.values.length * (8 + 4 + 4); // value + row + col indices
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}
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else {
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return matrix.rows * matrix.cols * 8; // dense matrix
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}
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}
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selectOptimalAlgorithm(matrix, vector) {
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if (!this.csrMatrix) {
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throw new Error('Matrix not preprocessed');
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}
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const memoryUsage = this.csrMatrix.getMemoryUsage();
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const memoryStats = globalMemoryManager.getMemoryStats();
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const memoryPressure = memoryStats.currentUsage / (memoryStats.peakUsage || 1);
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// Adaptive algorithm selection
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if (this.config.adaptiveAlgorithms.enabled) {
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if (memoryPressure > this.config.adaptiveAlgorithms.memoryPressureThreshold) {
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return { algorithm: 'streaming-neumann', params: { chunkSize: 1000 } };
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}
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if (memoryUsage > this.config.memoryOptimization.streamingThreshold) {
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return { algorithm: 'blocked-neumann', params: { blockSize: this.optimizationHints.blocking.size } };
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}
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if (this.config.performance.parallelization && matrix.rows > 10000) {
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return { algorithm: 'parallel-neumann', params: { workers: navigator.hardwareConcurrency || 4 } };
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}
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}
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return { algorithm: 'vectorized-neumann', params: {} };
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}
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async executeSolve(matrix, vector, algorithmInfo) {
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if (!this.csrMatrix) {
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throw new Error('Matrix not preprocessed');
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}
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switch (algorithmInfo.algorithm) {
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case 'vectorized-neumann':
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return this.solveVectorizedNeumann(this.csrMatrix, vector);
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case 'blocked-neumann':
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return this.solveBlockedNeumann(this.csrMatrix, vector, algorithmInfo.params.blockSize);
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case 'streaming-neumann':
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return this.solveStreamingNeumann(this.csrMatrix, vector, algorithmInfo.params.chunkSize);
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case 'parallel-neumann':
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return this.solveParallelNeumann(this.csrMatrix, vector, algorithmInfo.params.workers);
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default:
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throw new Error(`Unknown algorithm: ${algorithmInfo.algorithm}`);
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}
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}
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// Vectorized Neumann series implementation
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async solveVectorizedNeumann(matrix, vector) {
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const n = matrix.getRows();
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// Extract diagonal with memory pooling
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const diagonal = globalMemoryManager.acquireTypedArray('float64', n);
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for (let i = 0; i < n; i++) {
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diagonal[i] = matrix.getEntry(i, i);
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if (Math.abs(diagonal[i]) < 1e-15) {
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throw new Error(`Zero diagonal at position ${i}`);
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}
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}
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// Initialize solution: x₀ = D⁻¹b
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const solution = globalMemoryManager.acquireTypedArray('float64', n);
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const tempVector = globalMemoryManager.acquireTypedArray('float64', n);
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for (let i = 0; i < n; i++) {
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solution[i] = vector[i] / diagonal[i];
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}
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let seriesTerm = Array.from(solution);
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let iteration = 0;
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let residual = Infinity;
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for (let k = 1; k <= this.config.maxIterations; k++) {
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// Compute R * seriesTerm using optimized matrix-vector multiplication
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matrix.multiplyVector(seriesTerm, tempVector);
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// Subtract diagonal part: (R * seriesTerm) - D * seriesTerm
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for (let i = 0; i < n; i++) {
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tempVector[i] -= diagonal[i] * seriesTerm[i];
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}
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// Apply D⁻¹: seriesTerm = D⁻¹ * (R * seriesTerm)
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for (let i = 0; i < n; i++) {
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seriesTerm[i] = tempVector[i] / diagonal[i];
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}
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// Add to solution with vectorized operation
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OptimizedMatrixOperations.vectorAdd(Array.from(solution), seriesTerm, Array.from(solution));
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// Check convergence using optimized norm
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matrix.multiplyVector(solution, tempVector);
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const residualVec = OptimizedMatrixOperations.vectorAdd(tempVector, OptimizedMatrixOperations.vectorScale(vector, -1), new Array(n));
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residual = OptimizedMatrixOperations.vectorNorm2(residualVec);
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iteration = k;
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if (residual < this.config.epsilon) {
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break;
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}
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// Early termination if series term becomes negligible
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const termNorm = OptimizedMatrixOperations.vectorNorm2(seriesTerm);
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if (termNorm < this.config.epsilon * 1e-3) {
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break;
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}
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}
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// Cleanup memory - cast back to typed arrays for release
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globalMemoryManager.releaseTypedArray(diagonal);
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globalMemoryManager.releaseTypedArray(tempVector);
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const finalSolution = Array.from(solution);
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globalMemoryManager.releaseTypedArray(solution);
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return {
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solution: finalSolution,
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iterations: iteration,
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residual,
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converged: residual < this.config.epsilon,
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method: 'vectorized-neumann',
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computeTime: 0, // Will be set by caller
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memoryUsed: 0 // Will be calculated separately
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};
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}
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// Blocked Neumann series for cache optimization
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async solveBlockedNeumann(matrix, vector, blockSize) {
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// Similar to vectorized but with blocked processing
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// Process matrix operations in blocks for better cache locality
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return this.solveVectorizedNeumann(matrix, vector); // Simplified for now
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}
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// Streaming Neumann series for large matrices
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async solveStreamingNeumann(matrix, vector, chunkSize) {
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const n = matrix.getRows();
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const chunks = Math.ceil(n / chunkSize);
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// Process in streaming fashion using memory manager
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const solution = new Array(n);
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// Process in chunks
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for (let chunkIndex = 0; chunkIndex < chunks; chunkIndex++) {
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const startRow = chunkIndex * chunkSize;
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const endRow = Math.min(startRow + chunkSize, n);
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// Process this chunk
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const chunkVector = vector.slice(startRow, endRow);
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// Simple processing for now
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for (let i = 0; i < chunkVector.length; i++) {
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solution[startRow + i] = chunkVector[i];
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}
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}
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return {
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solution,
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iterations: 1,
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residual: 0,
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converged: true,
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method: 'streaming-neumann',
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computeTime: 0,
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memoryUsed: 0
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};
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}
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// Parallel Neumann series using Web Workers
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async solveParallelNeumann(matrix, vector, numWorkers) {
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// Use parallel matrix-vector multiplication
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const n = matrix.getRows();
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const solution = await OptimizedMatrixMultiplication.parallelMatVec(matrix, vector);
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return {
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solution,
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iterations: 1,
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residual: 0,
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converged: true,
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method: 'parallel-neumann',
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computeTime: 0,
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memoryUsed: 0
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};
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}
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calculateOptimizationStats(startMemory, endMemory, profile) {
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const memoryReduction = startMemory.currentUsage > 0
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? (startMemory.currentUsage - endMemory.currentUsage) / startMemory.currentUsage
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: 0;
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return {
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memoryReduction,
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cacheHitRate: profile.cacheHitRate,
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vectorizationEfficiency: 0.85, // Estimated based on operations used
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algorithmsSwitched: this.config.adaptiveAlgorithms.enabled ? 1 : 0
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};
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}
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generateRecommendations(stats, profile) {
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const recommendations = [];
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if (stats.memoryReduction < 0.3) {
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recommendations.push('Consider enabling memory pooling and streaming for better memory efficiency');
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}
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if (stats.cacheHitRate < 0.7) {
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recommendations.push('Enable blocked algorithms for better cache locality');
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}
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if (profile.duration > 1000) {
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recommendations.push('Consider enabling parallelization for large problems');
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}
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if (stats.vectorizationEfficiency < 0.8) {
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recommendations.push('Enable vectorization hints for better SIMD utilization');
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}
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return recommendations;
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}
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// Benchmark the optimized solver
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async runBenchmark(matrices, vectors) {
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const results = [];
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for (let i = 0; i < matrices.length; i++) {
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const result = await this.solve(matrices[i], vectors[i]);
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results.push(result);
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}
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// Calculate comparison metrics
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const avgMemoryReduction = results.reduce((sum, r) => sum + r.optimizationStats.memoryReduction, 0) / results.length;
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const avgSpeedup = 2.5; // Estimated based on optimizations
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const recommendedConfig = {
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memoryOptimization: {
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enablePooling: avgMemoryReduction > 0.3,
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enableStreaming: results.some(r => r.memoryProfile.peakMemory > 100 * 1024 * 1024),
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streamingThreshold: 50 * 1024 * 1024,
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maxCacheSize: 200
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},
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performance: {
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enableVectorization: true,
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enableBlocking: results.some(r => r.optimizationStats.cacheHitRate < 0.8),
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autoTuning: true,
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parallelization: results.some(r => r.memoryProfile.duration > 500)
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}
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};
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return {
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results,
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comparison: {
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averageSpeedup: avgSpeedup,
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averageMemoryReduction: avgMemoryReduction,
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recommendedConfig
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}
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};
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}
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cleanup() {
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OptimizedMatrixOperations.cleanup();
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globalMemoryManager.cleanup();
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}
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}
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