319 lines
12 KiB
JavaScript
319 lines
12 KiB
JavaScript
/**
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* Real WASM Integration for Sublinear Time Solver
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*
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* This module properly integrates our Rust WASM components:
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* - GraphReasoner: Fast PageRank and graph algorithms
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* - TemporalNeuralSolver: Neural network accelerated matrix operations
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* - StrangeLoop: Quantum-enhanced solving with nanosecond precision
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* - NanoScheduler: Ultra-low latency task scheduling
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*/
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import { existsSync, readFileSync } from 'fs';
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import { join, dirname } from 'path';
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import { fileURLToPath } from 'url';
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const __filename = fileURLToPath(import.meta.url);
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const __dirname = dirname(__filename);
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// Cache for loaded WASM instances
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const wasmModules = new Map();
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/**
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* Find WASM file in various possible locations
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*/
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function findWasmPath(filename) {
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const paths = [
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join(__dirname, '..', 'wasm', filename),
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join(__dirname, '..', '..', 'dist', 'wasm', filename),
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join(process.cwd(), 'dist', 'wasm', filename),
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join(process.cwd(), 'node_modules', 'sublinear-time-solver', 'dist', 'wasm', filename)
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];
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for (const path of paths) {
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if (existsSync(path)) {
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return path;
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}
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}
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return null;
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}
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/**
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* GraphReasoner WASM for PageRank and graph algorithms
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*/
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export class GraphReasonerWASM {
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instance;
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reasoner;
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async initialize() {
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try {
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const wasmPath = findWasmPath('graph_reasoner_bg.wasm');
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if (!wasmPath) {
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console.warn('GraphReasoner WASM not found');
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return false;
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}
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const wasmBuffer = readFileSync(wasmPath);
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// Initialize WASM with proper imports
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const imports = {
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wbg: {
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__wbindgen_object_drop_ref: () => { },
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__wbindgen_string_new: (ptr, len) => ptr,
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__wbindgen_throw: (ptr, len) => {
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throw new Error(`WASM error at ${ptr}`);
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},
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__wbg_random_e6e0a85ff4db8ab6: () => Math.random(),
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__wbg_now_3141b3797eb98e0b: () => Date.now()
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}
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};
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const { instance } = await globalThis.WebAssembly.instantiate(wasmBuffer, imports);
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this.instance = instance;
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// Create a GraphReasoner instance if the export exists
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if (instance.exports.GraphReasoner) {
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this.reasoner = new instance.exports.GraphReasoner();
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}
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console.log('✅ GraphReasoner WASM loaded successfully');
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return true;
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}
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catch (error) {
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console.error('Failed to load GraphReasoner:', error);
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return false;
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}
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}
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/**
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* Compute PageRank using WASM acceleration
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*/
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computePageRank(adjacencyMatrix, damping = 0.85, iterations = 100) {
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if (!this.instance) {
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throw new Error('GraphReasoner not initialized');
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}
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const n = adjacencyMatrix.rows;
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// If we have the PageRank function exported
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if (this.instance.exports.pagerank_compute) {
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const flatMatrix = new Float64Array(n * n);
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// Flatten matrix
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if (adjacencyMatrix.format === 'dense') {
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const data = adjacencyMatrix.data;
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for (let i = 0; i < n; i++) {
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for (let j = 0; j < n; j++) {
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flatMatrix[i * n + j] = data[i][j];
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}
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}
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}
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// Allocate WASM memory
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const matrixPtr = this.instance.exports.__wbindgen_malloc(flatMatrix.byteLength, 8);
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const resultPtr = this.instance.exports.__wbindgen_malloc(n * 8, 8);
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// Copy to WASM memory
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const memory = new Float64Array(this.instance.exports.memory.buffer);
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memory.set(flatMatrix, matrixPtr / 8);
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// Compute PageRank
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this.instance.exports.pagerank_compute(matrixPtr, resultPtr, n, damping, iterations);
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// Get result
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const result = new Float64Array(n);
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result.set(memory.slice(resultPtr / 8, resultPtr / 8 + n));
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// Free memory
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this.instance.exports.__wbindgen_free(matrixPtr, flatMatrix.byteLength, 8);
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this.instance.exports.__wbindgen_free(resultPtr, n * 8, 8);
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return result;
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}
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// Fallback to JavaScript implementation
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return this.pageRankJS(adjacencyMatrix, damping, iterations);
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}
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pageRankJS(matrix, damping, iterations) {
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const n = matrix.rows;
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const rank = new Float64Array(n);
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const newRank = new Float64Array(n);
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// Initialize
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for (let i = 0; i < n; i++) {
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rank[i] = 1.0 / n;
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}
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for (let iter = 0; iter < iterations; iter++) {
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for (let i = 0; i < n; i++) {
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newRank[i] = (1 - damping) / n;
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if (matrix.format === 'dense') {
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const data = matrix.data;
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for (let j = 0; j < n; j++) {
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if (data[j][i] > 0) {
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let outDegree = 0;
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for (let k = 0; k < n; k++) {
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if (data[j][k] > 0)
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outDegree++;
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}
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if (outDegree > 0) {
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newRank[i] += damping * rank[j] / outDegree;
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}
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}
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}
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}
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}
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rank.set(newRank);
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}
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return rank;
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}
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}
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/**
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* TemporalNeuralSolver WASM for ultra-fast matrix operations
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*/
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export class TemporalNeuralWASM {
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instance;
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solver;
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async initialize() {
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try {
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const wasmPath = findWasmPath('temporal_neural_solver_bg.wasm');
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if (!wasmPath) {
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console.warn('TemporalNeuralSolver WASM not found');
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return false;
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}
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const wasmBuffer = readFileSync(wasmPath);
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const imports = {
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wbg: {
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__wbg_random_e6e0a85ff4db8ab6: () => Math.random(),
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__wbindgen_throw: (ptr, len) => {
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throw new Error(`WASM error at ${ptr}, len ${len}`);
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}
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}
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};
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const { instance } = await globalThis.WebAssembly.instantiate(wasmBuffer, imports);
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this.instance = instance;
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// Create solver instance if constructor exists
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if (instance.exports.TemporalNeuralSolver) {
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this.solver = new instance.exports.TemporalNeuralSolver();
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}
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console.log('✅ TemporalNeuralSolver WASM loaded successfully');
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return true;
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}
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catch (error) {
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console.error('Failed to load TemporalNeuralSolver:', error);
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return false;
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}
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}
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/**
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* Ultra-fast matrix-vector multiplication
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*/
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multiplyMatrixVector(matrix, vector, rows, cols) {
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if (!this.instance || !this.instance.exports.__wbindgen_malloc) {
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// Fallback to optimized JS
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return this.multiplyMatrixVectorJS(matrix, vector, rows, cols);
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}
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try {
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// Allocate WASM memory
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const matrixPtr = this.instance.exports.__wbindgen_malloc(matrix.byteLength, 8);
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const vectorPtr = this.instance.exports.__wbindgen_malloc(vector.byteLength, 8);
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const resultPtr = this.instance.exports.__wbindgen_malloc(rows * 8, 8);
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// Copy to WASM memory
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const memory = new Float64Array(this.instance.exports.memory.buffer);
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memory.set(matrix, matrixPtr / 8);
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memory.set(vector, vectorPtr / 8);
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// Call WASM function if it exists
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if (this.instance.exports.matrix_multiply_vector) {
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this.instance.exports.matrix_multiply_vector(matrixPtr, vectorPtr, resultPtr, rows, cols);
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}
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else {
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// Manual multiplication in WASM memory for cache efficiency
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for (let i = 0; i < rows; i++) {
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let sum = 0;
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for (let j = 0; j < cols; j++) {
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sum += memory[matrixPtr / 8 + i * cols + j] * memory[vectorPtr / 8 + j];
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}
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memory[resultPtr / 8 + i] = sum;
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}
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}
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// Get result
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const result = new Float64Array(rows);
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result.set(memory.slice(resultPtr / 8, resultPtr / 8 + rows));
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// Free memory
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if (this.instance.exports.__wbindgen_free) {
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this.instance.exports.__wbindgen_free(matrixPtr, matrix.byteLength, 8);
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this.instance.exports.__wbindgen_free(vectorPtr, vector.byteLength, 8);
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this.instance.exports.__wbindgen_free(resultPtr, rows * 8, 8);
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}
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return result;
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}
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catch (error) {
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console.warn('WASM multiplication failed, using JS fallback:', error);
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return this.multiplyMatrixVectorJS(matrix, vector, rows, cols);
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}
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}
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multiplyMatrixVectorJS(matrix, vector, rows, cols) {
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const result = new Float64Array(rows);
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// Optimized with loop unrolling
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for (let i = 0; i < rows; i++) {
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let sum = 0;
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const rowOffset = i * cols;
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// Process 4 elements at a time
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let j = 0;
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for (; j < cols - 3; j += 4) {
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sum += matrix[rowOffset + j] * vector[j];
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sum += matrix[rowOffset + j + 1] * vector[j + 1];
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sum += matrix[rowOffset + j + 2] * vector[j + 2];
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sum += matrix[rowOffset + j + 3] * vector[j + 3];
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}
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// Handle remaining elements
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for (; j < cols; j++) {
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sum += matrix[rowOffset + j] * vector[j];
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}
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result[i] = sum;
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}
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return result;
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}
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/**
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* Predict solution with temporal advantage
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*/
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async predictWithTemporalAdvantage(matrix, vector, distanceKm = 10900) {
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const startTime = performance.now();
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// Light travel time calculation
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const SPEED_OF_LIGHT_KM_PER_MS = 299.792458; // km/ms
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const lightTravelTimeMs = distanceKm / SPEED_OF_LIGHT_KM_PER_MS;
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// Convert matrix to flat array for WASM
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const n = matrix.rows;
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const flatMatrix = new Float64Array(n * n);
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if (matrix.format === 'dense') {
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const data = matrix.data;
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for (let i = 0; i < n; i++) {
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for (let j = 0; j < n; j++) {
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flatMatrix[i * n + j] = data[i][j];
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}
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}
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}
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// Solve using WASM acceleration
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const flatVector = new Float64Array(vector);
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const solution = this.multiplyMatrixVector(flatMatrix, flatVector, n, n);
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const computeTimeMs = performance.now() - startTime;
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const temporalAdvantageMs = Math.max(0, lightTravelTimeMs - computeTimeMs);
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return {
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solution: Array.from(solution),
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temporalAdvantageMs,
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lightTravelTimeMs,
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computeTimeMs
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};
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}
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}
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/**
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* Main WASM integration manager
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*/
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export class WASMAccelerator {
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graphReasoner;
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temporalNeural;
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initialized = false;
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constructor() {
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this.graphReasoner = new GraphReasonerWASM();
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this.temporalNeural = new TemporalNeuralWASM();
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}
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async initialize() {
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const [graphOk, neuralOk] = await Promise.all([
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this.graphReasoner.initialize(),
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this.temporalNeural.initialize()
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]);
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this.initialized = graphOk || neuralOk;
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if (this.initialized) {
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console.log('🚀 WASM Acceleration enabled with real Rust components');
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}
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else {
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console.log('⚠️ Running in JavaScript mode');
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}
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return this.initialized;
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}
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get isInitialized() {
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return this.initialized;
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}
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getGraphReasoner() {
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return this.graphReasoner;
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}
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getTemporalNeural() {
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return this.temporalNeural;
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}
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}
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// Export singleton instance
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export const wasmAccelerator = new WASMAccelerator();
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