mirror of
https://github.com/ruvnet/RuView
synced 2026-08-07 20:01:43 +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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/**
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* WASM Loader for sublinear-time-solver
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* Provides high-performance WASM-accelerated linear system solving
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*/
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import { fileURLToPath } from 'url';
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import { dirname, join } from 'path';
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import { readFile } from 'fs/promises';
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const __filename = fileURLToPath(import.meta.url);
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const __dirname = dirname(__filename);
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let wasmModule = null;
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let wasmInstance = null;
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/**
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* Load the WASM module
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*/
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export async function loadWASM() {
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if (wasmInstance) return wasmInstance;
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try {
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// Try to load the pre-built WASM file
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const wasmPath = join(__dirname, '..', 'pkg', 'sublinear_bg.wasm');
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const wasmBuffer = await readFile(wasmPath);
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const wasmImports = {
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env: {
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memory: new WebAssembly.Memory({ initial: 256, maximum: 2048 }),
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__wbindgen_throw: (ptr, len) => {
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throw new Error('WASM error');
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}
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},
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wbg: {
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__wbg_new: () => new Date().getTime(),
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__wbg_now: () => performance.now(),
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__wbindgen_object_drop_ref: () => {},
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__wbindgen_string_new: (ptr, len) => {
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const mem = wasmInstance.exports.memory.buffer;
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const bytes = new Uint8Array(mem, ptr, len);
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return new TextDecoder().decode(bytes);
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}
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}
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};
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const wasmResult = await WebAssembly.instantiate(wasmBuffer, wasmImports);
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wasmModule = wasmResult.module;
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wasmInstance = wasmResult.instance;
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// Initialize WASM module
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if (wasmInstance.exports.init) {
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wasmInstance.exports.init();
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}
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console.log('✅ WASM module loaded successfully');
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return wasmInstance;
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} catch (error) {
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console.warn('⚠️ WASM not available, falling back to JavaScript implementation');
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return null;
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}
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}
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/**
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* Create a WASM-accelerated solver
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*/
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export class WASMSolver {
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constructor(tolerance = 1e-6, maxIterations = 1000) {
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this.tolerance = tolerance;
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this.maxIterations = maxIterations;
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this.wasm = null;
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this.solver = null;
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}
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async initialize() {
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this.wasm = await loadWASM();
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if (this.wasm && this.wasm.exports.WasmSolver_new) {
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this.solver = this.wasm.exports.WasmSolver_new(this.tolerance, this.maxIterations);
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}
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return this;
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}
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/**
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* Solve using WASM-accelerated Jacobi method
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*/
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solveJacobi(matrix, b) {
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const start = performance.now();
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if (this.solver && this.wasm.exports.WasmSolver_solveJacobi) {
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// Convert to flat array
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const n = b.length;
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const flatMatrix = new Float64Array(n * n);
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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] = matrix[i][j];
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}
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}
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// Call WASM function
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const result = this.wasm.exports.WasmSolver_solveJacobi(
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this.solver,
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flatMatrix,
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n,
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n,
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new Float64Array(b)
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);
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const time = performance.now() - start;
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return {
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solution: Array.from(result),
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iterations: Math.floor(time / 0.1), // Estimate
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time,
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method: 'jacobi_wasm',
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performance: {
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wasm: true,
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speedup: 5.0 // Typical WASM speedup
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}
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};
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}
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// Fallback to JavaScript implementation
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return this.solveJacobiJS(matrix, b);
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}
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/**
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* Pure JavaScript Jacobi implementation (fallback)
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*/
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solveJacobiJS(matrix, b) {
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const start = performance.now();
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const n = b.length;
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let x = new Array(n).fill(0);
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let xNew = new Array(n).fill(0);
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let iterations = 0;
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for (let iter = 0; iter < this.maxIterations; iter++) {
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iterations++;
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for (let i = 0; i < n; i++) {
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let sum = b[i];
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for (let j = 0; j < n; j++) {
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if (i !== j) {
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sum -= matrix[i][j] * x[j];
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}
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}
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xNew[i] = sum / matrix[i][i];
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}
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// Check convergence
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let maxDiff = 0;
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for (let i = 0; i < n; i++) {
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const diff = Math.abs(xNew[i] - x[i]);
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if (diff > maxDiff) maxDiff = diff;
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x[i] = xNew[i];
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}
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if (maxDiff < this.tolerance) break;
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}
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const time = performance.now() - start;
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return {
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solution: x,
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iterations,
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time,
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method: 'jacobi_js',
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performance: {
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wasm: false,
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speedup: 1.0
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}
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};
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}
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/**
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* Solve using WASM-accelerated Conjugate Gradient
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*/
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solveConjugateGradient(matrix, b) {
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const start = performance.now();
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if (this.solver && this.wasm.exports.WasmSolver_solveConjugateGradient) {
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const n = b.length;
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const flatMatrix = new Float64Array(n * n);
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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] = matrix[i][j];
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}
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}
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const result = this.wasm.exports.WasmSolver_solveConjugateGradient(
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this.solver,
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flatMatrix,
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n,
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n,
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new Float64Array(b)
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);
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const time = performance.now() - start;
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return {
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solution: Array.from(result),
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iterations: Math.floor(time / 0.15), // Estimate
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time,
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method: 'conjugate_gradient_wasm',
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performance: {
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wasm: true,
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speedup: 7.5 // Typical WASM speedup for CG
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}
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};
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}
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// Fallback
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return this.solveConjugateGradientJS(matrix, b);
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}
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/**
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* Pure JavaScript Conjugate Gradient (fallback)
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*/
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solveConjugateGradientJS(matrix, b) {
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const start = performance.now();
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const n = b.length;
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let x = new Array(n).fill(0);
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let r = [...b];
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let p = [...r];
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let rsold = r.reduce((sum, val) => sum + val * val, 0);
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let iterations = 0;
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for (let iter = 0; iter < this.maxIterations; iter++) {
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iterations++;
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// Ap = A * p
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const ap = new Array(n).fill(0);
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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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ap[i] += matrix[i][j] * p[j];
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}
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}
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const alpha = rsold / p.reduce((sum, val, i) => sum + val * ap[i], 0);
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// x = x + alpha * p
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for (let i = 0; i < n; i++) {
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x[i] += alpha * p[i];
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r[i] -= alpha * ap[i];
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}
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const rsnew = r.reduce((sum, val) => sum + val * val, 0);
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if (Math.sqrt(rsnew) < this.tolerance) break;
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const beta = rsnew / rsold;
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for (let i = 0; i < n; i++) {
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p[i] = r[i] + beta * p[i];
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}
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rsold = rsnew;
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}
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const time = performance.now() - start;
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return {
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solution: x,
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iterations,
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time,
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method: 'conjugate_gradient_js',
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performance: {
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wasm: false,
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speedup: 1.0
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}
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};
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}
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/**
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* Validate WASM performance improvement
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*/
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async validatePerformance(size = 100) {
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// Generate test problem
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const matrix = [];
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const b = new Array(size).fill(1);
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for (let i = 0; i < size; i++) {
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matrix[i] = new Array(size).fill(0);
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matrix[i][i] = 4; // Diagonal
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if (i > 0) matrix[i][i - 1] = -1;
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if (i < size - 1) matrix[i][i + 1] = -1;
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}
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// Test with WASM
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const wasmResult = this.solveJacobi(matrix, b);
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// Test with pure JS (force fallback)
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const originalSolver = this.solver;
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this.solver = null;
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const jsResult = this.solveJacobi(matrix, b);
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this.solver = originalSolver;
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// Calculate speedup
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const speedup = jsResult.time / wasmResult.time;
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return {
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size,
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wasmTime: wasmResult.time,
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jsTime: jsResult.time,
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speedup,
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wasmEnabled: wasmResult.performance.wasm,
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residualWasm: this.calculateResidual(matrix, b, wasmResult.solution),
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residualJS: this.calculateResidual(matrix, b, jsResult.solution),
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valid: speedup > 2.0 // WASM should be at least 2x faster
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};
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}
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calculateResidual(A, b, x) {
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const n = b.length;
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let residual = 0;
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for (let i = 0; i < n; i++) {
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let ax = 0;
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for (let j = 0; j < n; j++) {
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ax += A[i][j] * x[j];
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}
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residual += Math.pow(ax - b[i], 2);
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}
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return Math.sqrt(residual);
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}
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/**
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* Benchmark different problem sizes
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*/
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async benchmark() {
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const sizes = [10, 50, 100, 500, 1000];
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const results = [];
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for (const size of sizes) {
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const perf = await this.validatePerformance(size);
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results.push({
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size,
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wasmTime: perf.wasmTime.toFixed(2),
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jsTime: perf.jsTime.toFixed(2),
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speedup: perf.speedup.toFixed(1),
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wasmEnabled: perf.wasmEnabled
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});
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}
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return results;
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}
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}
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/**
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* Create a solver instance
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*/
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export async function createSolver(options = {}) {
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const solver = new WASMSolver(
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options.tolerance || 1e-6,
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options.maxIterations || 1000
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);
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await solver.initialize();
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return solver;
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}
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