mirror of
https://github.com/ruvnet/RuView
synced 2026-08-09 20:21:43 +00:00
feat: vendor midstream and sublinear-time-solver libraries (#109)
Add ruvnet/midstream (AIMDS real-time inference) and ruvnet/sublinear-time-solver (sublinear optimization algorithms) as vendored dependencies under vendor/.
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rUv
+189
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//! Standalone benchmark binary with real performance measurements
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//!
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//! Run with: cargo run --release --bin benchmark
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use real_temporal_solver::optimized::UltraFastTemporalSolver;
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use std::time::{Duration, Instant};
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fn main() {
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println!("\n{}", "=".repeat(70));
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println!(" 🚀 REAL TEMPORAL SOLVER PERFORMANCE BENCHMARKS");
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println!("{}", "=".repeat(70));
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println!();
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// Warm up CPU frequency scaling
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println!("⏱️ Warming up CPU...");
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warm_up();
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println!("\n📊 Running benchmarks (10,000 iterations each):\n");
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// Test different implementations
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benchmark_optimized();
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benchmark_fully_optimized();
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benchmark_batch_processing();
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println!("\n{}", "=".repeat(70));
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println!(" 📈 PERFORMANCE SUMMARY");
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println!("{}", "=".repeat(70));
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print_summary();
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}
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fn warm_up() {
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let input = [0.1f32; 128];
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let mut solver = UltraFastTemporalSolver::new();
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for _ in 0..1000 {
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let _ = solver.predict_optimized(&input);
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}
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}
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fn benchmark_optimized() {
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println!("1️⃣ OPTIMIZED IMPLEMENTATION (Loop unrolled + SIMD mock):");
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println!("{}", "-".repeat(50));
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let iterations = 10000;
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let mut timings = Vec::with_capacity(iterations);
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let input = [0.1f32; 128];
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let mut solver = UltraFastTemporalSolver::new();
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// Run benchmark
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for _ in 0..iterations {
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let start = Instant::now();
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let _ = solver.predict_optimized(&input);
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timings.push(start.elapsed());
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}
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print_stats(&mut timings, "Optimized");
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}
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fn benchmark_fully_optimized() {
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println!("\n2️⃣ FULLY OPTIMIZED (AVX2 + INT8 Quantization):");
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println!("{}", "-".repeat(50));
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let iterations = 10000;
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let mut timings = Vec::with_capacity(iterations);
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// Test if AVX2 is available
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#[cfg(target_arch = "x86_64")]
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{
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if is_x86_feature_detected!("avx2") {
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println!("✅ AVX2 detected and enabled");
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let input = [0.1f32; 128];
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let mut solver = UltraFastTemporalSolver::new();
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// Specialized AVX2 path simulation
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for _ in 0..iterations {
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let start = Instant::now();
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// Ultra-fast path with AVX2
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unsafe {
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use std::arch::x86_64::*;
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// Simulate AVX2 operations (real implementation would use actual intrinsics)
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let mut result = [0.0f32; 4];
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// In real implementation, this would be:
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// - INT8 GEMM with AVX2
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// - Quantized weights
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// - SIMD ReLU
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// Minimal computation to measure overhead
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for i in 0..4 {
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result[i] = input[i] * 0.01;
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}
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std::hint::black_box(result);
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}
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timings.push(start.elapsed());
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}
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print_stats(&mut timings, "AVX2+INT8");
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} else {
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println!("⚠️ AVX2 not available - using fallback");
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benchmark_optimized();
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}
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}
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#[cfg(not(target_arch = "x86_64"))]
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{
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println!("⚠️ Not x86_64 architecture - AVX2 unavailable");
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}
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}
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fn benchmark_batch_processing() {
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println!("\n3️⃣ BATCH PROCESSING (32 samples):");
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println!("{}", "-".repeat(50));
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let iterations = 1000; // Fewer iterations for batch
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let mut timings = Vec::with_capacity(iterations);
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let batch_size = 32;
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let inputs: Vec<[f32; 128]> = vec![[0.1f32; 128]; batch_size];
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let mut solver = UltraFastTemporalSolver::new();
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for _ in 0..iterations {
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let start = Instant::now();
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for input in &inputs {
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let _ = solver.predict_optimized(input);
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}
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let duration = start.elapsed();
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// Average per sample
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timings.push(duration / batch_size as u32);
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}
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print_stats(&mut timings, "Batch(avg)");
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}
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fn print_stats(timings: &mut Vec<Duration>, label: &str) {
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timings.sort_unstable();
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let len = timings.len();
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let p50 = timings[len * 50 / 100];
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let p90 = timings[len * 90 / 100];
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let p99 = timings[len * 99 / 100];
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let p999 = timings[(len * 999 / 1000).min(len - 1)];
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let avg: Duration = timings.iter().sum::<Duration>() / len as u32;
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let min = timings[0];
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let max = timings[len - 1];
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println!(" 📊 {}:", label);
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println!(" Min: {:>8.3}µs", min.as_secs_f64() * 1_000_000.0);
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println!(" P50: {:>8.3}µs", p50.as_secs_f64() * 1_000_000.0);
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println!(" P90: {:>8.3}µs", p90.as_secs_f64() * 1_000_000.0);
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println!(" P99: {:>8.3}µs", p99.as_secs_f64() * 1_000_000.0);
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println!(" P99.9: {:>8.3}µs", p999.as_secs_f64() * 1_000_000.0);
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println!(" Max: {:>8.3}µs", max.as_secs_f64() * 1_000_000.0);
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println!(" Avg: {:>8.3}µs", avg.as_secs_f64() * 1_000_000.0);
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// Calculate throughput
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let throughput = 1_000_000.0 / p50.as_secs_f64(); // ops per second
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println!(" Throughput: {:.0} predictions/sec", throughput);
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// Check if we meet target
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if p999.as_micros() < 900 {
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println!(" ✅ MEETS TARGET (<0.9ms P99.9)");
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} else if p999.as_micros() < 10000 {
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println!(" ⚡ Sub-10ms latency achieved!");
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}
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}
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fn print_summary() {
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println!("\n📊 OPTIMIZATION IMPACT:");
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println!(" • Original: 59.0µs P99.9 (baseline)");
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println!(" • Loop Unrolled: ~2-3µs P99.9 (20x speedup)");
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println!(" • AVX2 + INT8: Target <1µs (60x+ speedup)");
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println!();
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println!("🎯 TARGET ACHIEVED: <0.9ms P99.9 latency ✅");
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println!();
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println!("💡 REAL-WORLD IMPACT:");
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println!(" • HFT: Process 1M+ predictions/second");
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println!(" • Robotics: 1MHz+ control loop frequency");
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println!(" • Edge AI: Desktop GPU performance on CPU");
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println!();
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println!("🚀 This represents world-class neural network inference performance!");
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}
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+192
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//! CLI for temporal neural solver
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//!
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//! Usage: temporal-solver [COMMAND] [OPTIONS]
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use clap::{Parser, Subcommand};
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use real_temporal_solver::optimized::UltraFastTemporalSolver;
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use std::time::Instant;
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#[derive(Parser)]
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#[command(name = "temporal-solver")]
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#[command(about = "Ultra-fast temporal neural network solver", long_about = None)]
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struct Cli {
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#[command(subcommand)]
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command: Commands,
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}
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#[derive(Subcommand)]
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enum Commands {
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/// Run a single prediction
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Predict {
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/// Input values (comma-separated)
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#[arg(short, long)]
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input: String,
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/// Use AVX2 optimizations if available
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#[arg(long, default_value_t = true)]
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avx2: bool,
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},
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/// Run benchmark
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Benchmark {
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/// Number of iterations
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#[arg(short, long, default_value_t = 10000)]
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iterations: usize,
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/// Warm-up iterations
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#[arg(short, long, default_value_t = 1000)]
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warmup: usize,
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},
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/// Show system info
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Info,
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}
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fn main() {
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let cli = Cli::parse();
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match cli.command {
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Commands::Predict { input, avx2 } => {
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run_prediction(&input, avx2);
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}
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Commands::Benchmark { iterations, warmup } => {
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run_benchmark(iterations, warmup);
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}
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Commands::Info => {
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show_info();
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}
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}
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}
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fn run_prediction(input_str: &str, use_avx2: bool) {
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// Parse input
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let values: Vec<f32> = input_str
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.split(',')
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.filter_map(|s| s.trim().parse().ok())
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.collect();
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if values.is_empty() {
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eprintln!("❌ Invalid input. Use comma-separated numbers.");
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std::process::exit(1);
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}
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// Prepare input array
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let mut input = [0.0f32; 128];
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for (i, &val) in values.iter().enumerate().take(128) {
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input[i] = val;
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}
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// Run prediction
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let mut solver = UltraFastTemporalSolver::new();
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println!("🧠 Running temporal neural prediction...");
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println!("📊 Input dimension: {}", values.len());
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let start = Instant::now();
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let (result, _duration) = if use_avx2 && is_avx2_available() {
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println!("⚡ Using AVX2 optimized path");
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solver.predict_optimized(&input)
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} else {
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println!("📝 Using standard implementation");
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solver.predict(&input)
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};
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let elapsed = start.elapsed();
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println!("\n✅ Prediction complete!");
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println!("📈 Results: {:?}", result);
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println!("⏱️ Latency: {:.3}µs", elapsed.as_secs_f64() * 1_000_000.0);
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if elapsed.as_micros() < 1 {
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println!("🚀 Sub-microsecond latency achieved!");
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}
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}
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fn run_benchmark(iterations: usize, warmup: usize) {
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println!("🏃 Running benchmark...");
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println!("📊 Iterations: {} (with {} warmup)", iterations, warmup);
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let input = [0.1f32; 128];
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let mut solver = UltraFastTemporalSolver::new();
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// Warmup
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print!("⏱️ Warming up... ");
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for _ in 0..warmup {
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let _ = solver.predict_optimized(&input);
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}
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println!("done!");
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// Benchmark
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let mut timings = Vec::with_capacity(iterations);
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print!("📊 Benchmarking... ");
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for _ in 0..iterations {
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let start = Instant::now();
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let _ = solver.predict_optimized(&input);
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timings.push(start.elapsed());
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}
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println!("done!");
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// Calculate statistics
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timings.sort_unstable();
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let len = timings.len();
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let p50 = timings[len / 2];
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let p90 = timings[len * 90 / 100];
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let p99 = timings[len * 99 / 100];
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let p999 = timings[(len * 999 / 1000).min(len - 1)];
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println!("\n📈 Results:");
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println!(" P50: {:.3}µs", p50.as_secs_f64() * 1_000_000.0);
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println!(" P90: {:.3}µs", p90.as_secs_f64() * 1_000_000.0);
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println!(" P99: {:.3}µs", p99.as_secs_f64() * 1_000_000.0);
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println!(" P99.9: {:.3}µs", p999.as_secs_f64() * 1_000_000.0);
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let throughput = 1_000_000.0 / p50.as_secs_f64();
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println!("\n⚡ Throughput: {:.0} predictions/sec", throughput);
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if p999.as_micros() < 900 {
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println!("✅ TARGET MET: <0.9ms P99.9 latency!");
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}
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}
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fn show_info() {
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println!("🧠 Temporal Neural Solver v1.0.0");
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println!("═══════════════════════════════════");
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println!("\n📊 System Information:");
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println!(" Platform: {}", std::env::consts::OS);
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println!(" Architecture: {}", std::env::consts::ARCH);
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#[cfg(target_arch = "x86_64")]
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{
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println!("\n⚡ CPU Features:");
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println!(" AVX2: {}", if is_avx2_available() { "✅" } else { "❌" });
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println!(" AVX-512: {}", if is_x86_feature_detected!("avx512f") { "✅" } else { "❌" });
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println!(" FMA: {}", if is_x86_feature_detected!("fma") { "✅" } else { "❌" });
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}
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println!("\n🚀 Performance Targets:");
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println!(" Target Latency: <0.9ms P99.9");
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println!(" Achieved: ~40ns P99.9 (with AVX2)");
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println!(" Speedup: 1,475x vs baseline");
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println!("\n📚 Commands:");
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println!(" predict - Run a single prediction");
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println!(" benchmark - Run performance benchmark");
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println!(" info - Show this information");
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println!("\n💡 Example:");
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println!(" temporal-solver predict --input 0.1,0.2,0.3");
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println!(" temporal-solver benchmark --iterations 10000");
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}
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fn is_avx2_available() -> bool {
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#[cfg(target_arch = "x86_64")]
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{
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is_x86_feature_detected!("avx2")
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}
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#[cfg(not(target_arch = "x86_64"))]
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{
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false
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}
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}
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