866 lines
31 KiB
Rust
866 lines
31 KiB
Rust
//! Hardware-level timing validation
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//!
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//! CRITICAL VALIDATION: Use CPU cycle counters and hardware-level timing
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//! to verify that the <0.9ms latency claims are real and not artificially
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//! manipulated through software delays or system clock manipulation.
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use std::arch::x86_64::_rdtsc;
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use std::time::{Duration, Instant, SystemTime, UNIX_EPOCH};
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use std::sync::atomic::{AtomicU64, Ordering};
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use std::collections::VecDeque;
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use nalgebra::{DMatrix, DVector};
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use serde::{Deserialize, Serialize};
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/// Hardware timing measurement result
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct HardwareTimingResult {
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pub system_name: String,
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pub cpu_cycles: CycleTimingStats,
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pub wall_clock: WallClockStats,
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pub monotonic_time: MonotonicStats,
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pub cross_validation: TimingCrossValidation,
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pub red_flags: Vec<TimingRedFlag>,
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pub cpu_info: CpuInfo,
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pub measurement_quality: MeasurementQuality,
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}
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/// CPU cycle timing statistics
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct CycleTimingStats {
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pub mean_cycles: f64,
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pub std_dev_cycles: f64,
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pub p50_cycles: u64,
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pub p90_cycles: u64,
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pub p99_cycles: u64,
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pub p99_9_cycles: u64,
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pub min_cycles: u64,
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pub max_cycles: u64,
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pub cpu_freq_mhz: f64,
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pub mean_time_ns: f64,
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pub p99_9_time_ns: f64,
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}
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/// Wall clock timing statistics
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct WallClockStats {
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pub mean_ns: f64,
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pub std_dev_ns: f64,
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pub p99_9_ns: f64,
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pub timer_resolution_ns: f64,
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pub clock_source: String,
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}
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/// Monotonic clock statistics
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct MonotonicStats {
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pub mean_ns: f64,
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pub std_dev_ns: f64,
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pub p99_9_ns: f64,
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pub monotonic_violations: usize,
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}
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/// Cross-validation between timing methods
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct TimingCrossValidation {
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pub cycle_vs_wall_correlation: f64,
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pub cycle_vs_monotonic_correlation: f64,
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pub wall_vs_monotonic_correlation: f64,
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pub max_discrepancy_percent: f64,
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pub consistency_score: f64,
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}
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/// Detected timing red flags
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct TimingRedFlag {
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pub flag_type: TimingRedFlagType,
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pub severity: RedFlagSeverity,
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pub description: String,
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pub evidence: String,
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pub confidence: f64,
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}
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub enum TimingRedFlagType {
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ArtificialDelay,
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ClockManipulation,
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InaccurateTiming,
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SuspiciousVariance,
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ImpossibleLatency,
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TimingInconsistency,
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HardcodedValues,
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}
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub enum RedFlagSeverity {
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Critical,
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High,
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Medium,
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Low,
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}
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/// CPU information
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct CpuInfo {
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pub model_name: String,
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pub base_frequency_mhz: f64,
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pub boost_frequency_mhz: f64,
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pub cache_sizes: Vec<String>,
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pub features: Vec<String>,
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pub timestamp_counter_reliable: bool,
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}
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/// Measurement quality assessment
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct MeasurementQuality {
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pub sample_count: usize,
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pub outlier_rate: f64,
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pub noise_level: f64,
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pub thermal_stability: f64,
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pub frequency_stability: f64,
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pub overall_confidence: f64,
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}
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/// Hardware timing validator
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pub struct HardwareTimingValidator {
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cpu_freq_mhz: f64,
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baseline_noise: f64,
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thermal_monitor: ThermalMonitor,
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}
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/// Monitor for thermal throttling
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struct ThermalMonitor {
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recent_frequencies: VecDeque<f64>,
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frequency_checks: AtomicU64,
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}
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impl ThermalMonitor {
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fn new() -> Self {
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Self {
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recent_frequencies: VecDeque::with_capacity(100),
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frequency_checks: AtomicU64::new(0),
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}
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}
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fn record_frequency(&mut self, freq_mhz: f64) {
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self.recent_frequencies.push_back(freq_mhz);
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if self.recent_frequencies.len() > 100 {
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self.recent_frequencies.pop_front();
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}
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}
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fn get_frequency_stability(&self) -> f64 {
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if self.recent_frequencies.len() < 10 {
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return 1.0;
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}
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let mean: f64 = self.recent_frequencies.iter().sum::<f64>() / self.recent_frequencies.len() as f64;
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let variance: f64 = self.recent_frequencies.iter()
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.map(|f| (f - mean).powi(2))
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.sum::<f64>() / self.recent_frequencies.len() as f64;
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let cv = variance.sqrt() / mean;
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1.0 / (1.0 + cv * 10.0) // Lower coefficient of variation = higher stability
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}
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}
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impl HardwareTimingValidator {
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/// Create new hardware timing validator
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pub fn new() -> Result<Self, Box<dyn std::error::Error>> {
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let cpu_freq = Self::detect_cpu_frequency()?;
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let baseline_noise = Self::measure_baseline_noise()?;
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Ok(Self {
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cpu_freq_mhz: cpu_freq,
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baseline_noise,
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thermal_monitor: ThermalMonitor::new(),
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})
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}
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/// Validate System A with hardware-level timing
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pub fn validate_system_a(&mut self, iterations: usize) -> Result<HardwareTimingResult, Box<dyn std::error::Error>> {
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println!("🔬 Hardware timing validation: System A ({} iterations)", iterations);
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let mut cycle_measurements = Vec::with_capacity(iterations);
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let mut wall_clock_measurements = Vec::with_capacity(iterations);
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let mut monotonic_measurements = Vec::with_capacity(iterations);
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// Warmup phase
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self.warmup_cpu(1000)?;
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for i in 0..iterations {
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// Generate test input
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let input = self.generate_test_input();
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// Measure with multiple timing methods
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let (cycles, wall_ns, monotonic_ns) = self.measure_system_a_hardware(&input)?;
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cycle_measurements.push(cycles);
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wall_clock_measurements.push(wall_ns);
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monotonic_measurements.push(monotonic_ns);
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// Monitor thermal state
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if i % 100 == 0 {
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let current_freq = Self::estimate_current_frequency(cycles, wall_ns)?;
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self.thermal_monitor.record_frequency(current_freq);
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}
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// Progress indicator
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if i % 1000 == 0 && i > 0 {
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println!(" Progress: {}/{}", i, iterations);
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}
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}
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self.analyze_measurements("System A", cycle_measurements, wall_clock_measurements, monotonic_measurements)
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}
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/// Validate System B with hardware-level timing
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pub fn validate_system_b(&mut self, iterations: usize) -> Result<HardwareTimingResult, Box<dyn std::error::Error>> {
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println!("🚀 Hardware timing validation: System B ({} iterations)", iterations);
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let mut cycle_measurements = Vec::with_capacity(iterations);
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let mut wall_clock_measurements = Vec::with_capacity(iterations);
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let mut monotonic_measurements = Vec::with_capacity(iterations);
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// Warmup phase
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self.warmup_cpu(1000)?;
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for i in 0..iterations {
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let input = self.generate_test_input();
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let (cycles, wall_ns, monotonic_ns) = self.measure_system_b_hardware(&input)?;
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cycle_measurements.push(cycles);
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wall_clock_measurements.push(wall_ns);
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monotonic_measurements.push(monotonic_ns);
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if i % 100 == 0 {
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let current_freq = Self::estimate_current_frequency(cycles, wall_ns)?;
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self.thermal_monitor.record_frequency(current_freq);
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}
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if i % 1000 == 0 && i > 0 {
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println!(" Progress: {}/{}", i, iterations);
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}
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}
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self.analyze_measurements("System B", cycle_measurements, wall_clock_measurements, monotonic_measurements)
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}
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/// Measure System A with hardware timing
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fn measure_system_a_hardware(&self, input: &DMatrix<f64>) -> Result<(u64, u64, u64), Box<dyn std::error::Error>> {
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// CPU cycle measurement
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let cycle_start = unsafe { _rdtsc() };
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let wall_start = Instant::now();
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let mono_start = self.monotonic_time_ns();
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// CRITICAL: Call actual System A implementation
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let _result = self.system_a_predict(input)?;
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let cycle_end = unsafe { _rdtsc() };
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let wall_elapsed = wall_start.elapsed();
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let mono_end = self.monotonic_time_ns();
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let cycles = cycle_end - cycle_start;
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let wall_ns = wall_elapsed.as_nanos() as u64;
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let mono_ns = mono_end - mono_start;
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Ok((cycles, wall_ns, mono_ns))
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}
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/// Measure System B with hardware timing
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fn measure_system_b_hardware(&self, input: &DMatrix<f64>) -> Result<(u64, u64, u64), Box<dyn std::error::Error>> {
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let cycle_start = unsafe { _rdtsc() };
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let wall_start = Instant::now();
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let mono_start = self.monotonic_time_ns();
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// CRITICAL: Call actual System B implementation
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let _result = self.system_b_predict(input)?;
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let cycle_end = unsafe { _rdtsc() };
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let wall_elapsed = wall_start.elapsed();
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let mono_end = self.monotonic_time_ns();
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let cycles = cycle_end - cycle_start;
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let wall_ns = wall_elapsed.as_nanos() as u64;
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let mono_ns = mono_end - mono_start;
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Ok((cycles, wall_ns, mono_ns))
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}
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/// System A prediction (placeholder - should call real implementation)
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fn system_a_predict(&self, input: &DMatrix<f64>) -> Result<DVector<f64>, Box<dyn std::error::Error>> {
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// CRITICAL CHECK: Is this the real implementation or a mock?
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// Simulate realistic computation
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let mut computation_load = 0.0;
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for i in 0..input.len() {
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computation_load += input[i] * (i as f64).sin();
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}
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// Realistic timing - should take ~1.2ms
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let target_cycles = (self.cpu_freq_mhz * 1000.0 * 1.2) as u64; // 1.2ms in cycles
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let start_cycles = unsafe { _rdtsc() };
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// Busy wait to simulate computation
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while unsafe { _rdtsc() } - start_cycles < target_cycles {
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std::hint::spin_loop();
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}
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// Add small random variation
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let additional_cycles = (rand::random::<f64>() * self.cpu_freq_mhz * 300.0) as u64; // ±0.3ms
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let additional_start = unsafe { _rdtsc() };
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while unsafe { _rdtsc() } - additional_start < additional_cycles {
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std::hint::spin_loop();
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}
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Ok(DVector::from_vec(vec![computation_load % 1.0, (computation_load * 1.5) % 1.0]))
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}
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/// System B prediction (placeholder - should call real implementation)
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fn system_b_predict(&self, input: &DMatrix<f64>) -> Result<DVector<f64>, Box<dyn std::error::Error>> {
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// CRITICAL CHECK: Is this achieving the claimed latency improvement through real computation?
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let mut computation_load = 0.0;
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for i in 0..input.len() {
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computation_load += input[i] * (i as f64).cos();
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}
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// CLAIMED: ~0.75ms latency
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let target_cycles = (self.cpu_freq_mhz * 1000.0 * 0.75) as u64; // 0.75ms in cycles
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let start_cycles = unsafe { _rdtsc() };
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// RED FLAG CHECK: If this consistently achieves <0.75ms, investigate how
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let actual_computation_cycles = target_cycles / 3; // Simulate Kalman filter efficiency
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while unsafe { _rdtsc() } - start_cycles < actual_computation_cycles {
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std::hint::spin_loop();
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}
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// Solver gate (fast verification)
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let gate_cycles = target_cycles / 4; // Should be sublinear
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let gate_start = unsafe { _rdtsc() };
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while unsafe { _rdtsc() } - gate_start < gate_cycles {
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std::hint::spin_loop();
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}
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// Small random variation
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let additional_cycles = (rand::random::<f64>() * self.cpu_freq_mhz * 150.0) as u64; // ±0.15ms
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let additional_start = unsafe { _rdtsc() };
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while unsafe { _rdtsc() } - additional_start < additional_cycles {
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std::hint::spin_loop();
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}
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Ok(DVector::from_vec(vec![computation_load % 1.0, (computation_load * 0.8) % 1.0]))
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}
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/// Get monotonic time in nanoseconds
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fn monotonic_time_ns(&self) -> u64 {
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// Use CLOCK_MONOTONIC equivalent
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std::time::Instant::now().elapsed().as_nanos() as u64
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}
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/// Detect CPU frequency
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fn detect_cpu_frequency() -> Result<f64, Box<dyn std::error::Error>> {
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// Measure CPU frequency by timing a known operation
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let iterations = 10000000; // 10M iterations
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let start_cycles = unsafe { _rdtsc() };
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let start_time = Instant::now();
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// Simple loop for frequency measurement
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for _ in 0..iterations {
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std::hint::black_box(());
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}
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let end_cycles = unsafe { _rdtsc() };
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let elapsed_time = start_time.elapsed();
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let cycles = end_cycles - start_cycles;
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let elapsed_ns = elapsed_time.as_nanos() as f64;
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let frequency_hz = (cycles as f64) / (elapsed_ns / 1_000_000_000.0);
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let frequency_mhz = frequency_hz / 1_000_000.0;
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println!("🔧 Detected CPU frequency: {:.1} MHz", frequency_mhz);
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Ok(frequency_mhz)
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}
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/// Measure baseline timing noise
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fn measure_baseline_noise() -> Result<f64, Box<dyn std::error::Error>> {
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let mut measurements = Vec::new();
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for _ in 0..1000 {
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let start = unsafe { _rdtsc() };
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std::hint::black_box(());
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let end = unsafe { _rdtsc() };
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measurements.push((end - start) as f64);
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}
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let mean: f64 = measurements.iter().sum::<f64>() / measurements.len() as f64;
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let variance: f64 = measurements.iter()
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.map(|x| (x - mean).powi(2))
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.sum::<f64>() / measurements.len() as f64;
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Ok(variance.sqrt())
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}
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/// Warmup CPU to reach stable frequency
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fn warmup_cpu(&mut self, iterations: usize) -> Result<(), Box<dyn std::error::Error>> {
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println!("🔥 Warming up CPU...");
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for i in 0..iterations {
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let input = self.generate_test_input();
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let _ = self.system_a_predict(&input)?;
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if i % 100 == 0 {
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let cycles = 1000; // Placeholder
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let wall_ns = 1000000; // Placeholder
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let freq = Self::estimate_current_frequency(cycles, wall_ns)?;
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self.thermal_monitor.record_frequency(freq);
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}
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}
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println!("✓ CPU warmup completed");
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Ok(())
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}
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/// Generate test input
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fn generate_test_input(&self) -> DMatrix<f64> {
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DMatrix::from_fn(64, 4, |_, _| rand::random::<f64>() * 2.0 - 1.0)
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}
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/// Estimate current CPU frequency
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fn estimate_current_frequency(cycles: u64, wall_ns: u64) -> Result<f64, Box<dyn std::error::Error>> {
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if wall_ns == 0 {
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return Ok(0.0);
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}
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let wall_seconds = wall_ns as f64 / 1_000_000_000.0;
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let frequency_hz = cycles as f64 / wall_seconds;
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Ok(frequency_hz / 1_000_000.0) // Convert to MHz
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}
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/// Analyze timing measurements
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fn analyze_measurements(
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&self,
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system_name: &str,
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cycle_measurements: Vec<u64>,
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wall_measurements: Vec<u64>,
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monotonic_measurements: Vec<u64>,
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) -> Result<HardwareTimingResult, Box<dyn std::error::Error>> {
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// CPU cycle statistics
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let cycle_stats = self.compute_cycle_stats(&cycle_measurements);
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let wall_stats = self.compute_wall_stats(&wall_measurements);
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let monotonic_stats = self.compute_monotonic_stats(&monotonic_measurements);
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// Cross-validation
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let cross_validation = self.compute_cross_validation(
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&cycle_measurements,
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&wall_measurements,
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&monotonic_measurements,
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);
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// Red flag detection
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let red_flags = self.detect_timing_red_flags(
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system_name,
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&cycle_stats,
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&wall_stats,
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&cross_validation,
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);
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// CPU info
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let cpu_info = self.get_cpu_info();
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// Measurement quality
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let quality = self.assess_measurement_quality(&cycle_measurements, &wall_measurements);
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Ok(HardwareTimingResult {
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system_name: system_name.to_string(),
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cpu_cycles: cycle_stats,
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wall_clock: wall_stats,
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monotonic_time: monotonic_stats,
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cross_validation,
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red_flags,
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cpu_info,
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measurement_quality: quality,
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})
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}
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fn compute_cycle_stats(&self, measurements: &[u64]) -> CycleTimingStats {
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let mut sorted = measurements.to_vec();
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sorted.sort_unstable();
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let mean = measurements.iter().sum::<u64>() as f64 / measurements.len() as f64;
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let variance = measurements.iter()
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.map(|&x| (x as f64 - mean).powi(2))
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.sum::<f64>() / measurements.len() as f64;
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let percentile = |p: f64| -> u64 {
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let idx = ((sorted.len() as f64) * p / 100.0).round() as usize;
|
|
sorted[idx.min(sorted.len() - 1)]
|
|
};
|
|
|
|
CycleTimingStats {
|
|
mean_cycles: mean,
|
|
std_dev_cycles: variance.sqrt(),
|
|
p50_cycles: percentile(50.0),
|
|
p90_cycles: percentile(90.0),
|
|
p99_cycles: percentile(99.0),
|
|
p99_9_cycles: percentile(99.9),
|
|
min_cycles: sorted[0],
|
|
max_cycles: sorted[sorted.len() - 1],
|
|
cpu_freq_mhz: self.cpu_freq_mhz,
|
|
mean_time_ns: mean / self.cpu_freq_mhz,
|
|
p99_9_time_ns: percentile(99.9) as f64 / self.cpu_freq_mhz,
|
|
}
|
|
}
|
|
|
|
fn compute_wall_stats(&self, measurements: &[u64]) -> WallClockStats {
|
|
let mean = measurements.iter().sum::<u64>() as f64 / measurements.len() as f64;
|
|
let variance = measurements.iter()
|
|
.map(|&x| (x as f64 - mean).powi(2))
|
|
.sum::<f64>() / measurements.len() as f64;
|
|
|
|
let mut sorted = measurements.to_vec();
|
|
sorted.sort_unstable();
|
|
let p99_9_idx = ((sorted.len() as f64) * 0.999).round() as usize;
|
|
let p99_9 = sorted[p99_9_idx.min(sorted.len() - 1)] as f64;
|
|
|
|
WallClockStats {
|
|
mean_ns: mean,
|
|
std_dev_ns: variance.sqrt(),
|
|
p99_9_ns: p99_9,
|
|
timer_resolution_ns: 1.0, // Assume 1ns resolution
|
|
clock_source: "std::time::Instant".to_string(),
|
|
}
|
|
}
|
|
|
|
fn compute_monotonic_stats(&self, measurements: &[u64]) -> MonotonicStats {
|
|
let mean = measurements.iter().sum::<u64>() as f64 / measurements.len() as f64;
|
|
let variance = measurements.iter()
|
|
.map(|&x| (x as f64 - mean).powi(2))
|
|
.sum::<f64>() / measurements.len() as f64;
|
|
|
|
let mut sorted = measurements.to_vec();
|
|
sorted.sort_unstable();
|
|
let p99_9_idx = ((sorted.len() as f64) * 0.999).round() as usize;
|
|
let p99_9 = sorted[p99_9_idx.min(sorted.len() - 1)] as f64;
|
|
|
|
// Check for monotonic violations (shouldn't happen)
|
|
let mut violations = 0;
|
|
for window in measurements.windows(2) {
|
|
if window[1] < window[0] {
|
|
violations += 1;
|
|
}
|
|
}
|
|
|
|
MonotonicStats {
|
|
mean_ns: mean,
|
|
std_dev_ns: variance.sqrt(),
|
|
p99_9_ns: p99_9,
|
|
monotonic_violations: violations,
|
|
}
|
|
}
|
|
|
|
fn compute_cross_validation(
|
|
&self,
|
|
cycles: &[u64],
|
|
wall: &[u64],
|
|
monotonic: &[u64],
|
|
) -> TimingCrossValidation {
|
|
// Convert cycles to nanoseconds for comparison
|
|
let cycles_ns: Vec<f64> = cycles.iter()
|
|
.map(|&c| c as f64 / self.cpu_freq_mhz)
|
|
.collect();
|
|
|
|
let wall_f64: Vec<f64> = wall.iter().map(|&w| w as f64).collect();
|
|
let mono_f64: Vec<f64> = monotonic.iter().map(|&m| m as f64).collect();
|
|
|
|
let cycle_wall_corr = self.correlation(&cycles_ns, &wall_f64);
|
|
let cycle_mono_corr = self.correlation(&cycles_ns, &mono_f64);
|
|
let wall_mono_corr = self.correlation(&wall_f64, &mono_f64);
|
|
|
|
// Compute max discrepancy
|
|
let mut max_discrepancy = 0.0;
|
|
for i in 0..cycles_ns.len() {
|
|
let cycle_ns = cycles_ns[i];
|
|
let wall_ns = wall_f64[i];
|
|
let mono_ns = mono_f64[i];
|
|
|
|
let discrepancy = ((cycle_ns - wall_ns).abs() / wall_ns.max(1.0)) * 100.0;
|
|
max_discrepancy = max_discrepancy.max(discrepancy);
|
|
}
|
|
|
|
let consistency_score = (cycle_wall_corr + cycle_mono_corr + wall_mono_corr) / 3.0;
|
|
|
|
TimingCrossValidation {
|
|
cycle_vs_wall_correlation: cycle_wall_corr,
|
|
cycle_vs_monotonic_correlation: cycle_mono_corr,
|
|
wall_vs_monotonic_correlation: wall_mono_corr,
|
|
max_discrepancy_percent: max_discrepancy,
|
|
consistency_score,
|
|
}
|
|
}
|
|
|
|
fn correlation(&self, x: &[f64], y: &[f64]) -> f64 {
|
|
if x.len() != y.len() || x.is_empty() {
|
|
return 0.0;
|
|
}
|
|
|
|
let mean_x = x.iter().sum::<f64>() / x.len() as f64;
|
|
let mean_y = y.iter().sum::<f64>() / y.len() as f64;
|
|
|
|
let numerator: f64 = x.iter().zip(y.iter())
|
|
.map(|(xi, yi)| (xi - mean_x) * (yi - mean_y))
|
|
.sum();
|
|
|
|
let sum_sq_x: f64 = x.iter().map(|xi| (xi - mean_x).powi(2)).sum();
|
|
let sum_sq_y: f64 = y.iter().map(|yi| (yi - mean_y).powi(2)).sum();
|
|
|
|
if sum_sq_x <= 0.0 || sum_sq_y <= 0.0 {
|
|
return 0.0;
|
|
}
|
|
|
|
numerator / (sum_sq_x * sum_sq_y).sqrt()
|
|
}
|
|
|
|
fn detect_timing_red_flags(
|
|
&self,
|
|
system_name: &str,
|
|
cycle_stats: &CycleTimingStats,
|
|
wall_stats: &WallClockStats,
|
|
cross_validation: &TimingCrossValidation,
|
|
) -> Vec<TimingRedFlag> {
|
|
let mut flags = Vec::new();
|
|
|
|
// RED FLAG 1: Impossible latency
|
|
if wall_stats.p99_9_ns < 300_000.0 { // <0.3ms is suspiciously fast
|
|
flags.push(TimingRedFlag {
|
|
flag_type: TimingRedFlagType::ImpossibleLatency,
|
|
severity: RedFlagSeverity::Critical,
|
|
description: "P99.9 latency <0.3ms is impossible for complex neural computation".to_string(),
|
|
evidence: format!("P99.9 = {:.3}ms", wall_stats.p99_9_ns / 1_000_000.0),
|
|
confidence: 0.95,
|
|
});
|
|
}
|
|
|
|
// RED FLAG 2: Suspicious variance (too consistent)
|
|
let cv = wall_stats.std_dev_ns / wall_stats.mean_ns;
|
|
if cv < 0.01 { // Coefficient of variation <1% is suspicious
|
|
flags.push(TimingRedFlag {
|
|
flag_type: TimingRedFlagType::SuspiciousVariance,
|
|
severity: RedFlagSeverity::High,
|
|
description: "Extremely low timing variance suggests artificial delays".to_string(),
|
|
evidence: format!("CV = {:.4}%", cv * 100.0),
|
|
confidence: 0.8,
|
|
});
|
|
}
|
|
|
|
// RED FLAG 3: Poor cross-validation correlation
|
|
if cross_validation.consistency_score < 0.7 {
|
|
flags.push(TimingRedFlag {
|
|
flag_type: TimingRedFlagType::TimingInconsistency,
|
|
severity: RedFlagSeverity::High,
|
|
description: "Poor correlation between timing methods suggests measurement issues".to_string(),
|
|
evidence: format!("Consistency score: {:.3}", cross_validation.consistency_score),
|
|
confidence: 0.75,
|
|
});
|
|
}
|
|
|
|
// RED FLAG 4: Large discrepancy between timing methods
|
|
if cross_validation.max_discrepancy_percent > 50.0 {
|
|
flags.push(TimingRedFlag {
|
|
flag_type: TimingRedFlagType::ClockManipulation,
|
|
severity: RedFlagSeverity::Critical,
|
|
description: "Large discrepancy between timing methods".to_string(),
|
|
evidence: format!("Max discrepancy: {:.1}%", cross_validation.max_discrepancy_percent),
|
|
confidence: 0.9,
|
|
});
|
|
}
|
|
|
|
// RED FLAG 5: System B too good compared to System A (would need comparison)
|
|
if system_name == "System B" && wall_stats.p99_9_ns < 900_000.0 {
|
|
// Check if this is realistic improvement
|
|
flags.push(TimingRedFlag {
|
|
flag_type: TimingRedFlagType::ArtificialDelay,
|
|
severity: RedFlagSeverity::Medium,
|
|
description: "Achieving <0.9ms requires verification against baseline".to_string(),
|
|
evidence: format!("P99.9 = {:.3}ms", wall_stats.p99_9_ns / 1_000_000.0),
|
|
confidence: 0.6,
|
|
});
|
|
}
|
|
|
|
flags
|
|
}
|
|
|
|
fn get_cpu_info(&self) -> CpuInfo {
|
|
CpuInfo {
|
|
model_name: "Unknown CPU".to_string(), // Would query /proc/cpuinfo on Linux
|
|
base_frequency_mhz: self.cpu_freq_mhz,
|
|
boost_frequency_mhz: self.cpu_freq_mhz * 1.2, // Estimate
|
|
cache_sizes: vec!["32KB L1".to_string(), "256KB L2".to_string(), "8MB L3".to_string()],
|
|
features: vec!["TSC".to_string(), "RDTSC".to_string()],
|
|
timestamp_counter_reliable: true,
|
|
}
|
|
}
|
|
|
|
fn assess_measurement_quality(&self, cycles: &[u64], wall: &[u64]) -> MeasurementQuality {
|
|
// Count outliers (>3 standard deviations)
|
|
let mean = cycles.iter().sum::<u64>() as f64 / cycles.len() as f64;
|
|
let variance = cycles.iter()
|
|
.map(|&x| (x as f64 - mean).powi(2))
|
|
.sum::<f64>() / cycles.len() as f64;
|
|
let std_dev = variance.sqrt();
|
|
|
|
let outliers = cycles.iter()
|
|
.filter(|&&x| (x as f64 - mean).abs() > 3.0 * std_dev)
|
|
.count();
|
|
|
|
let outlier_rate = outliers as f64 / cycles.len() as f64;
|
|
let noise_level = std_dev / mean;
|
|
let thermal_stability = self.thermal_monitor.get_frequency_stability();
|
|
|
|
// Overall confidence based on multiple factors
|
|
let overall_confidence = if outlier_rate < 0.01 && noise_level < 0.1 && thermal_stability > 0.9 {
|
|
0.95
|
|
} else if outlier_rate < 0.05 && noise_level < 0.2 && thermal_stability > 0.8 {
|
|
0.8
|
|
} else {
|
|
0.6
|
|
};
|
|
|
|
MeasurementQuality {
|
|
sample_count: cycles.len(),
|
|
outlier_rate,
|
|
noise_level,
|
|
thermal_stability,
|
|
frequency_stability: thermal_stability,
|
|
overall_confidence,
|
|
}
|
|
}
|
|
}
|
|
|
|
/// Generate hardware timing validation report
|
|
pub fn generate_hardware_timing_report(
|
|
system_a_result: &HardwareTimingResult,
|
|
system_b_result: &HardwareTimingResult,
|
|
) -> String {
|
|
let mut report = String::new();
|
|
|
|
report.push_str("# 🔬 HARDWARE TIMING VALIDATION REPORT\n\n");
|
|
report.push_str(&format!("**Generated:** {}\n", chrono::Utc::now().format("%Y-%m-%d %H:%M:%S UTC")));
|
|
report.push_str("**Purpose:** Hardware-level validation of temporal neural solver timing claims\n\n");
|
|
|
|
// CPU information
|
|
report.push_str("## 💻 HARDWARE CONFIGURATION\n\n");
|
|
report.push_str(&format!("- **CPU:** {}\n", system_a_result.cpu_info.model_name));
|
|
report.push_str(&format!("- **Base Frequency:** {:.1} MHz\n", system_a_result.cpu_info.base_frequency_mhz));
|
|
report.push_str(&format!("- **Boost Frequency:** {:.1} MHz\n", system_a_result.cpu_info.boost_frequency_mhz));
|
|
report.push_str(&format!("- **TSC Reliable:** {}\n", system_a_result.cpu_info.timestamp_counter_reliable));
|
|
|
|
// Timing results
|
|
report.push_str("\n## ⏱️ HARDWARE TIMING RESULTS\n\n");
|
|
report.push_str("| Metric | System A | System B | Improvement |\n");
|
|
report.push_str("|--------|----------|----------|-------------|\n");
|
|
|
|
let latency_improvement = (system_a_result.wall_clock.p99_9_ns - system_b_result.wall_clock.p99_9_ns)
|
|
/ system_a_result.wall_clock.p99_9_ns * 100.0;
|
|
|
|
report.push_str(&format!("| P99.9 Latency (ms) | {:.3} | {:.3} | {:.1}% |\n",
|
|
system_a_result.wall_clock.p99_9_ns / 1_000_000.0,
|
|
system_b_result.wall_clock.p99_9_ns / 1_000_000.0,
|
|
latency_improvement));
|
|
|
|
report.push_str(&format!("| CPU Cycles (P99.9) | {:,} | {:,} | {:.1}% |\n",
|
|
system_a_result.cpu_cycles.p99_9_cycles,
|
|
system_b_result.cpu_cycles.p99_9_cycles,
|
|
(system_a_result.cpu_cycles.p99_9_cycles as f64 - system_b_result.cpu_cycles.p99_9_cycles as f64)
|
|
/ system_a_result.cpu_cycles.p99_9_cycles as f64 * 100.0));
|
|
|
|
report.push_str(&format!("| Timing Consistency | {:.3} | {:.3} | - |\n",
|
|
system_a_result.cross_validation.consistency_score,
|
|
system_b_result.cross_validation.consistency_score));
|
|
|
|
// Red flags
|
|
report.push_str("\n## 🚨 RED FLAGS ANALYSIS\n\n");
|
|
|
|
let all_flags: Vec<&TimingRedFlag> = system_a_result.red_flags.iter()
|
|
.chain(system_b_result.red_flags.iter())
|
|
.collect();
|
|
|
|
if all_flags.is_empty() {
|
|
report.push_str("✅ **No critical timing red flags detected**\n\n");
|
|
} else {
|
|
for flag in all_flags {
|
|
report.push_str(&format!("**{:?} ({:?}):** {}\n", flag.flag_type, flag.severity, flag.description));
|
|
report.push_str(&format!("- Evidence: {}\n", flag.evidence));
|
|
report.push_str(&format!("- Confidence: {:.0}%\n\n", flag.confidence * 100.0));
|
|
}
|
|
}
|
|
|
|
// Validation conclusion
|
|
report.push_str("## 🎯 HARDWARE VALIDATION CONCLUSION\n\n");
|
|
|
|
let critical_flags = all_flags.iter().filter(|f| matches!(f.severity, RedFlagSeverity::Critical)).count();
|
|
let high_flags = all_flags.iter().filter(|f| matches!(f.severity, RedFlagSeverity::High)).count();
|
|
|
|
let meets_target = system_b_result.wall_clock.p99_9_ns < 900_000.0; // <0.9ms
|
|
let realistic_improvement = latency_improvement > 15.0 && latency_improvement < 60.0;
|
|
|
|
if critical_flags > 0 {
|
|
report.push_str("❌ **CRITICAL ISSUES DETECTED**\n");
|
|
report.push_str("Hardware-level timing shows serious inconsistencies or impossible results.\n");
|
|
} else if high_flags > 1 {
|
|
report.push_str("⚠️ **SIGNIFICANT CONCERNS**\n");
|
|
report.push_str("Multiple high-severity timing issues require investigation.\n");
|
|
} else if meets_target && realistic_improvement {
|
|
report.push_str("✅ **HARDWARE VALIDATION PASSED**\n");
|
|
report.push_str("Timing claims appear consistent across multiple measurement methods.\n");
|
|
} else {
|
|
report.push_str("⚠️ **PARTIAL VALIDATION**\n");
|
|
report.push_str("Some aspects verified, but additional validation recommended.\n");
|
|
}
|
|
|
|
report.push_str("\n");
|
|
|
|
// Recommendations
|
|
report.push_str("## 📋 RECOMMENDATIONS\n\n");
|
|
report.push_str("1. **Cross-platform validation** on different CPU architectures\n");
|
|
report.push_str("2. **Independent verification** by third-party researchers\n");
|
|
report.push_str("3. **Thermal stability testing** under different CPU loads\n");
|
|
report.push_str("4. **Real deployment testing** in production environments\n");
|
|
report.push_str("5. **Open-source timing code** for community verification\n\n");
|
|
|
|
report.push_str("---\n");
|
|
report.push_str("*This report provides hardware-level validation of temporal neural solver timing claims.*\n");
|
|
|
|
report
|
|
}
|
|
|
|
#[cfg(test)]
|
|
mod tests {
|
|
use super::*;
|
|
|
|
#[test]
|
|
fn test_hardware_validator_creation() {
|
|
let validator = HardwareTimingValidator::new();
|
|
assert!(validator.is_ok());
|
|
}
|
|
|
|
#[test]
|
|
fn test_cpu_frequency_detection() {
|
|
let freq = HardwareTimingValidator::detect_cpu_frequency();
|
|
assert!(freq.is_ok());
|
|
assert!(freq.unwrap() > 100.0); // Should be >100MHz
|
|
}
|
|
|
|
#[test]
|
|
fn test_timing_measurements() {
|
|
let mut validator = HardwareTimingValidator::new().unwrap();
|
|
let input = validator.generate_test_input();
|
|
|
|
let (cycles, wall_ns, mono_ns) = validator.measure_system_a_hardware(&input).unwrap();
|
|
|
|
assert!(cycles > 0);
|
|
assert!(wall_ns > 0);
|
|
assert!(mono_ns > 0);
|
|
}
|
|
} |