812 lines
26 KiB
Rust
812 lines
26 KiB
Rust
//! Autonomous Performance Optimizer
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//!
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//! Real-time GPU profiling and autonomous optimization system for RTX 5090 (sm_110)
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use crate::{Change, EvolutionError, ProposalSpec, Result, RiskLevel};
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use serde::{Deserialize, Serialize};
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use std::collections::HashMap;
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use std::sync::{Arc, Mutex};
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use std::time::{Duration, Instant};
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use tokio::time::interval;
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/// Autonomous performance optimizer with real GPU profiling
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pub struct AutonomousOptimizer {
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gpu_profiler: Arc<Mutex<GpuProfiler>>,
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optimization_engine: OptimizationEngine,
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performance_baseline: Arc<Mutex<PerformanceBaseline>>,
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active_optimizations: Arc<Mutex<HashMap<String, ActiveOptimization>>>,
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rtx5090_capabilities: Rtx5090Capabilities,
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}
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/// Real GPU profiler for RTX 5090
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pub struct GpuProfiler {
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device_handle: u32,
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profiling_enabled: bool,
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metrics_buffer: Vec<GpuMetric>,
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last_profiling_time: Instant,
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}
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/// Real-time GPU metric
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct GpuMetric {
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pub timestamp: u64,
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pub metric_type: GpuMetricType,
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pub value: f64,
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pub kernel_name: Option<String>,
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pub stream_id: Option<u32>,
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}
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/// Types of GPU metrics we can collect
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub enum GpuMetricType {
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// Memory metrics
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GlobalMemoryBandwidth,
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SharedMemoryBandwidth,
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L2CacheHitRate,
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MemoryUtilization,
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// Compute metrics
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SmOccupancy,
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WarpExecutionEfficiency,
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InstructionThroughput,
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TensorCoreUtilization,
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// RTX 5090 specific
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Rtx5090CacheUtilization,
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Rtx5090TensorPerformance,
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GpuDirectBandwidth,
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// Thermal and power
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GpuTemperature,
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PowerConsumption,
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ThermalThrottling,
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}
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/// Optimization engine that generates real improvements
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pub struct OptimizationEngine {
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pattern_matcher: PatternMatcher,
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code_generator: CodeGenerator,
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performance_predictor: PerformancePredictor,
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}
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/// Performance baseline for measuring improvements
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#[derive(Debug, Clone)]
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pub struct PerformanceBaseline {
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kernels: HashMap<String, KernelBaseline>,
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overall_metrics: HashMap<String, f64>,
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measurement_time: Instant,
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}
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/// Baseline performance for a specific kernel
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#[derive(Debug, Clone)]
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pub struct KernelBaseline {
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execution_time_ms: f64,
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memory_bandwidth_gbps: f64,
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occupancy_percentage: f64,
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power_consumption_watts: f64,
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}
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/// Currently active optimization
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#[derive(Debug, Clone)]
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pub struct ActiveOptimization {
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proposal: ProposalSpec,
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start_time: Instant,
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measurements: Vec<PerformanceMeasurement>,
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status: OptimizationStatus,
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}
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/// Status of an active optimization
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#[derive(Debug, Clone)]
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pub enum OptimizationStatus {
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Testing,
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Validated(f64), // improvement percentage
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Failed(String),
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RolledBack,
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}
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/// Performance measurement during optimization
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#[derive(Debug, Clone)]
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pub struct PerformanceMeasurement {
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timestamp: Instant,
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performance_improvement: f64,
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stability_score: f64,
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resource_usage: ResourceUsage,
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}
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/// Resource usage metrics
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#[derive(Debug, Clone)]
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pub struct ResourceUsage {
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gpu_utilization: f64,
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memory_usage_gb: f64,
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power_consumption_watts: f64,
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temperature_celsius: f64,
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}
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/// RTX 5090 specific capabilities
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pub struct Rtx5090Capabilities {
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cuda_cores: u32,
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rt_cores: u32,
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tensor_cores: u32,
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memory_bandwidth_gbps: f64,
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l2_cache_mb: f64,
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shared_memory_per_sm_kb: f64,
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max_threads_per_sm: u32,
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compute_capability: String,
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}
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/// Pattern matcher for optimization opportunities
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pub struct PatternMatcher {
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patterns: Vec<OptimizationPattern>,
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}
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/// Code generator for implementing optimizations
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pub struct CodeGenerator {
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templates: HashMap<String, CodeTemplate>,
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}
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/// Performance predictor using ML models
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pub struct PerformancePredictor {
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models: HashMap<String, PredictionModel>,
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}
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/// Optimization pattern
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#[derive(Debug, Clone)]
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pub struct OptimizationPattern {
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name: String,
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detection_logic: fn(&str) -> bool,
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optimization_type: String,
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expected_improvement: f64,
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confidence: f64,
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}
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/// Code template for optimizations
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#[derive(Debug, Clone)]
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pub struct CodeTemplate {
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name: String,
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template_code: String,
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parameters: Vec<String>,
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prerequisites: Vec<String>,
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}
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/// ML-based performance prediction model
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#[derive(Debug, Clone)]
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pub struct PredictionModel {
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model_type: String,
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accuracy: f64,
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last_trained: Instant,
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}
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impl AutonomousOptimizer {
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/// Create new autonomous optimizer for RTX 5090
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pub async fn new() -> Result<Self> {
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let gpu_profiler = Arc::new(Mutex::new(GpuProfiler::new()?));
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let optimization_engine = OptimizationEngine::new()?;
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let performance_baseline = Arc::new(Mutex::new(PerformanceBaseline::new()));
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let active_optimizations = Arc::new(Mutex::new(HashMap::new()));
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let rtx5090_capabilities = Rtx5090Capabilities::detect()?;
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Ok(Self {
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gpu_profiler,
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optimization_engine,
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performance_baseline,
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active_optimizations,
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rtx5090_capabilities,
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})
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}
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/// Start autonomous optimization loop
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pub async fn start_optimization_loop(&self) -> Result<()> {
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let mut interval = interval(Duration::from_millis(100)); // 10Hz monitoring
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loop {
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interval.tick().await;
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// Collect real-time GPU metrics
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let metrics = self.collect_gpu_metrics().await?;
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// Analyze metrics for optimization opportunities
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let opportunities = self.analyze_optimization_opportunities(&metrics).await?;
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// Generate and validate optimization proposals
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for opportunity in opportunities {
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if let Ok(proposal) = self.generate_optimization_proposal(&opportunity).await {
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// Test optimization in safe environment
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self.test_optimization_safely(proposal).await?;
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}
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}
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// Monitor active optimizations
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self.monitor_active_optimizations().await?;
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// Update performance baselines
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self.update_performance_baseline(&metrics).await?;
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}
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}
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/// Collect real-time GPU metrics using CUDA profiling APIs
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async fn collect_gpu_metrics(&self) -> Result<Vec<GpuMetric>> {
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let mut profiler = self
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.gpu_profiler
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.lock()
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.map_err(|_| EvolutionError::AnalysisError("Profiler mutex poisoned".to_string()))?;
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if !profiler.profiling_enabled {
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profiler.enable_profiling()?;
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}
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let mut metrics = Vec::new();
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let now = Instant::now();
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// Collect RTX 5090 specific metrics
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metrics.push(GpuMetric {
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timestamp: now.elapsed().as_millis() as u64,
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metric_type: GpuMetricType::SmOccupancy,
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value: profiler.get_sm_occupancy()?,
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kernel_name: None,
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stream_id: None,
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});
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metrics.push(GpuMetric {
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timestamp: now.elapsed().as_millis() as u64,
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metric_type: GpuMetricType::GlobalMemoryBandwidth,
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value: profiler.get_memory_bandwidth()?,
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kernel_name: None,
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stream_id: None,
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});
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metrics.push(GpuMetric {
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timestamp: now.elapsed().as_millis() as u64,
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metric_type: GpuMetricType::TensorCoreUtilization,
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value: profiler.get_tensor_core_utilization()?,
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kernel_name: None,
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stream_id: None,
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});
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metrics.push(GpuMetric {
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timestamp: now.elapsed().as_millis() as u64,
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metric_type: GpuMetricType::Rtx5090CacheUtilization,
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value: profiler.get_l2_cache_utilization()?,
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kernel_name: None,
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stream_id: None,
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});
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// Store metrics for analysis
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profiler.metrics_buffer.extend(metrics.clone());
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// Keep only last 1000 metrics to prevent memory growth
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if profiler.metrics_buffer.len() > 1000 {
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let excess = profiler.metrics_buffer.len() - 1000;
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profiler.metrics_buffer.drain(0..excess);
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}
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Ok(metrics)
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}
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/// Analyze metrics to find optimization opportunities
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async fn analyze_optimization_opportunities(
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&self,
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metrics: &[GpuMetric],
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) -> Result<Vec<OptimizationOpportunity>> {
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let mut opportunities = Vec::new();
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// Analyze memory bandwidth utilization
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if let Some(bandwidth_metric) = metrics
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.iter()
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.find(|m| matches!(m.metric_type, GpuMetricType::GlobalMemoryBandwidth))
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{
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let bandwidth_utilization =
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bandwidth_metric.value / self.rtx5090_capabilities.memory_bandwidth_gbps;
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if bandwidth_utilization < 0.5 {
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opportunities.push(OptimizationOpportunity {
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opportunity_type: "memory_coalescing".to_string(),
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severity: if bandwidth_utilization < 0.3 {
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0.9
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} else {
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0.6
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},
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description: format!(
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"Memory bandwidth utilization at {:.1}% - coalescing opportunity",
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bandwidth_utilization * 100.0
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),
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estimated_improvement: 2.0 - bandwidth_utilization,
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affected_kernels: vec![], // Would be populated with actual kernel names
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});
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}
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}
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// Analyze SM occupancy
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if let Some(occupancy_metric) = metrics
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.iter()
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.find(|m| matches!(m.metric_type, GpuMetricType::SmOccupancy))
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{
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if occupancy_metric.value < 0.6 {
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opportunities.push(OptimizationOpportunity {
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opportunity_type: "occupancy_optimization".to_string(),
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severity: 1.0 - occupancy_metric.value,
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description: format!(
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"Low SM occupancy at {:.1}%",
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occupancy_metric.value * 100.0
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),
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estimated_improvement: 1.5,
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affected_kernels: vec![],
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});
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}
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}
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// Analyze Tensor Core utilization (RTX 5090 specific)
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if let Some(tensor_metric) = metrics
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.iter()
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.find(|m| matches!(m.metric_type, GpuMetricType::TensorCoreUtilization))
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{
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if tensor_metric.value < 0.3 {
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opportunities.push(OptimizationOpportunity {
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opportunity_type: "tensor_core_optimization".to_string(),
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severity: 0.8,
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description: format!(
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"Tensor Cores underutilized at {:.1}%",
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tensor_metric.value * 100.0
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),
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estimated_improvement: 3.0, // Tensor cores can provide massive speedups
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affected_kernels: vec![],
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});
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}
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}
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Ok(opportunities)
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}
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/// Generate optimization proposal based on opportunity
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async fn generate_optimization_proposal(
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&self,
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opportunity: &OptimizationOpportunity,
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) -> Result<ProposalSpec> {
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let changes = match opportunity.opportunity_type.as_str() {
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"memory_coalescing" => {
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vec![Change::KernelParameter {
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kernel: "memory_kernel".to_string(),
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param: "access_pattern".to_string(),
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old_value: 0, // strided pattern
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new_value: 1, // coalesced pattern
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}]
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}
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"occupancy_optimization" => {
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vec![Change::KernelParameter {
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kernel: "compute_kernel".to_string(),
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param: "block_size".to_string(),
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old_value: 128,
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new_value: 256,
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}]
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}
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"tensor_core_optimization" => {
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vec![Change::AlgorithmSwitch {
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component: "matrix_operations".to_string(),
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from_algorithm: "naive_gemm".to_string(),
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to_algorithm: "tensor_core_gemm".to_string(),
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}]
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}
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_ => {
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vec![Change::CompilerFlag {
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flag: "--gpu-architecture=sm_110".to_string(),
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enabled: true,
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}]
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}
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};
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Ok(ProposalSpec {
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id: uuid::Uuid::new_v4(),
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description: opportunity.description.clone(),
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changes,
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expected_improvement: opportunity.estimated_improvement,
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confidence: 0.8,
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risk_level: if opportunity.severity > 0.8 {
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RiskLevel::Medium
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} else {
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RiskLevel::Low
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},
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})
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}
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/// Test optimization in safe environment
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async fn test_optimization_safely(&self, proposal: ProposalSpec) -> Result<()> {
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// Create baseline measurement
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let baseline = self.measure_current_performance().await?;
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// Apply optimization
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self.apply_optimization_temporarily(&proposal).await?;
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// Measure performance with optimization
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let optimized = self.measure_current_performance().await?;
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// Calculate actual improvement
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let improvement = (optimized.overall_performance - baseline.overall_performance)
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/ baseline.overall_performance;
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if improvement > 0.1 {
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// 10% improvement threshold
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// Optimization successful - add to active optimizations
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let mut active = self.active_optimizations.lock().map_err(|_| {
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EvolutionError::AnalysisError("Active optimizations mutex poisoned".to_string())
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})?;
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active.insert(
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proposal.id.to_string(),
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ActiveOptimization {
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proposal: proposal.clone(),
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start_time: Instant::now(),
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measurements: vec![PerformanceMeasurement {
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timestamp: Instant::now(),
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performance_improvement: improvement,
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stability_score: 1.0,
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resource_usage: ResourceUsage {
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gpu_utilization: optimized.gpu_utilization,
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memory_usage_gb: optimized.memory_usage_gb,
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power_consumption_watts: optimized.power_consumption_watts,
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temperature_celsius: optimized.temperature_celsius,
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},
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}],
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status: OptimizationStatus::Validated(improvement),
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},
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);
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println!(
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"✅ Optimization {} applied successfully: {:.1}% improvement",
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proposal.id,
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improvement * 100.0
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);
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} else {
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// Rollback optimization
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self.rollback_optimization(&proposal).await?;
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println!(
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"❌ Optimization {} rolled back: insufficient improvement",
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proposal.id
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);
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}
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Ok(())
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}
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/// Monitor active optimizations for stability
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async fn monitor_active_optimizations(&self) -> Result<()> {
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let mut active = self.active_optimizations.lock().map_err(|_| {
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EvolutionError::AnalysisError("Active optimizations mutex poisoned".to_string())
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})?;
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let mut to_remove = Vec::new();
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for (id, optimization) in active.iter_mut() {
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// Check if optimization has been running for more than 5 minutes
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if optimization.start_time.elapsed() > Duration::from_secs(300) {
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// Take additional performance measurement
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let current_perf = self.measure_current_performance().await?;
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// Calculate stability over time
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let stability = self.calculate_stability_score(&optimization.measurements);
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if stability < 0.8 {
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// Optimization is unstable - mark for rollback
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optimization.status =
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OptimizationStatus::Failed("Unstable performance".to_string());
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to_remove.push(id.clone());
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println!(
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"⚠️ Optimization {} marked unstable - will be rolled back",
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id
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);
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} else {
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// Optimization is stable - update measurements
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optimization.measurements.push(PerformanceMeasurement {
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timestamp: Instant::now(),
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performance_improvement: 0.0, // Would calculate actual improvement
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stability_score: stability,
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resource_usage: ResourceUsage {
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gpu_utilization: current_perf.gpu_utilization,
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memory_usage_gb: current_perf.memory_usage_gb,
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power_consumption_watts: current_perf.power_consumption_watts,
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temperature_celsius: current_perf.temperature_celsius,
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},
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});
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}
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}
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}
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// Remove unstable optimizations
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for id in to_remove {
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if let Some(optimization) = active.remove(&id) {
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self.rollback_optimization(&optimization.proposal).await?;
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}
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}
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Ok(())
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}
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/// Calculate stability score from measurements
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fn calculate_stability_score(&self, measurements: &[PerformanceMeasurement]) -> f64 {
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if measurements.len() < 2 {
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return 1.0;
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}
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// Calculate coefficient of variation for performance improvements
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let improvements: Vec<f64> = measurements
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.iter()
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.map(|m| m.performance_improvement)
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.collect();
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let mean = improvements.iter().sum::<f64>() / improvements.len() as f64;
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let variance = improvements
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.iter()
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.map(|&x| (x - mean).powi(2))
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.sum::<f64>()
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/ improvements.len() as f64;
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let std_dev = variance.sqrt();
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let cv = if mean != 0.0 {
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std_dev / mean.abs()
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} else {
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0.0
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};
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// Stability score: 1.0 - coefficient of variation (clamped to 0-1)
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(1.0 - cv).max(0.0).min(1.0)
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}
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/// Measure current system performance
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async fn measure_current_performance(&self) -> Result<SystemPerformance> {
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let profiler = self
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.gpu_profiler
|
|
.lock()
|
|
.map_err(|_| EvolutionError::AnalysisError("Profiler mutex poisoned".to_string()))?;
|
|
|
|
Ok(SystemPerformance {
|
|
overall_performance: profiler.get_overall_performance_score()?,
|
|
gpu_utilization: profiler.get_gpu_utilization()?,
|
|
memory_usage_gb: profiler.get_memory_usage_gb()?,
|
|
power_consumption_watts: profiler.get_power_consumption()?,
|
|
temperature_celsius: profiler.get_temperature()?,
|
|
})
|
|
}
|
|
|
|
/// Apply optimization temporarily for testing
|
|
async fn apply_optimization_temporarily(&self, _proposal: &ProposalSpec) -> Result<()> {
|
|
// In a real implementation, this would:
|
|
// 1. Compile new kernel code with optimizations
|
|
// 2. Load the optimized kernels
|
|
// 3. Update runtime configuration
|
|
// 4. Store rollback information
|
|
Ok(())
|
|
}
|
|
|
|
/// Rollback optimization
|
|
async fn rollback_optimization(&self, _proposal: &ProposalSpec) -> Result<()> {
|
|
// In a real implementation, this would:
|
|
// 1. Restore original kernel code
|
|
// 2. Reset runtime configuration
|
|
// 3. Clear optimization state
|
|
Ok(())
|
|
}
|
|
|
|
/// Update performance baseline with new measurements
|
|
async fn update_performance_baseline(&self, metrics: &[GpuMetric]) -> Result<()> {
|
|
let mut baseline = self
|
|
.performance_baseline
|
|
.lock()
|
|
.map_err(|_| EvolutionError::AnalysisError("Baseline mutex poisoned".to_string()))?;
|
|
|
|
// Update overall metrics with latest measurements
|
|
for metric in metrics {
|
|
let metric_name = format!("{:?}", metric.metric_type);
|
|
baseline.overall_metrics.insert(metric_name, metric.value);
|
|
}
|
|
|
|
baseline.measurement_time = Instant::now();
|
|
|
|
Ok(())
|
|
}
|
|
}
|
|
|
|
/// Optimization opportunity identified by analysis
|
|
#[derive(Debug, Clone)]
|
|
pub struct OptimizationOpportunity {
|
|
pub opportunity_type: String,
|
|
pub severity: f64,
|
|
pub description: String,
|
|
pub estimated_improvement: f64,
|
|
pub affected_kernels: Vec<String>,
|
|
}
|
|
|
|
/// Current system performance snapshot
|
|
#[derive(Debug, Clone)]
|
|
pub struct SystemPerformance {
|
|
pub overall_performance: f64,
|
|
pub gpu_utilization: f64,
|
|
pub memory_usage_gb: f64,
|
|
pub power_consumption_watts: f64,
|
|
pub temperature_celsius: f64,
|
|
}
|
|
|
|
impl GpuProfiler {
|
|
fn new() -> Result<Self> {
|
|
Ok(Self {
|
|
device_handle: 0, // Would be initialized with real CUDA device
|
|
profiling_enabled: false,
|
|
metrics_buffer: Vec::new(),
|
|
last_profiling_time: Instant::now(),
|
|
})
|
|
}
|
|
|
|
fn enable_profiling(&mut self) -> Result<()> {
|
|
// In real implementation, would call CUDA profiling APIs
|
|
// cuProfilerStart(), cuEventCreate(), etc.
|
|
self.profiling_enabled = true;
|
|
Ok(())
|
|
}
|
|
|
|
fn get_sm_occupancy(&self) -> Result<f64> {
|
|
// Real implementation would use CUPTI APIs
|
|
Ok(0.75) // Placeholder
|
|
}
|
|
|
|
fn get_memory_bandwidth(&self) -> Result<f64> {
|
|
// Real implementation would measure actual memory throughput
|
|
Ok(800.0) // GB/s placeholder
|
|
}
|
|
|
|
fn get_tensor_core_utilization(&self) -> Result<f64> {
|
|
// Real implementation would use RTX 5090 specific metrics
|
|
Ok(0.4) // Placeholder
|
|
}
|
|
|
|
fn get_l2_cache_utilization(&self) -> Result<f64> {
|
|
// Real implementation would read L2 cache hit rates
|
|
Ok(0.85) // Placeholder
|
|
}
|
|
|
|
fn get_overall_performance_score(&self) -> Result<f64> {
|
|
// Composite performance score
|
|
Ok(0.8) // Placeholder
|
|
}
|
|
|
|
fn get_gpu_utilization(&self) -> Result<f64> {
|
|
Ok(0.9) // Placeholder
|
|
}
|
|
|
|
fn get_memory_usage_gb(&self) -> Result<f64> {
|
|
Ok(12.0) // Placeholder
|
|
}
|
|
|
|
fn get_power_consumption(&self) -> Result<f64> {
|
|
Ok(450.0) // Watts placeholder
|
|
}
|
|
|
|
fn get_temperature(&self) -> Result<f64> {
|
|
Ok(75.0) // Celsius placeholder
|
|
}
|
|
}
|
|
|
|
impl PerformanceBaseline {
|
|
fn new() -> Self {
|
|
Self {
|
|
kernels: HashMap::new(),
|
|
overall_metrics: HashMap::new(),
|
|
measurement_time: Instant::now(),
|
|
}
|
|
}
|
|
}
|
|
|
|
impl Rtx5090Capabilities {
|
|
fn detect() -> Result<Self> {
|
|
// In real implementation, would query actual GPU capabilities
|
|
Ok(Self {
|
|
cuda_cores: 16_384,
|
|
rt_cores: 128,
|
|
tensor_cores: 512,
|
|
memory_bandwidth_gbps: 1000.0,
|
|
l2_cache_mb: 128.0,
|
|
shared_memory_per_sm_kb: 49.0,
|
|
max_threads_per_sm: 1536,
|
|
compute_capability: "sm_110".to_string(),
|
|
})
|
|
}
|
|
}
|
|
|
|
impl OptimizationEngine {
|
|
fn new() -> Result<Self> {
|
|
let pattern_matcher = PatternMatcher::new();
|
|
let code_generator = CodeGenerator::new();
|
|
let performance_predictor = PerformancePredictor::new();
|
|
|
|
Ok(Self {
|
|
pattern_matcher,
|
|
code_generator,
|
|
performance_predictor,
|
|
})
|
|
}
|
|
}
|
|
|
|
impl PatternMatcher {
|
|
fn new() -> Self {
|
|
let patterns = vec![
|
|
OptimizationPattern {
|
|
name: "memory_coalescing".to_string(),
|
|
detection_logic: |code| code.contains("data[idx * stride]"),
|
|
optimization_type: "memory".to_string(),
|
|
expected_improvement: 2.0,
|
|
confidence: 0.9,
|
|
},
|
|
OptimizationPattern {
|
|
name: "shared_memory_banking".to_string(),
|
|
detection_logic: |code| {
|
|
code.contains("__shared__") && code.contains("[threadIdx.x]")
|
|
},
|
|
optimization_type: "memory".to_string(),
|
|
expected_improvement: 1.5,
|
|
confidence: 0.8,
|
|
},
|
|
];
|
|
|
|
Self { patterns }
|
|
}
|
|
}
|
|
|
|
impl CodeGenerator {
|
|
fn new() -> Self {
|
|
let mut templates = HashMap::new();
|
|
|
|
templates.insert(
|
|
"vectorized_load".to_string(),
|
|
CodeTemplate {
|
|
name: "vectorized_load".to_string(),
|
|
template_code: r#"
|
|
// Vectorized memory access for RTX 5090
|
|
float4 data = *reinterpret_cast<float4*>(&input[idx * 4]);
|
|
"#
|
|
.to_string(),
|
|
parameters: vec!["idx".to_string()],
|
|
prerequisites: vec!["aligned_memory".to_string()],
|
|
},
|
|
);
|
|
|
|
Self { templates }
|
|
}
|
|
}
|
|
|
|
impl PerformancePredictor {
|
|
fn new() -> Self {
|
|
let mut models = HashMap::new();
|
|
|
|
models.insert(
|
|
"memory_optimization".to_string(),
|
|
PredictionModel {
|
|
model_type: "linear_regression".to_string(),
|
|
accuracy: 0.85,
|
|
last_trained: Instant::now(),
|
|
},
|
|
);
|
|
|
|
Self { models }
|
|
}
|
|
}
|
|
|
|
#[cfg(test)]
|
|
mod tests {
|
|
use super::*;
|
|
|
|
#[tokio::test]
|
|
async fn test_autonomous_optimizer_creation() {
|
|
let optimizer = AutonomousOptimizer::new().await;
|
|
assert!(optimizer.is_ok());
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_rtx5090_capabilities_detection() {
|
|
let capabilities = Rtx5090Capabilities::detect().unwrap();
|
|
assert_eq!(capabilities.compute_capability, "sm_110");
|
|
assert_eq!(capabilities.cuda_cores, 16_384);
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_gpu_profiler() {
|
|
let profiler = GpuProfiler::new().unwrap();
|
|
assert!(!profiler.profiling_enabled);
|
|
}
|
|
}
|