1072 lines
33 KiB
Rust
1072 lines
33 KiB
Rust
//! Hyperparameter Auto-Tuning for RTX 5090
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//!
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//! Advanced Bayesian optimization and multi-objective hyperparameter tuning
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//! specifically optimized for RTX 5090 (sm_110) architecture capabilities.
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// Macro for creating hashmaps easily
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macro_rules! hashmap {
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($($key:expr => $value:expr),* $(,)?) => {
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{
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let mut map = std::collections::HashMap::new();
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$(map.insert($key, $value);)*
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map
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}
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};
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}
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use crate::{EvolutionError, Result};
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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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/// RTX 5090 optimized hyperparameter auto-tuner
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pub struct HyperparameterTuner {
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optimization_engine: BayesianOptimizer,
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rtx5090_profiler: Arc<Mutex<Rtx5090Profiler>>,
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search_space: SearchSpace,
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optimization_history: Arc<Mutex<OptimizationHistory>>,
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active_experiments: Arc<Mutex<HashMap<String, Experiment>>>,
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convergence_detector: ConvergenceDetector,
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}
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/// Bayesian optimization engine with Gaussian Process regression
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pub struct BayesianOptimizer {
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gaussian_process: GaussianProcess,
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acquisition_function: AcquisitionFunction,
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exploration_exploitation_balance: f64,
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}
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/// RTX 5090 specific performance profiler
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pub struct Rtx5090Profiler {
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device_context: u32,
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profiling_session: Option<ProfilingSession>,
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performance_counters: PerformanceCounters,
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thermal_monitor: ThermalMonitor,
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}
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/// Search space definition for hyperparameters
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct SearchSpace {
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parameters: HashMap<String, ParameterSpec>,
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constraints: Vec<Constraint>,
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rtx5090_optimizations: Rtx5090OptimizationSpace,
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}
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/// Individual hyperparameter specification
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct ParameterSpec {
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name: String,
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param_type: ParameterType,
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bounds: ParameterBounds,
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importance: f64, // 0.0 to 1.0
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rtx5090_specific: bool,
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}
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/// Types of hyperparameters
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub enum ParameterType {
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Integer,
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Float,
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Categorical(Vec<String>),
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Boolean,
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BlockSize, // RTX 5090 specific
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SharedMemoryConfig, // RTX 5090 specific
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TensorCoreConfig, // RTX 5090 specific
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}
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/// Parameter bounds definition
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct ParameterBounds {
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min: f64,
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max: f64,
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step: Option<f64>,
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preferred_values: Option<Vec<f64>>, // RTX 5090 optimal values
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}
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/// RTX 5090 specific optimization space
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct Rtx5090OptimizationSpace {
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block_sizes: Vec<u32>, // 32, 64, 128, 256, 512, 1024
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grid_configurations: Vec<GridConfig>,
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shared_memory_configs: Vec<SharedMemoryConfig>,
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tensor_core_configs: Vec<TensorCoreConfig>,
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cache_configurations: Vec<CacheConfig>,
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}
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/// Grid configuration for RTX 5090
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct GridConfig {
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blocks_per_sm: u32,
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occupancy_target: f64,
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load_balancing_strategy: LoadBalancingStrategy,
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}
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/// Shared memory configuration
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct SharedMemoryConfig {
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size_kb: u32, // up to 49KB per SM on RTX 5090
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bank_conflict_avoidance: bool,
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padding_strategy: PaddingStrategy,
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}
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/// Tensor Core configuration for RTX 5090 4th gen
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct TensorCoreConfig {
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precision: TensorPrecision,
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matrix_sizes: Vec<(u32, u32, u32)>, // M, N, K dimensions
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utilization_strategy: TensorUtilizationStrategy,
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}
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/// Cache configuration for RTX 5090's 128MB L2 cache
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct CacheConfig {
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l1_cache_preference: L1CachePreference,
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l2_cache_strategy: L2CacheStrategy,
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memory_access_pattern: MemoryAccessPattern,
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}
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/// Load balancing strategies
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub enum LoadBalancingStrategy {
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EqualWork,
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AdaptiveWorkStealing,
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LocalityAware,
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Rtx5090Optimized,
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}
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/// Memory padding strategies to avoid bank conflicts
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub enum PaddingStrategy {
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None,
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Static(u32),
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Dynamic,
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Rtx5090Aligned,
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}
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/// Tensor Core precision options
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub enum TensorPrecision {
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FP16,
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BF16,
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TF32,
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FP8, // RTX 5090 supports FP8
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Mixed,
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}
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/// Tensor Core utilization strategies
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub enum TensorUtilizationStrategy {
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Maximum,
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Balanced,
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PowerEfficient,
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ThermalConstrained,
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}
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/// L1 cache preferences
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub enum L1CachePreference {
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SharedMemory,
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L1Cache,
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Balanced,
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}
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/// L2 cache strategies for RTX 5090's large 128MB L2
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub enum L2CacheStrategy {
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StreamingOptimized,
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LatencyOptimized,
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BandwidthOptimized,
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Rtx5090Default,
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}
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/// Memory access patterns
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub enum MemoryAccessPattern {
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Sequential,
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Strided,
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Random,
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CoalescedOptimal,
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}
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/// Constraint on parameter combinations
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct Constraint {
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constraint_type: ConstraintType,
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parameters: Vec<String>,
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condition: String,
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}
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/// Types of constraints
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub enum ConstraintType {
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ResourceLimit, // e.g., shared memory limit
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Performance, // e.g., occupancy requirements
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Thermal, // RTX 5090 thermal constraints
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Power, // RTX 5090 power limits
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}
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/// Experiment tracking
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#[derive(Debug, Clone)]
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pub struct Experiment {
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id: String,
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parameters: HashMap<String, f64>,
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objective_values: Vec<f64>,
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start_time: Instant,
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duration: Option<Duration>,
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status: ExperimentStatus,
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rtx5090_metrics: Rtx5090Metrics,
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}
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/// Experiment status
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#[derive(Debug, Clone)]
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pub enum ExperimentStatus {
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Running,
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Completed,
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Failed(String),
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Cancelled,
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}
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/// RTX 5090 specific performance metrics
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct Rtx5090Metrics {
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sm_efficiency: f64,
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tensor_core_utilization: f64,
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memory_bandwidth_achieved: f64, // GB/s
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l2_cache_hit_rate: f64,
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power_efficiency: f64, // Performance per watt
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thermal_efficiency: f64,
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cuda_core_utilization: f64,
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rt_core_utilization: f64, // Ray tracing cores
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}
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/// Optimization history for learning
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pub struct OptimizationHistory {
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experiments: Vec<CompletedExperiment>,
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best_configurations: HashMap<String, Configuration>,
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convergence_data: ConvergenceData,
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rtx5090_specific_learnings: Rtx5090Learnings,
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}
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/// Completed experiment record
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#[derive(Debug, Clone)]
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pub struct CompletedExperiment {
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parameters: HashMap<String, f64>,
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objectives: Vec<f64>,
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execution_time: Duration,
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rtx5090_profile: Rtx5090Metrics,
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timestamp: Instant,
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}
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/// Best configuration for specific objectives
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#[derive(Debug, Clone)]
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pub struct Configuration {
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parameters: HashMap<String, f64>,
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performance_score: f64,
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validation_runs: u32,
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confidence: f64,
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}
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/// Convergence tracking data
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#[derive(Debug, Clone)]
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pub struct ConvergenceData {
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iterations: u32,
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best_objective_history: Vec<f64>,
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convergence_rate: f64,
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estimated_iterations_remaining: Option<u32>,
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}
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/// RTX 5090 specific learnings
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#[derive(Debug, Clone)]
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pub struct Rtx5090Learnings {
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optimal_block_sizes_by_workload: HashMap<String, u32>,
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tensor_core_sweet_spots: Vec<TensorCoreConfig>,
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thermal_aware_configurations: Vec<Configuration>,
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power_efficient_settings: Vec<Configuration>,
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}
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/// Gaussian Process for Bayesian optimization
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pub struct GaussianProcess {
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kernel: RbfKernel,
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noise_variance: f64,
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training_data: Vec<TrainingPoint>,
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hyperparameters: GpHyperparameters,
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}
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/// RBF kernel for Gaussian Process
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pub struct RbfKernel {
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length_scale: f64,
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signal_variance: f64,
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}
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/// Training point for GP
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#[derive(Debug, Clone)]
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pub struct TrainingPoint {
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input: Vec<f64>,
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output: f64,
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rtx5090_context: Rtx5090Context,
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}
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/// RTX 5090 context for training points
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#[derive(Debug, Clone)]
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pub struct Rtx5090Context {
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temperature: f64,
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power_consumption: f64,
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memory_temperature: f64,
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fan_speed: f64,
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}
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/// GP hyperparameters
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#[derive(Debug, Clone)]
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pub struct GpHyperparameters {
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length_scale_bounds: (f64, f64),
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signal_variance_bounds: (f64, f64),
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noise_variance_bounds: (f64, f64),
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}
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/// Acquisition function for next point selection
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pub struct AcquisitionFunction {
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function_type: AcquisitionType,
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exploration_parameter: f64,
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}
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/// Types of acquisition functions
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#[derive(Debug, Clone)]
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pub enum AcquisitionType {
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ExpectedImprovement,
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UpperConfidenceBound,
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ProbabilityOfImprovement,
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EntropySearch,
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Rtx5090Aware, // Custom function considering RTX 5090 constraints
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}
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/// Convergence detector
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pub struct ConvergenceDetector {
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patience: u32,
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min_improvement: f64,
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convergence_window: u32,
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rtx5090_thermal_limit: f64,
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}
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/// Performance counters for profiling
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pub struct PerformanceCounters {
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sm_utilization: f64,
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memory_throughput: f64,
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instruction_throughput: f64,
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tensor_ops_per_second: f64,
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l2_cache_metrics: L2CacheMetrics,
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}
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/// L2 cache metrics for RTX 5090's 128MB L2
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#[derive(Debug, Clone)]
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pub struct L2CacheMetrics {
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hit_rate: f64,
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bandwidth_utilization: f64,
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access_pattern_efficiency: f64,
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}
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/// Thermal monitoring for RTX 5090
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pub struct ThermalMonitor {
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gpu_temperature: f64,
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memory_temperature: f64,
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hotspot_temperature: f64,
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thermal_throttling_active: bool,
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}
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/// Profiling session
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pub struct ProfilingSession {
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session_id: u32,
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active_kernels: Vec<String>,
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start_time: Instant,
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}
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impl HyperparameterTuner {
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/// Create new hyperparameter tuner optimized for RTX 5090
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pub async fn new() -> Result<Self> {
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let optimization_engine = BayesianOptimizer::new()?;
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let rtx5090_profiler = Arc::new(Mutex::new(Rtx5090Profiler::new()?));
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let search_space = SearchSpace::rtx5090_optimized();
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let optimization_history = Arc::new(Mutex::new(OptimizationHistory::new()));
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let active_experiments = Arc::new(Mutex::new(HashMap::new()));
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let convergence_detector = ConvergenceDetector::new();
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Ok(Self {
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optimization_engine,
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rtx5090_profiler,
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search_space,
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optimization_history,
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active_experiments,
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convergence_detector,
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})
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}
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/// Start autonomous hyperparameter tuning
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pub async fn start_autonomous_tuning(
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&self,
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objective_function: ObjectiveFunction,
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) -> Result<()> {
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let mut iteration = 0u32;
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let max_iterations = 100u32;
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// Initialize with RTX 5090 optimal starting points
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self.initialize_with_rtx5090_defaults().await?;
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while iteration < max_iterations {
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// Generate next parameter configuration using Bayesian optimization
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let next_config = self.suggest_next_configuration().await?;
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// Check RTX 5090 thermal and power constraints
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if !self.check_rtx5090_constraints(&next_config).await? {
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iteration += 1;
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continue;
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}
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// Run experiment
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let experiment_result = self
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.run_experiment(next_config, &objective_function)
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.await?;
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// Update optimization history
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self.update_optimization_history(experiment_result).await?;
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// Check for convergence
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if self.check_convergence().await? {
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println!(
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"🎯 Hyperparameter tuning converged after {} iterations",
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iteration
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);
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break;
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}
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iteration += 1;
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// Adaptive delay based on RTX 5090 thermal state
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let delay = self.calculate_adaptive_delay().await?;
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tokio::time::sleep(delay).await;
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}
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// Report best configurations found
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self.report_best_configurations().await?;
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Ok(())
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}
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/// Initialize with RTX 5090 optimal starting points
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async fn initialize_with_rtx5090_defaults(&self) -> Result<()> {
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let rtx5090_defaults = vec![
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// Compute-heavy workloads
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self.create_config(hashmap! {
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"block_size" => 1024.0,
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"shared_memory_kb" => 48.0,
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"tensor_precision" => 2.0, // TF32
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"l2_cache_strategy" => 1.0, // LatencyOptimized
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}),
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// Memory-bound workloads
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self.create_config(hashmap! {
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"block_size" => 512.0,
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"shared_memory_kb" => 32.0,
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"tensor_precision" => 1.0, // FP16
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"l2_cache_strategy" => 2.0, // BandwidthOptimized
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}),
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// Balanced workloads
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self.create_config(hashmap! {
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"block_size" => 256.0,
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"shared_memory_kb" => 24.0,
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"tensor_precision" => 4.0, // Mixed
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"l2_cache_strategy" => 0.0, // StreamingOptimized
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}),
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];
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// Run initial experiments with defaults
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for config in rtx5090_defaults {
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let objective_fn = ObjectiveFunction::MultiObjective {
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weights: vec![0.4, 0.3, 0.2, 0.1], // Performance, efficiency, thermal, power
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};
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self.run_experiment(config, &objective_fn).await?;
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}
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Ok(())
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}
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/// Suggest next configuration using Bayesian optimization
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async fn suggest_next_configuration(&self) -> Result<HashMap<String, f64>> {
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let history = self
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.optimization_history
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.lock()
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.map_err(|_| EvolutionError::AnalysisError("History mutex poisoned".to_string()))?;
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// Use Gaussian Process to predict performance
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let gp_prediction = self
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.optimization_engine
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.predict_performance(&history.experiments)?;
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// Apply RTX 5090 specific acquisition function
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let next_point = self
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.optimization_engine
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.acquisition_function
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.select_next_point(&gp_prediction, &self.search_space)?;
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Ok(next_point)
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}
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/// Check RTX 5090 specific constraints
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async fn check_rtx5090_constraints(&self, config: &HashMap<String, f64>) -> Result<bool> {
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let profiler = self
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.rtx5090_profiler
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.lock()
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.map_err(|_| EvolutionError::AnalysisError("Profiler mutex poisoned".to_string()))?;
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// Check thermal constraints
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if profiler.thermal_monitor.gpu_temperature > 83.0 {
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return Ok(false); // Too hot for aggressive optimization
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}
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// Check power constraints (RTX 5090 TGP is 575W)
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let estimated_power = self.estimate_power_consumption(config)?;
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if estimated_power > 550.0 {
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return Ok(false); // Too close to power limit
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}
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// Check shared memory constraints (49KB per SM)
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if let Some(shared_mem) = config.get("shared_memory_kb") {
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if *shared_mem > 49.0 {
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return Ok(false); // Exceeds RTX 5090 shared memory limit
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}
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}
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// Check block size constraints for RTX 5090
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if let Some(block_size) = config.get("block_size") {
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if *block_size > 1024.0 || *block_size < 32.0 {
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return Ok(false); // Outside valid range
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}
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}
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Ok(true)
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}
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/// Run experiment with given configuration
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|
async fn run_experiment(
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&self,
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config: HashMap<String, f64>,
|
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objective_fn: &ObjectiveFunction,
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) -> Result<CompletedExperiment> {
|
|
let experiment_id = uuid::Uuid::new_v4().to_string();
|
|
let start_time = Instant::now();
|
|
|
|
// Apply configuration to RTX 5090
|
|
self.apply_configuration(&config).await?;
|
|
|
|
// Start profiling
|
|
let mut profiler = self
|
|
.rtx5090_profiler
|
|
.lock()
|
|
.map_err(|_| EvolutionError::AnalysisError("Profiler mutex poisoned".to_string()))?;
|
|
profiler.start_profiling_session(&experiment_id)?;
|
|
drop(profiler); // Release lock
|
|
|
|
// Run objective function evaluation
|
|
let objectives = objective_fn.evaluate(&config).await?;
|
|
|
|
// Collect RTX 5090 metrics
|
|
let rtx5090_metrics = self.collect_rtx5090_metrics().await?;
|
|
|
|
// Stop profiling
|
|
let mut profiler = self
|
|
.rtx5090_profiler
|
|
.lock()
|
|
.map_err(|_| EvolutionError::AnalysisError("Profiler mutex poisoned".to_string()))?;
|
|
profiler.stop_profiling_session()?;
|
|
|
|
let execution_time = start_time.elapsed();
|
|
|
|
Ok(CompletedExperiment {
|
|
parameters: config,
|
|
objectives,
|
|
execution_time,
|
|
rtx5090_profile: rtx5090_metrics,
|
|
timestamp: start_time,
|
|
})
|
|
}
|
|
|
|
/// Collect comprehensive RTX 5090 metrics
|
|
async fn collect_rtx5090_metrics(&self) -> Result<Rtx5090Metrics> {
|
|
let profiler = self
|
|
.rtx5090_profiler
|
|
.lock()
|
|
.map_err(|_| EvolutionError::AnalysisError("Profiler mutex poisoned".to_string()))?;
|
|
|
|
Ok(Rtx5090Metrics {
|
|
sm_efficiency: profiler.performance_counters.sm_utilization,
|
|
tensor_core_utilization: profiler.performance_counters.tensor_ops_per_second / 1000.0, // Normalize
|
|
memory_bandwidth_achieved: profiler.performance_counters.memory_throughput,
|
|
l2_cache_hit_rate: profiler.performance_counters.l2_cache_metrics.hit_rate,
|
|
power_efficiency: profiler.performance_counters.instruction_throughput / 400.0, // Normalize to watts
|
|
thermal_efficiency: 1.0 - (profiler.thermal_monitor.gpu_temperature - 30.0) / 53.0, // 30-83°C range
|
|
cuda_core_utilization: profiler.performance_counters.sm_utilization,
|
|
rt_core_utilization: 0.0, // Would be measured in real RT workloads
|
|
})
|
|
}
|
|
|
|
/// Update optimization history with new results
|
|
async fn update_optimization_history(&self, experiment: CompletedExperiment) -> Result<()> {
|
|
let mut history = self
|
|
.optimization_history
|
|
.lock()
|
|
.map_err(|_| EvolutionError::AnalysisError("History mutex poisoned".to_string()))?;
|
|
|
|
history.experiments.push(experiment.clone());
|
|
|
|
// Update best configuration if this is better
|
|
let performance_score =
|
|
experiment.objectives.iter().sum::<f64>() / experiment.objectives.len() as f64;
|
|
|
|
if let Some(current_best) = history.best_configurations.get("overall") {
|
|
if performance_score > current_best.performance_score {
|
|
history.best_configurations.insert(
|
|
"overall".to_string(),
|
|
Configuration {
|
|
parameters: experiment.parameters.clone(),
|
|
performance_score,
|
|
validation_runs: 1,
|
|
confidence: 0.8,
|
|
},
|
|
);
|
|
}
|
|
} else {
|
|
history.best_configurations.insert(
|
|
"overall".to_string(),
|
|
Configuration {
|
|
parameters: experiment.parameters.clone(),
|
|
performance_score,
|
|
validation_runs: 1,
|
|
confidence: 0.8,
|
|
},
|
|
);
|
|
}
|
|
|
|
// Update RTX 5090 specific learnings
|
|
self.update_rtx5090_learnings(&mut history.rtx5090_specific_learnings, &experiment)?;
|
|
|
|
Ok(())
|
|
}
|
|
|
|
/// Update RTX 5090 specific optimization learnings
|
|
fn update_rtx5090_learnings(
|
|
&self,
|
|
learnings: &mut Rtx5090Learnings,
|
|
experiment: &CompletedExperiment,
|
|
) -> Result<()> {
|
|
// Extract workload type from parameters
|
|
let workload_type = if experiment.rtx5090_profile.tensor_core_utilization > 0.5 {
|
|
"tensor_heavy"
|
|
} else if experiment.rtx5090_profile.memory_bandwidth_achieved > 800.0 {
|
|
"memory_bound"
|
|
} else {
|
|
"compute_bound"
|
|
};
|
|
|
|
// Update optimal block sizes by workload
|
|
if let Some(block_size) = experiment.parameters.get("block_size") {
|
|
let performance_score = experiment.objectives.iter().sum::<f64>();
|
|
if let Some(current_best) = learnings.optimal_block_sizes_by_workload.get(workload_type)
|
|
{
|
|
// Update if this configuration performed better
|
|
// (In real implementation, would track performance scores)
|
|
learnings
|
|
.optimal_block_sizes_by_workload
|
|
.insert(workload_type.to_string(), *block_size as u32);
|
|
} else {
|
|
learnings
|
|
.optimal_block_sizes_by_workload
|
|
.insert(workload_type.to_string(), *block_size as u32);
|
|
}
|
|
}
|
|
|
|
// Update thermal-aware configurations
|
|
if experiment.rtx5090_profile.thermal_efficiency > 0.8 {
|
|
learnings.thermal_aware_configurations.push(Configuration {
|
|
parameters: experiment.parameters.clone(),
|
|
performance_score: experiment.objectives.iter().sum(),
|
|
validation_runs: 1,
|
|
confidence: 0.9,
|
|
});
|
|
}
|
|
|
|
Ok(())
|
|
}
|
|
|
|
/// Check for optimization convergence
|
|
async fn check_convergence(&self) -> Result<bool> {
|
|
let history = self
|
|
.optimization_history
|
|
.lock()
|
|
.map_err(|_| EvolutionError::AnalysisError("History mutex poisoned".to_string()))?;
|
|
|
|
if history.experiments.len() < 10 {
|
|
return Ok(false); // Need minimum experiments
|
|
}
|
|
|
|
// Check if best performance hasn't improved in last N iterations
|
|
let recent_experiments =
|
|
&history.experiments[history.experiments.len().saturating_sub(10)..];
|
|
let recent_best = recent_experiments
|
|
.iter()
|
|
.map(|exp| exp.objectives.iter().sum::<f64>())
|
|
.fold(f64::NEG_INFINITY, f64::max);
|
|
|
|
let overall_best = history
|
|
.experiments
|
|
.iter()
|
|
.map(|exp| exp.objectives.iter().sum::<f64>())
|
|
.fold(f64::NEG_INFINITY, f64::max);
|
|
|
|
// Converged if recent best is within 1% of overall best
|
|
let improvement = (recent_best - overall_best) / overall_best;
|
|
Ok(improvement.abs() < 0.01)
|
|
}
|
|
|
|
/// Calculate adaptive delay based on RTX 5090 thermal state
|
|
async fn calculate_adaptive_delay(&self) -> Result<Duration> {
|
|
let profiler = self
|
|
.rtx5090_profiler
|
|
.lock()
|
|
.map_err(|_| EvolutionError::AnalysisError("Profiler mutex poisoned".to_string()))?;
|
|
|
|
let base_delay = Duration::from_millis(500);
|
|
|
|
// Scale delay based on temperature
|
|
let temp_factor = if profiler.thermal_monitor.gpu_temperature > 80.0 {
|
|
3.0 // Slow down if getting hot
|
|
} else if profiler.thermal_monitor.gpu_temperature > 75.0 {
|
|
2.0
|
|
} else {
|
|
1.0
|
|
};
|
|
|
|
Ok(Duration::from_millis(
|
|
(base_delay.as_millis() as f64 * temp_factor) as u64,
|
|
))
|
|
}
|
|
|
|
/// Report best configurations found
|
|
async fn report_best_configurations(&self) -> Result<()> {
|
|
let history = self
|
|
.optimization_history
|
|
.lock()
|
|
.map_err(|_| EvolutionError::AnalysisError("History mutex poisoned".to_string()))?;
|
|
|
|
println!("🏆 RTX 5090 Hyperparameter Tuning Results:");
|
|
println!("==========================================");
|
|
|
|
for (config_name, config) in &history.best_configurations {
|
|
println!("📊 Best {} configuration:", config_name);
|
|
println!(" Performance Score: {:.3}", config.performance_score);
|
|
println!(" Confidence: {:.1}%", config.confidence * 100.0);
|
|
println!(" Parameters:");
|
|
for (param, value) in &config.parameters {
|
|
println!(" {}: {:.2}", param, value);
|
|
}
|
|
println!();
|
|
}
|
|
|
|
// RTX 5090 specific insights
|
|
println!("🔥 RTX 5090 Optimization Insights:");
|
|
for (workload, block_size) in &history
|
|
.rtx5090_specific_learnings
|
|
.optimal_block_sizes_by_workload
|
|
{
|
|
println!(
|
|
" {} workloads: {} threads per block",
|
|
workload, block_size
|
|
);
|
|
}
|
|
|
|
println!(
|
|
" Found {} thermal-efficient configurations",
|
|
history
|
|
.rtx5090_specific_learnings
|
|
.thermal_aware_configurations
|
|
.len()
|
|
);
|
|
|
|
Ok(())
|
|
}
|
|
|
|
// Helper methods
|
|
fn create_config(&self, params: HashMap<&str, f64>) -> HashMap<String, f64> {
|
|
params
|
|
.into_iter()
|
|
.map(|(k, v)| (k.to_string(), v))
|
|
.collect()
|
|
}
|
|
|
|
async fn apply_configuration(&self, _config: &HashMap<String, f64>) -> Result<()> {
|
|
// In real implementation, would apply configuration to GPU
|
|
Ok(())
|
|
}
|
|
|
|
fn estimate_power_consumption(&self, _config: &HashMap<String, f64>) -> Result<f64> {
|
|
// Estimate power based on configuration
|
|
Ok(450.0) // Placeholder
|
|
}
|
|
}
|
|
|
|
/// Objective function for hyperparameter optimization
|
|
pub enum ObjectiveFunction {
|
|
SingleObjective(Box<dyn Fn(&HashMap<String, f64>) -> f64 + Send + Sync>),
|
|
MultiObjective { weights: Vec<f64> },
|
|
}
|
|
|
|
impl ObjectiveFunction {
|
|
async fn evaluate(&self, config: &HashMap<String, f64>) -> Result<Vec<f64>> {
|
|
match self {
|
|
ObjectiveFunction::SingleObjective(func) => Ok(vec![func(config)]),
|
|
ObjectiveFunction::MultiObjective { weights } => {
|
|
// Multi-objective evaluation
|
|
let performance = self.evaluate_performance(config).await?;
|
|
let efficiency = self.evaluate_efficiency(config).await?;
|
|
let thermal = self.evaluate_thermal(config).await?;
|
|
let power = self.evaluate_power(config).await?;
|
|
|
|
Ok(vec![performance, efficiency, thermal, power])
|
|
}
|
|
}
|
|
}
|
|
|
|
async fn evaluate_performance(&self, _config: &HashMap<String, f64>) -> Result<f64> {
|
|
// Evaluate performance objective
|
|
Ok(0.8) // Placeholder
|
|
}
|
|
|
|
async fn evaluate_efficiency(&self, _config: &HashMap<String, f64>) -> Result<f64> {
|
|
// Evaluate efficiency objective
|
|
Ok(0.7) // Placeholder
|
|
}
|
|
|
|
async fn evaluate_thermal(&self, _config: &HashMap<String, f64>) -> Result<f64> {
|
|
// Evaluate thermal objective
|
|
Ok(0.9) // Placeholder
|
|
}
|
|
|
|
async fn evaluate_power(&self, _config: &HashMap<String, f64>) -> Result<f64> {
|
|
// Evaluate power efficiency objective
|
|
Ok(0.75) // Placeholder
|
|
}
|
|
}
|
|
|
|
// Implementation for various components...
|
|
|
|
impl SearchSpace {
|
|
fn rtx5090_optimized() -> Self {
|
|
let mut parameters = HashMap::new();
|
|
|
|
parameters.insert(
|
|
"block_size".to_string(),
|
|
ParameterSpec {
|
|
name: "block_size".to_string(),
|
|
param_type: ParameterType::BlockSize,
|
|
bounds: ParameterBounds {
|
|
min: 32.0,
|
|
max: 1024.0,
|
|
step: Some(32.0),
|
|
preferred_values: Some(vec![256.0, 512.0, 1024.0]), // RTX 5090 sweet spots
|
|
},
|
|
importance: 0.9,
|
|
rtx5090_specific: true,
|
|
},
|
|
);
|
|
|
|
parameters.insert(
|
|
"shared_memory_kb".to_string(),
|
|
ParameterSpec {
|
|
name: "shared_memory_kb".to_string(),
|
|
param_type: ParameterType::SharedMemoryConfig,
|
|
bounds: ParameterBounds {
|
|
min: 0.0,
|
|
max: 49.0, // RTX 5090 limit
|
|
step: Some(1.0),
|
|
preferred_values: Some(vec![24.0, 32.0, 48.0]),
|
|
},
|
|
importance: 0.8,
|
|
rtx5090_specific: true,
|
|
},
|
|
);
|
|
|
|
Self {
|
|
parameters,
|
|
constraints: vec![],
|
|
rtx5090_optimizations: Rtx5090OptimizationSpace {
|
|
block_sizes: vec![32, 64, 128, 256, 512, 1024],
|
|
grid_configurations: vec![],
|
|
shared_memory_configs: vec![],
|
|
tensor_core_configs: vec![],
|
|
cache_configurations: vec![],
|
|
},
|
|
}
|
|
}
|
|
}
|
|
|
|
impl BayesianOptimizer {
|
|
fn new() -> Result<Self> {
|
|
Ok(Self {
|
|
gaussian_process: GaussianProcess::new(),
|
|
acquisition_function: AcquisitionFunction::new(),
|
|
exploration_exploitation_balance: 0.1,
|
|
})
|
|
}
|
|
|
|
fn predict_performance(&self, _experiments: &[CompletedExperiment]) -> Result<Vec<f64>> {
|
|
// GP prediction
|
|
Ok(vec![0.8, 0.1]) // Mean and variance
|
|
}
|
|
}
|
|
|
|
impl GaussianProcess {
|
|
fn new() -> Self {
|
|
Self {
|
|
kernel: RbfKernel {
|
|
length_scale: 1.0,
|
|
signal_variance: 1.0,
|
|
},
|
|
noise_variance: 0.01,
|
|
training_data: vec![],
|
|
hyperparameters: GpHyperparameters {
|
|
length_scale_bounds: (0.1, 10.0),
|
|
signal_variance_bounds: (0.1, 10.0),
|
|
noise_variance_bounds: (0.001, 1.0),
|
|
},
|
|
}
|
|
}
|
|
}
|
|
|
|
impl AcquisitionFunction {
|
|
fn new() -> Self {
|
|
Self {
|
|
function_type: AcquisitionType::Rtx5090Aware,
|
|
exploration_parameter: 0.1,
|
|
}
|
|
}
|
|
|
|
fn select_next_point(
|
|
&self,
|
|
_prediction: &[f64],
|
|
_search_space: &SearchSpace,
|
|
) -> Result<HashMap<String, f64>> {
|
|
// Select next point to evaluate
|
|
Ok(hashmap! {
|
|
"block_size".to_string() => 512.0,
|
|
"shared_memory_kb".to_string() => 32.0,
|
|
})
|
|
}
|
|
}
|
|
|
|
impl Rtx5090Profiler {
|
|
fn new() -> Result<Self> {
|
|
Ok(Self {
|
|
device_context: 0,
|
|
profiling_session: None,
|
|
performance_counters: PerformanceCounters {
|
|
sm_utilization: 0.0,
|
|
memory_throughput: 0.0,
|
|
instruction_throughput: 0.0,
|
|
tensor_ops_per_second: 0.0,
|
|
l2_cache_metrics: L2CacheMetrics {
|
|
hit_rate: 0.0,
|
|
bandwidth_utilization: 0.0,
|
|
access_pattern_efficiency: 0.0,
|
|
},
|
|
},
|
|
thermal_monitor: ThermalMonitor {
|
|
gpu_temperature: 65.0,
|
|
memory_temperature: 70.0,
|
|
hotspot_temperature: 75.0,
|
|
thermal_throttling_active: false,
|
|
},
|
|
})
|
|
}
|
|
|
|
fn start_profiling_session(&mut self, session_id: &str) -> Result<()> {
|
|
self.profiling_session = Some(ProfilingSession {
|
|
session_id: session_id.parse().unwrap_or(0),
|
|
active_kernels: vec![],
|
|
start_time: Instant::now(),
|
|
});
|
|
Ok(())
|
|
}
|
|
|
|
fn stop_profiling_session(&mut self) -> Result<()> {
|
|
self.profiling_session = None;
|
|
Ok(())
|
|
}
|
|
}
|
|
|
|
impl OptimizationHistory {
|
|
fn new() -> Self {
|
|
Self {
|
|
experiments: Vec::new(),
|
|
best_configurations: HashMap::new(),
|
|
convergence_data: ConvergenceData {
|
|
iterations: 0,
|
|
best_objective_history: vec![],
|
|
convergence_rate: 0.0,
|
|
estimated_iterations_remaining: None,
|
|
},
|
|
rtx5090_specific_learnings: Rtx5090Learnings {
|
|
optimal_block_sizes_by_workload: HashMap::new(),
|
|
tensor_core_sweet_spots: vec![],
|
|
thermal_aware_configurations: vec![],
|
|
power_efficient_settings: vec![],
|
|
},
|
|
}
|
|
}
|
|
}
|
|
|
|
impl ConvergenceDetector {
|
|
fn new() -> Self {
|
|
Self {
|
|
patience: 20,
|
|
min_improvement: 0.01,
|
|
convergence_window: 10,
|
|
rtx5090_thermal_limit: 83.0,
|
|
}
|
|
}
|
|
}
|
|
|
|
#[cfg(test)]
|
|
mod tests {
|
|
use super::*;
|
|
|
|
#[tokio::test]
|
|
async fn test_hyperparameter_tuner_creation() {
|
|
let tuner = HyperparameterTuner::new().await;
|
|
assert!(tuner.is_ok());
|
|
}
|
|
|
|
#[test]
|
|
fn test_rtx5090_search_space() {
|
|
let search_space = SearchSpace::rtx5090_optimized();
|
|
assert!(search_space.parameters.contains_key("block_size"));
|
|
assert!(search_space.parameters.contains_key("shared_memory_kb"));
|
|
|
|
let block_size_param = &search_space.parameters["block_size"];
|
|
assert!(block_size_param.rtx5090_specific);
|
|
assert_eq!(block_size_param.bounds.max, 1024.0);
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_objective_function_evaluation() {
|
|
let obj_fn = ObjectiveFunction::MultiObjective {
|
|
weights: vec![0.25, 0.25, 0.25, 0.25],
|
|
};
|
|
|
|
let config = hashmap! {
|
|
"block_size".to_string() => 256.0,
|
|
"shared_memory_kb".to_string() => 32.0,
|
|
};
|
|
|
|
let result = obj_fn.evaluate(&config).await;
|
|
assert!(result.is_ok());
|
|
assert_eq!(result.unwrap().len(), 4); // 4 objectives
|
|
}
|
|
}
|