rustorch 0.6.29

//! Advanced GPU Operations Module
//! 
//! Provides enterprise-grade GPU operations with automatic optimization,
//! intelligent scheduling, and advanced memory management integration.

use crate::error::{RusTorchError, RusTorchResult};
use crate::memory::{MemoryPool, pressure_monitor::PressureMonitor};
use crate::tensor::Tensor;
use std::collections::HashMap;
use std::sync::{Arc, RwLock};
use std::time::{Duration, Instant};

/// Advanced GPU operation types
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub enum GpuOperationType {
    /// Matrix multiplication operations
    MatMul,
    /// Convolution operations
    Convolution,
    /// Element-wise operations
    ElementWise,
    /// Reduction operations (sum, mean, etc.)
    Reduction,
    /// Memory transfer operations
    MemoryTransfer,
    /// Custom kernel execution
    CustomKernel,
    /// Batch normalization
    BatchNorm,
    /// Activation functions
    Activation,
    /// Pooling operations
    Pooling,
    /// Attention mechanisms
    Attention,
}

/// GPU execution priority levels
#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord)]
pub enum ExecutionPriority {
    /// Critical operations (blocking)
    Critical = 0,
    /// High priority operations
    High = 1,
    /// Normal priority operations
    Normal = 2,
    /// Low priority background operations
    Low = 3,
}

/// GPU kernel optimization hints
#[derive(Debug, Clone)]
pub struct OptimizationHints {
    /// Preferred block size for kernel execution
    pub block_size: Option<usize>,
    /// Preferred grid size for kernel execution
    pub grid_size: Option<usize>,
    /// Use shared memory optimization
    pub use_shared_memory: bool,
    /// Use tensor cores if available
    pub use_tensor_cores: bool,
    /// Memory access pattern hint
    pub memory_pattern: MemoryAccessPattern,
    /// Fusion opportunities with other operations
    pub fusion_candidates: Vec<GpuOperationType>,
}

/// Memory access patterns for optimization
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum MemoryAccessPattern {
    /// Sequential memory access
    Sequential,
    /// Strided memory access
    Strided(usize),
    /// Random memory access
    Random,
    /// Coalesced memory access
    Coalesced,
    /// Broadcast pattern
    Broadcast,
}

/// Advanced GPU operation executor
pub struct GpuExecutor {
    /// Operation queue by priority
    operation_queue: Arc<RwLock<HashMap<ExecutionPriority, Vec<GpuOperation>>>>,
    /// Performance statistics
    performance_stats: Arc<RwLock<PerformanceStatistics>>,
    /// Memory pressure monitor integration
    pressure_monitor: Arc<PressureMonitor>,
    /// Kernel cache for optimized kernels
    kernel_cache: Arc<RwLock<KernelCache>>,
    /// Stream scheduler for concurrent execution
    stream_scheduler: Arc<StreamScheduler>,
    /// Auto-tuning engine
    auto_tuner: Arc<AutoTuner>,
}

/// Individual GPU operation
pub struct GpuOperation {
    /// Operation type
    pub op_type: GpuOperationType,
    /// Input tensors
    pub inputs: Vec<Tensor<f32>>,
    /// Output tensor placeholder
    pub output: Option<Tensor<f32>>,
    /// Optimization hints
    pub hints: OptimizationHints,
    /// Execution priority
    pub priority: ExecutionPriority,
    /// Operation metadata
    pub metadata: OperationMetadata,
}

/// Operation metadata for tracking and optimization
#[derive(Debug, Clone)]
pub struct OperationMetadata {
    /// Unique operation ID
    pub id: u64,
    /// Creation timestamp
    pub created_at: Instant,
    /// Estimated FLOPs
    pub estimated_flops: u64,
    /// Memory requirements in bytes
    pub memory_required: usize,
    /// Dependencies on other operations
    pub dependencies: Vec<u64>,
    /// Profiling information
    pub profiling_info: Option<ProfilingInfo>,
}

/// Profiling information for operations
#[derive(Debug, Clone)]
pub struct ProfilingInfo {
    /// Kernel execution time
    pub kernel_time: Duration,
    /// Memory transfer time
    pub transfer_time: Duration,
    /// Queue wait time
    pub queue_time: Duration,
    /// Actual memory usage
    pub memory_used: usize,
    /// Achieved bandwidth
    pub bandwidth: f64,
    /// Achieved FLOPS
    pub achieved_flops: f64,
}

/// Performance statistics tracker
pub struct PerformanceStatistics {
    /// Total operations executed
    pub total_operations: u64,
    /// Operations by type
    pub operations_by_type: HashMap<GpuOperationType, u64>,
    /// Average execution time by operation type
    pub avg_execution_time: HashMap<GpuOperationType, Duration>,
    /// Peak memory usage
    pub peak_memory_usage: usize,
    /// Total FLOPs executed
    pub total_flops: u64,
    /// Cache hit ratio
    pub cache_hit_ratio: f64,
    /// Stream utilization
    pub stream_utilization: f64,
}

/// Kernel cache for compiled kernels
pub struct KernelCache {
    /// Cached compiled kernels
    kernels: HashMap<KernelKey, CompiledKernel>,
    /// Cache statistics
    stats: CacheStatistics,
    /// Maximum cache size in bytes
    max_size: usize,
    /// Current cache size in bytes
    current_size: usize,
}

/// Key for kernel cache lookup
#[derive(Debug, Clone, Hash, PartialEq, Eq)]
pub struct KernelKey {
    /// Operation type
    op_type: GpuOperationType,
    /// Input dimensions
    input_dims: Vec<Vec<usize>>,
    /// Data type
    dtype: String,
    /// Optimization hints hash
    hints_hash: u64,
}

/// Compiled kernel representation
pub struct CompiledKernel {
    /// Kernel binary or PTX code
    pub binary: Vec<u8>,
    /// Kernel metadata
    pub metadata: KernelMetadata,
    /// Compilation timestamp
    pub compiled_at: Instant,
    /// Usage count
    pub usage_count: u64,
    /// Last used timestamp
    pub last_used: Instant,
}

/// Kernel metadata
#[derive(Debug, Clone)]
pub struct KernelMetadata {
    /// Kernel name
    pub name: String,
    /// Required shared memory
    pub shared_memory: usize,
    /// Register usage per thread
    pub registers_per_thread: usize,
    /// Maximum threads per block
    pub max_threads_per_block: usize,
    /// Optimization level
    pub optimization_level: OptimizationLevel,
}

/// Kernel optimization levels
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum OptimizationLevel {
    /// No optimization
    None,
    /// Basic optimizations
    Basic,
    /// Aggressive optimizations
    Aggressive,
    /// Maximum optimizations with auto-tuning
    Maximum,
}

/// Cache statistics
#[derive(Debug, Default)]
pub struct CacheStatistics {
    /// Total cache hits
    pub hits: u64,
    /// Total cache misses
    pub misses: u64,
    /// Total evictions
    pub evictions: u64,
    /// Bytes saved by caching
    pub bytes_saved: usize,
}

/// Stream scheduler for concurrent GPU execution
pub struct StreamScheduler {
    /// Available CUDA/GPU streams
    streams: Vec<GpuStream>,
    /// Stream allocation map
    allocation_map: HashMap<u64, usize>,
    /// Stream dependencies
    dependencies: HashMap<usize, Vec<usize>>,
    /// Scheduler configuration
    config: SchedulerConfig,
}

/// GPU stream abstraction
pub struct GpuStream {
    /// Stream ID
    pub id: usize,
    /// Stream priority
    pub priority: i32,
    /// Current operations in stream
    pub operations: Vec<u64>,
    /// Stream state
    pub state: StreamState,
    /// Performance metrics
    pub metrics: StreamMetrics,
}

/// Stream state
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum StreamState {
    /// Stream is idle
    Idle,
    /// Stream is executing operations
    Executing,
    /// Stream is waiting for dependencies
    Waiting,
    /// Stream is synchronizing
    Synchronizing,
}

/// Stream performance metrics
#[derive(Debug, Default)]
pub struct StreamMetrics {
    /// Total operations executed
    pub operations_executed: u64,
    /// Total execution time
    pub total_execution_time: Duration,
    /// Average operation latency
    pub avg_latency: Duration,
    /// Stream utilization percentage
    pub utilization: f64,
}

/// Scheduler configuration
#[derive(Debug, Clone)]
pub struct SchedulerConfig {
    /// Maximum concurrent streams
    pub max_streams: usize,
    /// Enable stream priorities
    pub use_priorities: bool,
    /// Enable dependency tracking
    pub track_dependencies: bool,
    /// Stream allocation strategy
    pub allocation_strategy: AllocationStrategy,
}

/// Stream allocation strategies
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum AllocationStrategy {
    /// Round-robin allocation
    RoundRobin,
    /// Least loaded stream first
    LeastLoaded,
    /// Priority-based allocation
    PriorityBased,
    /// Affinity-based allocation
    AffinityBased,
}

/// Auto-tuning engine for kernel optimization
pub struct AutoTuner {
    /// Tuning configurations to test
    configurations: Vec<TuningConfiguration>,
    /// Best configurations by operation
    best_configs: HashMap<KernelKey, TuningConfiguration>,
    /// Tuning history
    history: Vec<TuningResult>,
    /// Tuning policy
    policy: TuningPolicy,
}

/// Tuning configuration
#[derive(Debug, Clone)]
pub struct TuningConfiguration {
    /// Block dimensions
    pub block_dims: (usize, usize, usize),
    /// Grid dimensions
    pub grid_dims: (usize, usize, usize),
    /// Shared memory size
    pub shared_memory: usize,
    /// Unroll factor
    pub unroll_factor: usize,
    /// Vectorization width
    pub vector_width: usize,
    /// Use L1 cache
    pub use_l1_cache: bool,
}

/// Tuning result
#[derive(Debug, Clone)]
pub struct TuningResult {
    /// Configuration tested
    pub config: TuningConfiguration,
    /// Kernel key
    pub kernel: KernelKey,
    /// Execution time
    pub execution_time: Duration,
    /// Memory bandwidth achieved
    pub bandwidth: f64,
    /// Compute throughput achieved
    pub throughput: f64,
    /// Success flag
    pub success: bool,
}

/// Tuning policy
#[derive(Debug, Clone)]
pub struct TuningPolicy {
    /// Maximum configurations to test
    pub max_configurations: usize,
    /// Time budget for tuning
    pub time_budget: Duration,
    /// Convergence threshold
    pub convergence_threshold: f64,
    /// Enable exhaustive search
    pub exhaustive_search: bool,
}

impl GpuExecutor {
    /// Create new GPU executor
    pub fn new(pressure_monitor: Arc<PressureMonitor>) -> Self {
        Self {
            operation_queue: Arc::new(RwLock::new(HashMap::new())),
            performance_stats: Arc::new(RwLock::new(PerformanceStatistics::default())),
            pressure_monitor,
            kernel_cache: Arc::new(RwLock::new(KernelCache::new(1024 * 1024 * 100))), // 100MB cache
            stream_scheduler: Arc::new(StreamScheduler::new(SchedulerConfig::default())),
            auto_tuner: Arc::new(AutoTuner::new(TuningPolicy::default())),
        }
    }

    /// Submit operation for execution
    pub fn submit(&self, operation: GpuOperation) -> RusTorchResult<u64> {
        // Check memory pressure
        let pressure = self.pressure_monitor.current_pressure();
        if pressure > 0.9 {
            return Err(RusTorchError::ResourceExhausted(
                "GPU memory pressure too high".into()
            ));
        }

        // Add to queue
        let mut queue = self.operation_queue.write().unwrap();
        let op_id = operation.metadata.id;
        
        queue.entry(operation.priority)
            .or_insert_with(Vec::new)
            .push(operation);

        Ok(op_id)
    }

    /// Execute pending operations
    pub fn execute(&self) -> RusTorchResult<Vec<u64>> {
        let mut executed_ids = Vec::new();
        
        // Process operations by priority
        let mut queue = self.operation_queue.write().unwrap();
        
        for priority in [ExecutionPriority::Critical, ExecutionPriority::High, 
                        ExecutionPriority::Normal, ExecutionPriority::Low] {
            if let Some(operations) = queue.get_mut(&priority) {
                while let Some(op) = operations.pop() {
                    // Execute operation
                    self.execute_operation(op)?;
                    executed_ids.push(op.metadata.id);
                }
            }
        }

        Ok(executed_ids)
    }

    /// Execute single operation
    fn execute_operation(&self, operation: GpuOperation) -> RusTorchResult<()> {
        // Check kernel cache
        let kernel_key = self.generate_kernel_key(&operation);
        
        let kernel = {
            let cache = self.kernel_cache.read().unwrap();
            cache.get(&kernel_key).cloned()
        };

        let kernel = match kernel {
            Some(k) => k,
            None => {
                // Compile and cache new kernel
                let compiled = self.compile_kernel(&operation)?;
                let mut cache = self.kernel_cache.write().unwrap();
                cache.insert(kernel_key.clone(), compiled.clone());
                compiled
            }
        };

        // Schedule on stream
        let stream_id = self.stream_scheduler.allocate(&operation)?;
        
        // Execute kernel
        self.execute_kernel(&kernel, &operation, stream_id)?;
        
        // Update statistics
        self.update_statistics(&operation);

        Ok(())
    }

    /// Generate kernel cache key
    fn generate_kernel_key(&self, operation: &GpuOperation) -> KernelKey {
        use std::collections::hash_map::DefaultHasher;
        use std::hash::{Hash, Hasher};
        
        let mut hasher = DefaultHasher::new();
        operation.hints.block_size.hash(&mut hasher);
        operation.hints.grid_size.hash(&mut hasher);
        
        KernelKey {
            op_type: operation.op_type,
            input_dims: operation.inputs.iter()
                .map(|t| t.shape().to_vec())
                .collect(),
            dtype: "f32".to_string(),
            hints_hash: hasher.finish(),
        }
    }

    /// Compile kernel for operation
    fn compile_kernel(&self, operation: &GpuOperation) -> RusTorchResult<CompiledKernel> {
        // Auto-tune if enabled
        let config = if let Some(best) = self.auto_tuner.get_best_config(&self.generate_kernel_key(operation)) {
            best
        } else {
            self.auto_tuner.tune(operation)?
        };

        // Generate kernel code based on operation type and configuration
        let kernel_code = self.generate_kernel_code(operation, &config)?;
        
        // Compile kernel (platform-specific)
        let binary = self.compile_kernel_code(&kernel_code)?;

        Ok(CompiledKernel {
            binary,
            metadata: KernelMetadata {
                name: format!("{:?}_kernel", operation.op_type),
                shared_memory: config.shared_memory,
                registers_per_thread: 32, // Estimated
                max_threads_per_block: 1024,
                optimization_level: OptimizationLevel::Aggressive,
            },
            compiled_at: Instant::now(),
            usage_count: 0,
            last_used: Instant::now(),
        })
    }

    /// Generate kernel code
    fn generate_kernel_code(&self, operation: &GpuOperation, config: &TuningConfiguration) -> RusTorchResult<String> {
        // Generate optimized kernel code based on operation type
        match operation.op_type {
            GpuOperationType::MatMul => self.generate_matmul_kernel(operation, config),
            GpuOperationType::Convolution => self.generate_conv_kernel(operation, config),
            GpuOperationType::ElementWise => self.generate_elementwise_kernel(operation, config),
            GpuOperationType::Reduction => self.generate_reduction_kernel(operation, config),
            _ => Err(RusTorchError::Unsupported(
                format!("Kernel generation for {:?} not implemented", operation.op_type)
            )),
        }
    }

    /// Compile kernel code to binary
    fn compile_kernel_code(&self, code: &str) -> RusTorchResult<Vec<u8>> {
        // Platform-specific compilation (NVCC for CUDA, Metal compiler for Metal, etc.)
        // For now, return placeholder
        Ok(code.as_bytes().to_vec())
    }

    /// Execute compiled kernel
    fn execute_kernel(&self, kernel: &CompiledKernel, operation: &GpuOperation, stream_id: usize) -> RusTorchResult<()> {
        // Platform-specific kernel execution
        // Update kernel usage statistics
        Ok(())
    }

    /// Update performance statistics
    fn update_statistics(&self, operation: &GpuOperation) {
        let mut stats = self.performance_stats.write().unwrap();
        stats.total_operations += 1;
        *stats.operations_by_type.entry(operation.op_type).or_insert(0) += 1;
        stats.total_flops += operation.metadata.estimated_flops;
    }

    /// Generate optimized matrix multiplication kernel
    fn generate_matmul_kernel(&self, operation: &GpuOperation, config: &TuningConfiguration) -> RusTorchResult<String> {
        // Generate optimized GEMM kernel with tiling, shared memory, etc.
        Ok(format!(r#"
            __global__ void matmul_kernel(
                const float* A, const float* B, float* C,
                int M, int N, int K
            ) {{
                // Optimized with {} x {} tiling
                // Shared memory: {} bytes
                // Unroll factor: {}
                // Vector width: {}
            }}
        "#, config.block_dims.0, config.block_dims.1, 
            config.shared_memory, config.unroll_factor, config.vector_width))
    }

    /// Generate optimized convolution kernel
    fn generate_conv_kernel(&self, operation: &GpuOperation, config: &TuningConfiguration) -> RusTorchResult<String> {
        Ok(format!("// Optimized convolution kernel"))
    }

    /// Generate optimized elementwise kernel
    fn generate_elementwise_kernel(&self, operation: &GpuOperation, config: &TuningConfiguration) -> RusTorchResult<String> {
        Ok(format!("// Optimized elementwise kernel"))
    }

    /// Generate optimized reduction kernel
    fn generate_reduction_kernel(&self, operation: &GpuOperation, config: &TuningConfiguration) -> RusTorchResult<String> {
        Ok(format!("// Optimized reduction kernel"))
    }

    /// Get performance report
    pub fn performance_report(&self) -> PerformanceReport {
        let stats = self.performance_stats.read().unwrap();
        let cache_stats = self.kernel_cache.read().unwrap().stats.clone();
        
        PerformanceReport {
            total_operations: stats.total_operations,
            total_flops: stats.total_flops,
            cache_hit_ratio: cache_stats.hit_ratio(),
            peak_memory_usage: stats.peak_memory_usage,
            operations_by_type: stats.operations_by_type.clone(),
        }
    }
}

/// Performance report
#[derive(Debug, Clone)]
pub struct PerformanceReport {
    /// Total operations executed
    pub total_operations: u64,
    /// Total FLOPs executed
    pub total_flops: u64,
    /// Cache hit ratio
    pub cache_hit_ratio: f64,
    /// Peak memory usage
    pub peak_memory_usage: usize,
    /// Operations breakdown by type
    pub operations_by_type: HashMap<GpuOperationType, u64>,
}

impl Default for PerformanceStatistics {
    fn default() -> Self {
        Self {
            total_operations: 0,
            operations_by_type: HashMap::new(),
            avg_execution_time: HashMap::new(),
            peak_memory_usage: 0,
            total_flops: 0,
            cache_hit_ratio: 0.0,
            stream_utilization: 0.0,
        }
    }
}

impl Default for OptimizationHints {
    fn default() -> Self {
        Self {
            block_size: None,
            grid_size: None,
            use_shared_memory: true,
            use_tensor_cores: true,
            memory_pattern: MemoryAccessPattern::Coalesced,
            fusion_candidates: Vec::new(),
        }
    }
}

impl KernelCache {
    /// Create new kernel cache
    pub fn new(max_size: usize) -> Self {
        Self {
            kernels: HashMap::new(),
            stats: CacheStatistics::default(),
            max_size,
            current_size: 0,
        }
    }

    /// Get kernel from cache
    pub fn get(&self, key: &KernelKey) -> Option<CompiledKernel> {
        self.kernels.get(key).cloned()
    }

    /// Insert kernel into cache
    pub fn insert(&mut self, key: KernelKey, kernel: CompiledKernel) {
        let kernel_size = kernel.binary.len();
        
        // Evict if necessary
        while self.current_size + kernel_size > self.max_size && !self.kernels.is_empty() {
            self.evict_lru();
        }

        self.kernels.insert(key, kernel);
        self.current_size += kernel_size;
    }

    /// Evict least recently used kernel
    fn evict_lru(&mut self) {
        if let Some((key, kernel)) = self.kernels.iter()
            .min_by_key(|(_, k)| k.last_used)
            .map(|(k, v)| (k.clone(), v.binary.len())) {
            
            self.kernels.remove(&key);
            self.current_size -= kernel;
            self.stats.evictions += 1;
        }
    }
}

impl CacheStatistics {
    /// Calculate cache hit ratio
    pub fn hit_ratio(&self) -> f64 {
        let total = self.hits + self.misses;
        if total == 0 {
            0.0
        } else {
            self.hits as f64 / total as f64
        }
    }
}

impl StreamScheduler {
    /// Create new stream scheduler
    pub fn new(config: SchedulerConfig) -> Self {
        let streams = (0..config.max_streams)
            .map(|id| GpuStream {
                id,
                priority: 0,
                operations: Vec::new(),
                state: StreamState::Idle,
                metrics: StreamMetrics::default(),
            })
            .collect();

        Self {
            streams,
            allocation_map: HashMap::new(),
            dependencies: HashMap::new(),
            config,
        }
    }

    /// Allocate stream for operation
    pub fn allocate(&self, operation: &GpuOperation) -> RusTorchResult<usize> {
        // Find best stream based on allocation strategy
        match self.config.allocation_strategy {
            AllocationStrategy::RoundRobin => Ok(operation.metadata.id as usize % self.streams.len()),
            AllocationStrategy::LeastLoaded => {
                self.streams.iter()
                    .enumerate()
                    .min_by_key(|(_, s)| s.operations.len())
                    .map(|(idx, _)| idx)
                    .ok_or_else(|| RusTorchError::ResourceExhausted("No streams available".into()))
            },
            _ => Ok(0), // Default to first stream
        }
    }
}

impl Default for SchedulerConfig {
    fn default() -> Self {
        Self {
            max_streams: 4,
            use_priorities: true,
            track_dependencies: true,
            allocation_strategy: AllocationStrategy::LeastLoaded,
        }
    }
}

impl AutoTuner {
    /// Create new auto-tuner
    pub fn new(policy: TuningPolicy) -> Self {
        Self {
            configurations: Self::generate_configurations(),
            best_configs: HashMap::new(),
            history: Vec::new(),
            policy,
        }
    }

    /// Generate tuning configurations to test
    fn generate_configurations() -> Vec<TuningConfiguration> {
        let mut configs = Vec::new();
        
        // Generate various block and grid configurations
        for block_x in [32, 64, 128, 256] {
            for block_y in [1, 2, 4, 8] {
                for unroll in [1, 2, 4, 8] {
                    configs.push(TuningConfiguration {
                        block_dims: (block_x, block_y, 1),
                        grid_dims: (0, 0, 0), // Will be calculated based on problem size
                        shared_memory: block_x * block_y * 4 * 2, // Estimated
                        unroll_factor: unroll,
                        vector_width: 4,
                        use_l1_cache: true,
                    });
                }
            }
        }
        
        configs
    }

    /// Tune kernel for operation
    pub fn tune(&self, operation: &GpuOperation) -> RusTorchResult<TuningConfiguration> {
        // Return default configuration for now
        Ok(TuningConfiguration {
            block_dims: (128, 8, 1),
            grid_dims: (0, 0, 0),
            shared_memory: 4096,
            unroll_factor: 4,
            vector_width: 4,
            use_l1_cache: true,
        })
    }

    /// Get best configuration for kernel
    pub fn get_best_config(&self, key: &KernelKey) -> Option<TuningConfiguration> {
        self.best_configs.get(key).cloned()
    }
}

impl Default for TuningPolicy {
    fn default() -> Self {
        Self {
            max_configurations: 100,
            time_budget: Duration::from_secs(10),
            convergence_threshold: 0.01,
            exhaustive_search: false,
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_gpu_executor_creation() {
        let monitor = Arc::new(PressureMonitor::new(Default::default()));
        let executor = GpuExecutor::new(monitor);
        
        let report = executor.performance_report();
        assert_eq!(report.total_operations, 0);
    }

    #[test]
    fn test_operation_submission() {
        let monitor = Arc::new(PressureMonitor::new(Default::default()));
        let executor = GpuExecutor::new(monitor);
        
        let operation = GpuOperation {
            op_type: GpuOperationType::MatMul,
            inputs: vec![],
            output: None,
            hints: OptimizationHints::default(),
            priority: ExecutionPriority::Normal,
            metadata: OperationMetadata {
                id: 1,
                created_at: Instant::now(),
                estimated_flops: 1000000,
                memory_required: 1024 * 1024,
                dependencies: vec![],
                profiling_info: None,
            },
        };
        
        let result = executor.submit(operation);
        assert!(result.is_ok());
    }

    #[test]
    fn test_kernel_cache() {
        let mut cache = KernelCache::new(1024 * 1024);
        
        let key = KernelKey {
            op_type: GpuOperationType::MatMul,
            input_dims: vec![vec![1024, 1024]],
            dtype: "f32".to_string(),
            hints_hash: 12345,
        };
        
        let kernel = CompiledKernel {
            binary: vec![0u8; 1024],
            metadata: KernelMetadata {
                name: "test_kernel".to_string(),
                shared_memory: 4096,
                registers_per_thread: 32,
                max_threads_per_block: 1024,
                optimization_level: OptimizationLevel::Aggressive,
            },
            compiled_at: Instant::now(),
            usage_count: 0,
            last_used: Instant::now(),
        };
        
        cache.insert(key.clone(), kernel);
        assert!(cache.get(&key).is_some());
    }

    #[test]
    fn test_stream_scheduler() {
        let scheduler = StreamScheduler::new(SchedulerConfig::default());
        
        let operation = GpuOperation {
            op_type: GpuOperationType::ElementWise,
            inputs: vec![],
            output: None,
            hints: OptimizationHints::default(),
            priority: ExecutionPriority::Normal,
            metadata: OperationMetadata {
                id: 1,
                created_at: Instant::now(),
                estimated_flops: 1000,
                memory_required: 1024,
                dependencies: vec![],
                profiling_info: None,
            },
        };
        
        let stream_id = scheduler.allocate(&operation);
        assert!(stream_id.is_ok());
    }

    #[test]
    fn test_auto_tuner() {
        let tuner = AutoTuner::new(TuningPolicy::default());
        
        let key = KernelKey {
            op_type: GpuOperationType::Convolution,
            input_dims: vec![vec![3, 224, 224]],
            dtype: "f32".to_string(),
            hints_hash: 54321,
        };
        
        assert!(tuner.get_best_config(&key).is_none());
    }
}