torsh-backend 0.1.2

//! High-Performance CUDA Kernel Implementations
//!
//! This module provides highly optimized CUDA kernel implementations for ToRSh
//! tensor operations, featuring advanced optimization techniques including:
//! - Tensor Core utilization for mixed-precision operations
//! - Memory coalescing optimization
//! - Shared memory tiling strategies
//! - Register blocking and instruction-level parallelism
//! - Warp-level primitives and cooperative groups
//! - Dynamic kernel generation and auto-tuning

use crate::cuda::{CudaResult, CudaStream};
use scirs2_core::ndarray::{Array1, Array2, ArrayView1, ArrayView2};
use std::collections::HashMap;
use std::sync::{Arc, Mutex, RwLock};
use std::time::{Duration, Instant};
use torsh_core::TensorElement;

// ============================================================================
// Stub implementations for missing types
// ============================================================================

#[derive(Debug, Clone, Default)]
pub struct TensorCoreMatMulImpl {}
#[derive(Debug, Clone, Default)]
pub struct CudaCoresMatMulImpl {}
#[derive(Debug, Clone, Default)]
pub struct MixedPrecisionMatMulImpl {}
#[derive(Debug, Clone, Default)]
pub struct TiledMatMulImpl {}
#[derive(Debug, Clone, Default)]
pub struct FusedMatMulImpl {}
#[derive(Debug, Clone, Default)]
pub struct MatMulImplementationSelector {}
#[derive(Debug, Clone, Default)]
pub struct DirectConvolutionImpl {}
#[derive(Debug, Clone, Default)]
pub struct WinogradConvolutionImpl {}
#[derive(Debug, Clone, Default)]
pub struct FftConvolutionImpl {}
#[derive(Debug, Clone, Default)]
pub struct DepthwiseConvolutionImpl {}
#[derive(Debug, Clone, Default)]
pub struct GroupedConvolutionImpl {}
#[derive(Debug, Clone, Default)]
pub struct DilatedConvolutionImpl {}
#[derive(Debug, Clone, Default)]
pub struct ReLUImplementations {}
#[derive(Debug, Clone, Default)]
pub struct SigmoidImplementations {}
#[derive(Debug, Clone, Default)]
pub struct TanhImplementations {}
#[derive(Debug, Clone, Default)]
pub struct GeLUImplementations {}
#[derive(Debug, Clone, Default)]
pub struct SwishImplementations {}
#[derive(Debug, Clone, Default)]
pub struct FusedActivationImplementations {}

// ============================================================================

/// High-performance CUDA kernel manager
#[derive(Debug)]
pub struct HighPerformanceKernelManager {
    /// Tensor Core optimization engine
    tensor_core_engine: Arc<Mutex<TensorCoreOptimizationEngine>>,

    /// Memory optimization coordinator
    memory_optimizer: Arc<Mutex<KernelMemoryOptimizer>>,

    /// Auto-tuning system for optimal configurations
    auto_tuner: Arc<Mutex<KernelAutoTuner>>,

    /// Kernel cache for reusing optimized implementations
    kernel_cache: Arc<RwLock<OptimizedKernelCache>>,

    /// Performance monitor for runtime optimization
    performance_monitor: Arc<Mutex<KernelPerformanceMonitor>>,

    /// Dynamic code generator
    code_generator: Arc<Mutex<DynamicKernelCodeGenerator>>,

    /// Configuration
    config: KernelOptimizationConfig,

    /// Statistics
    statistics: Arc<Mutex<KernelPerformanceStatistics>>,
}

/// Tensor Core optimization engine for mixed-precision acceleration
#[derive(Debug)]
pub struct TensorCoreOptimizationEngine {
    /// Available Tensor Core configurations
    available_configs: Vec<TensorCoreConfiguration>,

    /// Optimal precision selection for different operations
    precision_selector: PrecisionSelector,

    /// WMMA (Warp Matrix Multiply Accumulate) optimizer
    wmma_optimizer: WmmaOptimizer,

    /// Mixed-precision strategy manager
    mixed_precision_manager: MixedPrecisionManager,

    /// Tensor Core utilization tracker
    utilization_tracker: TensorCoreUtilizationTracker,

    /// Performance predictor for Tensor Core operations
    performance_predictor: TensorCorePerformancePredictor,
}

/// Memory optimization coordinator for CUDA kernels
#[derive(Debug)]
pub struct KernelMemoryOptimizer {
    /// Coalescing pattern analyzer
    coalescing_analyzer: CoalescingPatternAnalyzer,

    /// Shared memory tiling optimizer
    tiling_optimizer: SharedMemoryTilingOptimizer,

    /// Register blocking strategy manager
    register_blocker: RegisterBlockingManager,

    /// Bank conflict detector and resolver
    bank_conflict_resolver: BankConflictResolver,

    /// Memory access pattern optimizer
    access_pattern_optimizer: MemoryAccessPatternOptimizer,

    /// Cache utilization enhancer
    cache_utilization_enhancer: CacheUtilizationEnhancer,
}

/// Auto-tuning system for optimal kernel configurations
#[derive(Debug)]
pub struct KernelAutoTuner {
    /// Block size optimizer
    block_size_optimizer: BlockSizeOptimizer,

    /// Grid size calculator
    grid_size_calculator: GridSizeCalculator,

    /// Shared memory allocator
    shared_memory_allocator: SharedMemoryAllocator,

    /// Register usage optimizer
    register_optimizer: RegisterUsageOptimizer,

    /// Occupancy maximizer
    occupancy_maximizer: OccupancyMaximizer,

    /// Performance benchmark runner
    benchmark_runner: AutoTuningBenchmarkRunner,

    /// Configuration search space
    search_space: ConfigurationSearchSpace,

    /// Genetic algorithm for optimization
    genetic_optimizer: GeneticAlgorithmOptimizer,
}

/// Tensor Core configuration for different precision modes
#[derive(Debug, Clone)]
pub struct TensorCoreConfiguration {
    /// Compute capability requirement
    compute_capability: (u32, u32),

    /// Input precision
    input_precision: TensorCorePrecision,

    /// Output precision
    output_precision: TensorCorePrecision,

    /// Accumulator precision
    accumulator_precision: TensorCorePrecision,

    /// Matrix dimensions supported (M, N, K)
    supported_dimensions: Vec<(usize, usize, usize)>,

    /// Performance characteristics
    performance_profile: TensorCorePerformanceProfile,

    /// Memory bandwidth requirements
    memory_bandwidth: f64,

    /// Register usage
    register_usage: usize,
}

/// Tensor Core precision types
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum TensorCorePrecision {
    /// Half precision (FP16)
    Half,

    /// Brain float 16
    BFloat16,

    /// TensorFloat-32
    TensorFloat32,

    /// Single precision (FP32)
    Float32,

    /// Integer 8-bit
    Int8,

    /// Integer 4-bit
    Int4,

    /// Integer 1-bit (binary)
    Int1,
}

/// High-performance matrix multiplication kernel
#[derive(Debug)]
pub struct OptimizedMatMulKernel {
    /// Tensor Core implementation
    tensor_core_impl: TensorCoreMatMulImpl,

    /// Standard CUDA Cores implementation
    cuda_cores_impl: CudaCoresMatMulImpl,

    /// Mixed-precision implementation
    mixed_precision_impl: MixedPrecisionMatMulImpl,

    /// Tiled implementation for large matrices
    tiled_impl: TiledMatMulImpl,

    /// Fused implementations (MatMul + Bias + Activation)
    fused_implementations: Vec<FusedMatMulImpl>,

    /// Performance selector
    implementation_selector: MatMulImplementationSelector,
}

/// Optimized convolution kernel implementations
#[derive(Debug)]
pub struct OptimizedConvolutionKernel {
    /// Direct convolution implementation
    direct_impl: DirectConvolutionImpl,

    /// Winograd convolution implementation
    winograd_impl: WinogradConvolutionImpl,

    /// FFT-based convolution implementation
    fft_impl: FftConvolutionImpl,

    /// Depthwise separable convolution
    depthwise_impl: DepthwiseConvolutionImpl,

    /// Grouped convolution implementation
    grouped_impl: GroupedConvolutionImpl,

    /// Dilated convolution implementation
    dilated_impl: DilatedConvolutionImpl,
}

/// High-performance activation function kernels
#[derive(Debug)]
pub struct OptimizedActivationKernels {
    /// ReLU family implementations
    relu_implementations: ReLUImplementations,

    /// Sigmoid family implementations
    sigmoid_implementations: SigmoidImplementations,

    /// Tanh family implementations
    tanh_implementations: TanhImplementations,

    /// GELU implementations
    gelu_implementations: GeLUImplementations,

    /// Swish/SiLU implementations
    swish_implementations: SwishImplementations,

    /// Fused activation implementations
    fused_implementations: FusedActivationImplementations,
}

impl HighPerformanceKernelManager {
    /// Create new high-performance kernel manager
    pub fn new(config: KernelOptimizationConfig) -> CudaResult<Self> {
        let tensor_core_engine = Arc::new(Mutex::new(TensorCoreOptimizationEngine::new(
            &config.tensor_core_config,
        )?));

        let memory_optimizer = Arc::new(Mutex::new(KernelMemoryOptimizer::new(
            &config.memory_optimization_config,
        )?));

        let auto_tuner = Arc::new(Mutex::new(KernelAutoTuner::new(
            &config.auto_tuning_config,
        )?));

        let kernel_cache = Arc::new(RwLock::new(OptimizedKernelCache::new(config.cache_size)));

        let performance_monitor = Arc::new(Mutex::new(KernelPerformanceMonitor::new(
            &config.monitoring_config,
        )?));

        let code_generator = Arc::new(Mutex::new(DynamicKernelCodeGenerator::new(
            &config.code_generation_config,
        )?));

        let statistics = Arc::new(Mutex::new(KernelPerformanceStatistics::new()));

        Ok(Self {
            tensor_core_engine,
            memory_optimizer,
            auto_tuner,
            kernel_cache,
            performance_monitor,
            code_generator,
            config,
            statistics,
        })
    }

    /// Execute optimized matrix multiplication
    pub fn optimized_matmul<T>(
        &self,
        a: &ArrayView2<T>,
        b: &ArrayView2<T>,
        c: &mut Array2<T>,
        stream: &CudaStream,
    ) -> CudaResult<()>
    where
        T: TensorElement + Send + Sync,
    {
        let start_time = Instant::now();

        // Analyze operation characteristics
        let operation_signature = self.analyze_matmul_characteristics(a, b, c)?;

        // Check cache for optimized implementation
        if let Some(cached_impl) = self.get_cached_implementation(&operation_signature)? {
            return self.execute_cached_matmul(cached_impl, a, b, c, stream);
        }

        // Select optimal implementation strategy
        let implementation = self.select_matmul_implementation(&operation_signature)?;

        // Execute the operation
        let result = match implementation {
            MatMulImplementation::TensorCore => self.execute_tensor_core_matmul(a, b, c, stream),
            MatMulImplementation::CudaCores => self.execute_cuda_cores_matmul(a, b, c, stream),
            MatMulImplementation::MixedPrecision => {
                self.execute_mixed_precision_matmul(a, b, c, stream)
            }
            MatMulImplementation::Tiled => self.execute_tiled_matmul(a, b, c, stream),
        };

        // Record performance metrics
        let execution_time = start_time.elapsed();
        self.record_matmul_performance(&operation_signature, execution_time, &result)?;

        // Cache successful implementation
        if result.is_ok() {
            self.cache_implementation(operation_signature, implementation)?;
        }

        result
    }

    /// Execute optimized convolution
    pub fn optimized_convolution<T>(
        &self,
        input: &ArrayView2<T>,
        weight: &ArrayView2<T>,
        output: &mut Array2<T>,
        config: &ConvolutionConfig,
        stream: &CudaStream,
    ) -> CudaResult<()>
    where
        T: TensorElement + Send + Sync,
    {
        let start_time = Instant::now();

        // Analyze convolution characteristics
        let operation_signature =
            self.analyze_convolution_characteristics(input, weight, output, config)?;

        // Select optimal convolution implementation
        let implementation = self.select_convolution_implementation(&operation_signature)?;

        // Execute the convolution
        let result = match implementation {
            ConvolutionImplementation::Direct => {
                self.execute_direct_convolution(input, weight, output, config, stream)
            }
            ConvolutionImplementation::Winograd => {
                self.execute_winograd_convolution(input, weight, output, config, stream)
            }
            ConvolutionImplementation::FFT => {
                self.execute_fft_convolution(input, weight, output, config, stream)
            }
            ConvolutionImplementation::Depthwise => {
                self.execute_depthwise_convolution(input, weight, output, config, stream)
            }
        };

        // Record performance metrics
        let execution_time = start_time.elapsed();
        self.record_convolution_performance(&operation_signature, execution_time, &result)?;

        result
    }

    /// Execute optimized activation function
    pub fn optimized_activation<T>(
        &self,
        input: &ArrayView1<T>,
        output: &mut Array1<T>,
        activation_type: ActivationType,
        stream: &CudaStream,
    ) -> CudaResult<()>
    where
        T: TensorElement + Send + Sync,
    {
        let start_time = Instant::now();

        // Select optimal activation implementation
        let implementation = self.select_activation_implementation(input.len(), activation_type)?;

        // Execute the activation
        let result = match activation_type {
            ActivationType::ReLU => self.execute_optimized_relu(input, output, stream),
            ActivationType::Sigmoid => self.execute_optimized_sigmoid(input, output, stream),
            ActivationType::Tanh => self.execute_optimized_tanh(input, output, stream),
            ActivationType::GELU => self.execute_optimized_gelu(input, output, stream),
            ActivationType::Swish => self.execute_optimized_swish(input, output, stream),
        };

        // Record performance metrics
        let execution_time = start_time.elapsed();
        self.record_activation_performance(activation_type, input.len(), execution_time, &result)?;

        result
    }

    /// Auto-tune kernel for optimal performance
    pub fn auto_tune_kernel(
        &self,
        operation_type: KernelOperationType,
        problem_size: ProblemSize,
        target_device: u32,
    ) -> CudaResult<OptimalConfiguration> {
        let mut auto_tuner = self.auto_tuner.lock().expect("lock should not be poisoned");
        auto_tuner.tune_kernel(operation_type, problem_size, target_device)
    }

    /// Generate optimized kernel code dynamically
    pub fn generate_optimized_kernel(
        &self,
        operation_spec: KernelOperationSpec,
        optimization_hints: OptimizationHints,
    ) -> CudaResult<GeneratedKernel> {
        let mut code_generator = self.code_generator.lock().expect("lock should not be poisoned");
        code_generator.generate_kernel(operation_spec, optimization_hints)
    }

    /// Get comprehensive performance statistics
    pub fn get_performance_statistics(&self) -> CudaResult<KernelPerformanceReport> {
        let statistics = self.statistics.lock().expect("lock should not be poisoned");
        statistics.generate_comprehensive_report()
    }

    // Private implementation methods

    fn analyze_matmul_characteristics<T>(
        &self,
        a: &ArrayView2<T>,
        b: &ArrayView2<T>,
        c: &Array2<T>,
    ) -> CudaResult<MatMulOperationSignature>
    where
        T: TensorElement,
    {
        let m = a.nrows();
        let k = a.ncols();
        let n = b.ncols();

        // Analyze memory access patterns
        let memory_pattern = self.analyze_memory_access_pattern(a, b, c)?;

        // Check Tensor Core compatibility
        let tensor_core_compatible = self.check_tensor_core_compatibility(m, n, k)?;

        // Estimate computational intensity
        let computational_intensity = self.calculate_computational_intensity(m, n, k)?;

        Ok(MatMulOperationSignature {
            dimensions: (m, n, k),
            memory_pattern,
            tensor_core_compatible,
            computational_intensity,
            data_type: std::any::type_name::<T>().to_string(),
        })
    }

    fn select_matmul_implementation(
        &self,
        signature: &MatMulOperationSignature,
    ) -> CudaResult<MatMulImplementation> {
        // Use Tensor Cores for compatible operations
        if signature.tensor_core_compatible && signature.computational_intensity > 100.0 {
            return Ok(MatMulImplementation::TensorCore);
        }

        // Use tiled implementation for large matrices
        let (m, n, k) = signature.dimensions;
        if m > 1024 && n > 1024 && k > 1024 {
            return Ok(MatMulImplementation::Tiled);
        }

        // Use mixed precision for medium-sized operations
        if signature.computational_intensity > 50.0 {
            return Ok(MatMulImplementation::MixedPrecision);
        }

        // Fall back to standard CUDA Cores implementation
        Ok(MatMulImplementation::CudaCores)
    }

    fn execute_tensor_core_matmul<T>(
        &self,
        a: &ArrayView2<T>,
        b: &ArrayView2<T>,
        c: &mut Array2<T>,
        stream: &CudaStream,
    ) -> CudaResult<()>
    where
        T: TensorElement + Send + Sync,
    {
        let tensor_core_engine = self.tensor_core_engine.lock().expect("lock should not be poisoned");
        tensor_core_engine.execute_wmma_matmul(a, b, c, stream)
    }

    fn execute_cuda_cores_matmul<T>(
        &self,
        a: &ArrayView2<T>,
        b: &ArrayView2<T>,
        c: &mut Array2<T>,
        stream: &CudaStream,
    ) -> CudaResult<()>
    where
        T: TensorElement + Send + Sync,
    {
        // Implement optimized CUDA Cores matrix multiplication
        // with memory coalescing and shared memory tiling
        self.launch_tiled_matmul_kernel(a, b, c, stream)
    }

    fn execute_mixed_precision_matmul<T>(
        &self,
        a: &ArrayView2<T>,
        b: &ArrayView2<T>,
        c: &mut Array2<T>,
        stream: &CudaStream,
    ) -> CudaResult<()>
    where
        T: TensorElement + Send + Sync,
    {
        // Convert to half precision, compute, then convert back
        let tensor_core_engine = self.tensor_core_engine.lock().expect("lock should not be poisoned");
        tensor_core_engine.execute_mixed_precision_matmul(a, b, c, stream)
    }

    fn execute_tiled_matmul<T>(
        &self,
        a: &ArrayView2<T>,
        b: &ArrayView2<T>,
        c: &mut Array2<T>,
        stream: &CudaStream,
    ) -> CudaResult<()>
    where
        T: TensorElement + Send + Sync,
    {
        // Implement large matrix multiplication with optimal tiling
        self.launch_large_tiled_matmul_kernel(a, b, c, stream)
    }

    fn launch_tiled_matmul_kernel<T>(
        &self,
        a: &ArrayView2<T>,
        b: &ArrayView2<T>,
        c: &mut Array2<T>,
        stream: &CudaStream,
    ) -> CudaResult<()>
    where
        T: TensorElement + Send + Sync,
    {
        // This would contain the actual CUDA kernel launch
        // For now, we'll return a placeholder success
        Ok(())
    }

    fn launch_large_tiled_matmul_kernel<T>(
        &self,
        a: &ArrayView2<T>,
        b: &ArrayView2<T>,
        c: &mut Array2<T>,
        stream: &CudaStream,
    ) -> CudaResult<()>
    where
        T: TensorElement + Send + Sync,
    {
        // This would contain the actual large matrix CUDA kernel launch
        // For now, we'll return a placeholder success
        Ok(())
    }

    fn get_cached_implementation(
        &self,
        signature: &MatMulOperationSignature,
    ) -> CudaResult<Option<CachedImplementation>> {
        let cache = self.kernel_cache.read().expect("lock should not be poisoned");
        Ok(cache.get_implementation(signature))
    }

    fn execute_cached_matmul<T>(
        &self,
        cached_impl: CachedImplementation,
        a: &ArrayView2<T>,
        b: &ArrayView2<T>,
        c: &mut Array2<T>,
        stream: &CudaStream,
    ) -> CudaResult<()>
    where
        T: TensorElement + Send + Sync,
    {
        // Execute the cached implementation
        cached_impl.execute(a, b, c, stream)
    }

    fn cache_implementation(
        &self,
        signature: MatMulOperationSignature,
        implementation: MatMulImplementation,
    ) -> CudaResult<()> {
        let mut cache = self.kernel_cache.write().expect("lock should not be poisoned");
        cache.store_implementation(signature, implementation);
        Ok(())
    }

    fn record_matmul_performance(
        &self,
        signature: &MatMulOperationSignature,
        execution_time: Duration,
        result: &CudaResult<()>,
    ) -> CudaResult<()> {
        let mut statistics = self.statistics.lock().expect("lock should not be poisoned");
        statistics.record_matmul_performance(signature, execution_time, result.is_ok());
        Ok(())
    }

    fn analyze_memory_access_pattern<T>(
        &self,
        a: &ArrayView2<T>,
        b: &ArrayView2<T>,
        c: &Array2<T>,
    ) -> CudaResult<MemoryAccessPattern>
    where
        T: TensorElement,
    {
        // Analyze stride patterns and memory layout
        Ok(MemoryAccessPattern::Coalesced) // Placeholder
    }

    fn check_tensor_core_compatibility(&self, m: usize, n: usize, k: usize) -> CudaResult<bool> {
        // Check if dimensions are compatible with Tensor Core operations
        // Tensor Cores typically require specific alignment (e.g., multiples of 8)
        Ok(m % 8 == 0 && n % 8 == 0 && k % 8 == 0)
    }

    fn calculate_computational_intensity(&self, m: usize, n: usize, k: usize) -> CudaResult<f64> {
        // Calculate FLOPs per byte ratio
        let flops = 2.0 * m as f64 * n as f64 * k as f64;
        let bytes = (m * k + k * n + m * n) as f64 * 4.0; // Assuming f32
        Ok(flops / bytes)
    }

    // Placeholder implementations for other methods
    fn analyze_convolution_characteristics<T>(
        &self,
        input: &ArrayView2<T>,
        weight: &ArrayView2<T>,
        output: &Array2<T>,
        config: &ConvolutionConfig,
    ) -> CudaResult<ConvolutionOperationSignature>
    where
        T: TensorElement,
    {
        Ok(ConvolutionOperationSignature::default())
    }

    fn select_convolution_implementation(
        &self,
        signature: &ConvolutionOperationSignature,
    ) -> CudaResult<ConvolutionImplementation> {
        Ok(ConvolutionImplementation::Direct)
    }

    fn execute_direct_convolution<T>(
        &self,
        input: &ArrayView2<T>,
        weight: &ArrayView2<T>,
        output: &mut Array2<T>,
        config: &ConvolutionConfig,
        stream: &CudaStream,
    ) -> CudaResult<()>
    where
        T: TensorElement + Send + Sync,
    {
        Ok(())
    }

    fn execute_winograd_convolution<T>(
        &self,
        input: &ArrayView2<T>,
        weight: &ArrayView2<T>,
        output: &mut Array2<T>,
        config: &ConvolutionConfig,
        stream: &CudaStream,
    ) -> CudaResult<()>
    where
        T: TensorElement + Send + Sync,
    {
        Ok(())
    }

    fn execute_fft_convolution<T>(
        &self,
        input: &ArrayView2<T>,
        weight: &ArrayView2<T>,
        output: &mut Array2<T>,
        config: &ConvolutionConfig,
        stream: &CudaStream,
    ) -> CudaResult<()>
    where
        T: TensorElement + Send + Sync,
    {
        Ok(())
    }

    fn execute_depthwise_convolution<T>(
        &self,
        input: &ArrayView2<T>,
        weight: &ArrayView2<T>,
        output: &mut Array2<T>,
        config: &ConvolutionConfig,
        stream: &CudaStream,
    ) -> CudaResult<()>
    where
        T: TensorElement + Send + Sync,
    {
        Ok(())
    }

    fn record_convolution_performance(
        &self,
        signature: &ConvolutionOperationSignature,
        execution_time: Duration,
        result: &CudaResult<()>,
    ) -> CudaResult<()> {
        Ok(())
    }

    fn select_activation_implementation(
        &self,
        size: usize,
        activation_type: ActivationType,
    ) -> CudaResult<ActivationImplementation> {
        Ok(ActivationImplementation::Vectorized)
    }

    fn execute_optimized_relu<T>(
        &self,
        input: &ArrayView1<T>,
        output: &mut Array1<T>,
        stream: &CudaStream,
    ) -> CudaResult<()>
    where
        T: TensorElement + Send + Sync,
    {
        Ok(())
    }

    fn execute_optimized_sigmoid<T>(
        &self,
        input: &ArrayView1<T>,
        output: &mut Array1<T>,
        stream: &CudaStream,
    ) -> CudaResult<()>
    where
        T: TensorElement + Send + Sync,
    {
        Ok(())
    }

    fn execute_optimized_tanh<T>(
        &self,
        input: &ArrayView1<T>,
        output: &mut Array1<T>,
        stream: &CudaStream,
    ) -> CudaResult<()>
    where
        T: TensorElement + Send + Sync,
    {
        Ok(())
    }

    fn execute_optimized_gelu<T>(
        &self,
        input: &ArrayView1<T>,
        output: &mut Array1<T>,
        stream: &CudaStream,
    ) -> CudaResult<()>
    where
        T: TensorElement + Send + Sync,
    {
        Ok(())
    }

    fn execute_optimized_swish<T>(
        &self,
        input: &ArrayView1<T>,
        output: &mut Array1<T>,
        stream: &CudaStream,
    ) -> CudaResult<()>
    where
        T: TensorElement + Send + Sync,
    {
        Ok(())
    }

    fn record_activation_performance(
        &self,
        activation_type: ActivationType,
        size: usize,
        execution_time: Duration,
        result: &CudaResult<()>,
    ) -> CudaResult<()> {
        Ok(())
    }
}

// Supporting types and implementations

/// Matrix multiplication implementation types
#[derive(Debug, Clone, Copy)]
pub enum MatMulImplementation {
    TensorCore,
    CudaCores,
    MixedPrecision,
    Tiled,
}

/// Convolution implementation types
#[derive(Debug, Clone, Copy)]
pub enum ConvolutionImplementation {
    Direct,
    Winograd,
    FFT,
    Depthwise,
}

/// Activation implementation types
#[derive(Debug, Clone, Copy)]
pub enum ActivationImplementation {
    Vectorized,
    Fused,
    MemoryOptimized,
}

/// Activation function types
#[derive(Debug, Clone, Copy)]
pub enum ActivationType {
    ReLU,
    Sigmoid,
    Tanh,
    GELU,
    Swish,
}

/// Memory access pattern types
#[derive(Debug, Clone, Copy)]
pub enum MemoryAccessPattern {
    Coalesced,
    Strided,
    Random,
}

/// Matrix multiplication operation signature
#[derive(Debug, Clone)]
pub struct MatMulOperationSignature {
    pub dimensions: (usize, usize, usize),
    pub memory_pattern: MemoryAccessPattern,
    pub tensor_core_compatible: bool,
    pub computational_intensity: f64,
    pub data_type: String,
}

/// Convolution operation signature
#[derive(Debug, Clone, Default)]
pub struct ConvolutionOperationSignature {
    pub input_dimensions: (usize, usize, usize, usize),
    pub kernel_size: (usize, usize),
    pub stride: (usize, usize),
    pub padding: (usize, usize),
    pub data_type: String,
}

/// Convolution configuration
#[derive(Debug, Clone)]
pub struct ConvolutionConfig {
    pub stride: (usize, usize),
    pub padding: (usize, usize),
    pub dilation: (usize, usize),
    pub groups: usize,
}

/// Kernel optimization configuration
#[derive(Debug, Clone)]
pub struct KernelOptimizationConfig {
    pub tensor_core_config: TensorCoreOptimizationConfig,
    pub memory_optimization_config: MemoryOptimizationConfig,
    pub auto_tuning_config: AutoTuningConfig,
    pub cache_size: usize,
    pub monitoring_config: MonitoringConfig,
    pub code_generation_config: CodeGenerationConfig,
}

/// Tensor Core optimization configuration
#[derive(Debug, Clone)]
pub struct TensorCoreOptimizationConfig {
    pub enable_mixed_precision: bool,
    pub preferred_precision: TensorCorePrecision,
    pub fallback_to_cuda_cores: bool,
}

/// Memory optimization configuration
#[derive(Debug, Clone)]
pub struct MemoryOptimizationConfig {
    pub enable_coalescing_optimization: bool,
    pub enable_shared_memory_tiling: bool,
    pub enable_register_blocking: bool,
    pub tile_size: usize,
}

/// Auto-tuning configuration
#[derive(Debug, Clone)]
pub struct AutoTuningConfig {
    pub enable_auto_tuning: bool,
    pub max_tuning_iterations: usize,
    pub performance_threshold: f64,
}

/// Monitoring configuration
#[derive(Debug, Clone)]
pub struct MonitoringConfig {
    pub enable_performance_monitoring: bool,
    pub sampling_rate: Duration,
    pub metrics_retention_period: Duration,
}

/// Code generation configuration
#[derive(Debug, Clone)]
pub struct CodeGenerationConfig {
    pub enable_dynamic_generation: bool,
    pub optimization_level: u32,
    pub include_debug_info: bool,
}

// Placeholder implementations for supporting structures

impl TensorCoreOptimizationEngine {
    pub fn new(config: &TensorCoreOptimizationConfig) -> CudaResult<Self> {
        Ok(Self {
            available_configs: Vec::new(),
            precision_selector: PrecisionSelector::new(),
            wmma_optimizer: WmmaOptimizer::new(),
            mixed_precision_manager: MixedPrecisionManager::new(),
            utilization_tracker: TensorCoreUtilizationTracker::new(),
            performance_predictor: TensorCorePerformancePredictor::new(),
        })
    }

    pub fn execute_wmma_matmul<T>(
        &self,
        a: &ArrayView2<T>,
        b: &ArrayView2<T>,
        c: &mut Array2<T>,
        stream: &CudaStream,
    ) -> CudaResult<()>
    where
        T: TensorElement + Send + Sync,
    {
        Ok(())
    }

    pub fn execute_mixed_precision_matmul<T>(
        &self,
        a: &ArrayView2<T>,
        b: &ArrayView2<T>,
        c: &mut Array2<T>,
        stream: &CudaStream,
    ) -> CudaResult<()>
    where
        T: TensorElement + Send + Sync,
    {
        Ok(())
    }
}

impl KernelMemoryOptimizer {
    pub fn new(config: &MemoryOptimizationConfig) -> CudaResult<Self> {
        Ok(Self {
            coalescing_analyzer: CoalescingPatternAnalyzer::new(),
            tiling_optimizer: SharedMemoryTilingOptimizer::new(),
            register_blocker: RegisterBlockingManager::new(),
            bank_conflict_resolver: BankConflictResolver::new(),
            access_pattern_optimizer: MemoryAccessPatternOptimizer::new(),
            cache_utilization_enhancer: CacheUtilizationEnhancer::new(),
        })
    }
}

impl KernelAutoTuner {
    pub fn new(config: &AutoTuningConfig) -> CudaResult<Self> {
        Ok(Self {
            block_size_optimizer: BlockSizeOptimizer::new(),
            grid_size_calculator: GridSizeCalculator::new(),
            shared_memory_allocator: SharedMemoryAllocator::new(),
            register_optimizer: RegisterUsageOptimizer::new(),
            occupancy_maximizer: OccupancyMaximizer::new(),
            benchmark_runner: AutoTuningBenchmarkRunner::new(),
            search_space: ConfigurationSearchSpace::new(),
            genetic_optimizer: GeneticAlgorithmOptimizer::new(),
        })
    }

    pub fn tune_kernel(
        &mut self,
        operation_type: KernelOperationType,
        problem_size: ProblemSize,
        target_device: u32,
    ) -> CudaResult<OptimalConfiguration> {
        Ok(OptimalConfiguration::default())
    }
}

// Macro for generating placeholder structures
macro_rules! impl_placeholder_struct {
    ($name:ident) => {
        #[derive(Debug)]
        pub struct $name;

        impl $name {
            pub fn new() -> Self {
                Self
            }
        }
    };
}

// Generate placeholder implementations
impl_placeholder_struct!(PrecisionSelector);
impl_placeholder_struct!(WmmaOptimizer);
impl_placeholder_struct!(MixedPrecisionManager);
impl_placeholder_struct!(TensorCoreUtilizationTracker);
impl_placeholder_struct!(TensorCorePerformancePredictor);
impl_placeholder_struct!(CoalescingPatternAnalyzer);
impl_placeholder_struct!(SharedMemoryTilingOptimizer);
impl_placeholder_struct!(RegisterBlockingManager);
impl_placeholder_struct!(BankConflictResolver);
impl_placeholder_struct!(MemoryAccessPatternOptimizer);
impl_placeholder_struct!(CacheUtilizationEnhancer);
impl_placeholder_struct!(BlockSizeOptimizer);
impl_placeholder_struct!(GridSizeCalculator);
impl_placeholder_struct!(SharedMemoryAllocator);
impl_placeholder_struct!(RegisterUsageOptimizer);
impl_placeholder_struct!(OccupancyMaximizer);
impl_placeholder_struct!(AutoTuningBenchmarkRunner);
impl_placeholder_struct!(ConfigurationSearchSpace);
impl_placeholder_struct!(GeneticAlgorithmOptimizer);
// These structs have explicit implementations below, so we don't use the macro
#[derive(Debug)]
pub struct OptimizedKernelCache;
#[derive(Debug)]
pub struct KernelPerformanceMonitor;
#[derive(Debug)]
pub struct DynamicKernelCodeGenerator;
#[derive(Debug)]
pub struct KernelPerformanceStatistics;

impl OptimizedKernelCache {
    pub fn new(_cache_size: usize) -> Self {
        Self
    }

    pub fn get_implementation(
        &self,
        signature: &MatMulOperationSignature,
    ) -> Option<CachedImplementation> {
        None
    }

    pub fn store_implementation(
        &mut self,
        signature: MatMulOperationSignature,
        implementation: MatMulImplementation,
    ) {
    }
}

impl KernelPerformanceMonitor {
    pub fn new(config: &MonitoringConfig) -> CudaResult<Self> {
        Ok(Self)
    }
}

impl DynamicKernelCodeGenerator {
    pub fn new(config: &CodeGenerationConfig) -> CudaResult<Self> {
        Ok(Self)
    }

    pub fn generate_kernel(
        &mut self,
        operation_spec: KernelOperationSpec,
        optimization_hints: OptimizationHints,
    ) -> CudaResult<GeneratedKernel> {
        Ok(GeneratedKernel::default())
    }
}

impl KernelPerformanceStatistics {
    pub fn new() -> Self {
        Self
    }

    pub fn record_matmul_performance(
        &mut self,
        signature: &MatMulOperationSignature,
        execution_time: Duration,
        success: bool,
    ) {
    }

    pub fn generate_comprehensive_report(&self) -> CudaResult<KernelPerformanceReport> {
        Ok(KernelPerformanceReport::default())
    }
}

// Additional placeholder types
#[derive(Debug, Clone)]
pub struct CachedImplementation;

impl CachedImplementation {
    pub fn execute<T>(
        &self,
        a: &ArrayView2<T>,
        b: &ArrayView2<T>,
        c: &mut Array2<T>,
        stream: &CudaStream,
    ) -> CudaResult<()>
    where
        T: TensorElement + Send + Sync,
    {
        Ok(())
    }
}

#[derive(Debug, Clone)]
pub struct TensorCorePerformanceProfile {
    pub throughput: f64,
    pub latency: Duration,
    pub efficiency: f64,
}

#[derive(Debug, Clone, Copy)]
pub enum KernelOperationType {
    MatMul,
    Convolution,
    Activation,
    Reduction,
}

#[derive(Debug, Clone)]
pub struct ProblemSize {
    pub dimensions: Vec<usize>,
    pub data_size: usize,
}

#[derive(Debug, Clone, Default)]
pub struct OptimalConfiguration {
    pub block_size: (u32, u32, u32),
    pub grid_size: (u32, u32, u32),
    pub shared_memory: usize,
    pub registers_per_thread: u32,
}

#[derive(Debug, Clone)]
pub struct KernelOperationSpec {
    pub operation_type: KernelOperationType,
    pub input_shapes: Vec<Vec<usize>>,
    pub output_shape: Vec<usize>,
    pub parameters: HashMap<String, f64>,
}

#[derive(Debug, Clone)]
pub struct OptimizationHints {
    pub prefer_tensor_cores: bool,
    pub optimize_for_latency: bool,
    pub optimize_for_throughput: bool,
    pub memory_budget: usize,
}

#[derive(Debug, Clone, Default)]
pub struct GeneratedKernel {
    pub source_code: String,
    pub binary_code: Vec<u8>,
    pub configuration: OptimalConfiguration,
}

#[derive(Debug, Clone, Default)]
pub struct KernelPerformanceReport {
    pub total_operations: u64,
    pub average_execution_time: Duration,
    pub peak_throughput: f64,
    pub cache_hit_rate: f64,
    pub tensor_core_utilization: f64,
}

#[cfg(test)]
mod tests {
    use super::*;
    use scirs2_core::ndarray::{Array1, Array2};

    #[test]
    fn test_kernel_manager_creation() {
        let config = KernelOptimizationConfig {
            tensor_core_config: TensorCoreOptimizationConfig {
                enable_mixed_precision: true,
                preferred_precision: TensorCorePrecision::Half,
                fallback_to_cuda_cores: true,
            },
            memory_optimization_config: MemoryOptimizationConfig {
                enable_coalescing_optimization: true,
                enable_shared_memory_tiling: true,
                enable_register_blocking: true,
                tile_size: 16,
            },
            auto_tuning_config: AutoTuningConfig {
                enable_auto_tuning: true,
                max_tuning_iterations: 100,
                performance_threshold: 0.95,
            },
            cache_size: 1024,
            monitoring_config: MonitoringConfig {
                enable_performance_monitoring: true,
                sampling_rate: Duration::from_millis(10),
                metrics_retention_period: Duration::from_secs(3600),
            },
            code_generation_config: CodeGenerationConfig {
                enable_dynamic_generation: true,
                optimization_level: 3,
                include_debug_info: false,
            },
        };

        // This test would require CUDA to be available
        // For now, just test the config structure
        assert!(config.tensor_core_config.enable_mixed_precision);
        assert_eq!(config.cache_size, 1024);
    }

    #[test]
    fn test_tensor_core_precision_types() {
        assert_eq!(TensorCorePrecision::Half, TensorCorePrecision::Half);
        assert_ne!(TensorCorePrecision::Half, TensorCorePrecision::Float32);
    }

    #[test]
    fn test_matmul_signature_creation() {
        let signature = MatMulOperationSignature {
            dimensions: (128, 256, 512),
            memory_pattern: MemoryAccessPattern::Coalesced,
            tensor_core_compatible: true,
            computational_intensity: 85.6,
            data_type: "f32".to_string(),
        };

        assert_eq!(signature.dimensions, (128, 256, 512));
        assert!(signature.tensor_core_compatible);
        assert!(signature.computational_intensity > 80.0);
    }
}