torsh-backend 0.1.2

//! Backend-Specific Tuning Strategies
//!
//! This module contains implementations of tuning strategies for different compute backends,
//! each optimized for the specific characteristics and capabilities of their target hardware.

use super::types::*;

// Re-export strategy implementations for coordination module
pub use super::types::{
    CpuTuningStrategy, CudaTuningStrategy, MetalTuningStrategy, WebGpuTuningStrategy,
};
use crate::backend::BackendType;
use crate::error::BackendResult;
use std::collections::HashMap;
use std::time::Duration;

// ================================================================================================
// CPU Tuning Strategy Implementation
// ================================================================================================

impl CpuTuningStrategy {
    pub fn new() -> BackendResult<Self> {
        Ok(Self {})
    }
}

impl BackendTuningStrategy for CpuTuningStrategy {
    fn backend_type(&self) -> BackendType {
        BackendType::Cpu
    }

    fn tune_for_workload(
        &self,
        workload: &WorkloadCharacteristics,
        system_state: &SystemState,
        _constraints: &TuningConstraints,
    ) -> BackendResult<TuningRecommendation> {
        // CPU-specific tuning logic
        let thread_count = if workload.parallelization_potential > 0.8 {
            num_cpus::get()
        } else {
            (num_cpus::get() / 2).max(1)
        };

        let vector_width = if workload.vectorization_potential > 0.7 {
            256 // Use AVX2 if available
        } else {
            128 // Fall back to SSE
        };

        let parameters = TuningParameters {
            thread_count,
            vector_width,
            block_size: Some(1024),
            tile_size: None,
            unroll_factor: 4,
            scheduling_strategy: if system_state.concurrent_workloads > 2 {
                SchedulingStrategy::Dynamic
            } else {
                SchedulingStrategy::Static
            },
            memory_allocation_strategy: if system_state.numa_topology.node_count > 1 {
                MemoryAllocationStrategy::NumaLocal
            } else {
                MemoryAllocationStrategy::Default
            },
            optimization_level: OptimizationLevel::Optimized,
            backend_specific: HashMap::new(),
        };

        let prediction = PerformancePrediction {
            execution_time: Duration::from_millis(100), // Placeholder
            throughput: 1e6,
            memory_usage: workload.data_size,
            power_consumption: 50.0,
            cache_efficiency: 0.85,
            thermal_impact: 5.0,
            confidence_interval: (0.8, 1.2),
        };

        Ok(TuningRecommendation {
            parameters,
            expected_performance: prediction,
            confidence_score: 0.9,
            alternative_configs: Vec::new(),
            reasoning: "CPU tuning based on parallelization and vectorization potential"
                .to_string(),
        })
    }

    fn update_from_feedback(&mut self, _feedback: &PerformanceFeedback) -> BackendResult<()> {
        // Update CPU strategy based on feedback
        Ok(())
    }

    fn get_strategy_metrics(&self) -> BackendResult<StrategyMetrics> {
        Ok(StrategyMetrics {
            prediction_accuracy: 0.85,
            optimization_success_rate: 0.9,
            average_speedup: 2.1,
            energy_efficiency_improvement: 0.15,
            total_optimizations: 1000,
            strategy_overhead: Duration::from_micros(50),
        })
    }

    fn predict_performance(
        &self,
        _workload: &WorkloadCharacteristics,
        _parameters: &TuningParameters,
    ) -> BackendResult<PerformancePrediction> {
        // Implement CPU performance prediction
        Ok(PerformancePrediction {
            execution_time: Duration::from_millis(100),
            throughput: 1e6,
            memory_usage: 1024 * 1024,
            power_consumption: 50.0,
            cache_efficiency: 0.85,
            thermal_impact: 5.0,
            confidence_interval: (0.8, 1.2),
        })
    }
}

// ================================================================================================
// CUDA Tuning Strategy Implementation
// ================================================================================================

impl CudaTuningStrategy {
    pub fn new() -> BackendResult<Self> {
        Ok(Self {})
    }
}

impl BackendTuningStrategy for CudaTuningStrategy {
    fn backend_type(&self) -> BackendType {
        BackendType::Cuda
    }

    fn tune_for_workload(
        &self,
        workload: &WorkloadCharacteristics,
        system_state: &SystemState,
        constraints: &TuningConstraints,
    ) -> BackendResult<TuningRecommendation> {
        // CUDA-specific tuning logic
        let mut backend_specific = HashMap::new();

        // Determine optimal block size based on operation type
        let block_size = match workload.operation_type {
            OperationType::MatrixMultiply => {
                if workload.data_size > 1024 * 1024 {
                    Some(256) // Large matrices benefit from larger blocks
                } else {
                    Some(128) // Smaller matrices use smaller blocks
                }
            }
            OperationType::Convolution2D => Some(128),
            OperationType::ElementWise => Some(256),
            _ => Some(128),
        };

        // Determine grid dimensions for CUDA
        let grid_size = match workload.operation_type {
            OperationType::MatrixMultiply => {
                let n = (workload.data_size as f64).sqrt() as usize;
                let blocks_per_dim = (n + 15) / 16; // 16x16 thread blocks
                (blocks_per_dim, blocks_per_dim, 1)
            }
            OperationType::ElementWise => {
                let total_threads = workload.data_size;
                let threads_per_block = block_size.unwrap_or(256);
                let blocks = (total_threads + threads_per_block - 1) / threads_per_block;
                (blocks, 1, 1)
            }
            _ => (256, 1, 1),
        };

        backend_specific.insert(
            "grid_size".to_string(),
            TuningValue::Array(vec![
                TuningValue::Integer(grid_size.0 as i64),
                TuningValue::Integer(grid_size.1 as i64),
                TuningValue::Integer(grid_size.2 as i64),
            ]),
        );

        // Occupancy optimization
        let target_occupancy = if workload.compute_intensity > 0.8 {
            0.75 // High compute intensity - aim for high occupancy
        } else {
            0.5 // Memory-bound workloads may not need full occupancy
        };
        backend_specific.insert(
            "target_occupancy".to_string(),
            TuningValue::Float(target_occupancy),
        );

        // Memory allocation strategy based on thermal and power constraints
        let memory_strategy = if system_state.thermal_state.thermal_throttling_active {
            MemoryAllocationStrategy::Default // Conservative under thermal stress
        } else if let Some(power_limit) = constraints.max_power_draw {
            if system_state.power_state.current_power_draw / power_limit > 0.8 {
                MemoryAllocationStrategy::Default
            } else {
                MemoryAllocationStrategy::Pinned // Higher performance when power allows
            }
        } else {
            MemoryAllocationStrategy::Unified // Use unified memory for simplicity
        };

        // Tensor Cores optimization for compatible operations
        let use_tensor_cores = matches!(
            workload.operation_type,
            OperationType::MatrixMultiply | OperationType::Convolution2D
        ) && matches!(workload.data_type, DataType::F16 | DataType::F32);
        backend_specific.insert(
            "use_tensor_cores".to_string(),
            TuningValue::Boolean(use_tensor_cores),
        );

        // Shared memory optimization
        let shared_memory_size = match workload.operation_type {
            OperationType::MatrixMultiply => 48 * 1024, // Use most of available shared memory
            OperationType::Convolution2D => 32 * 1024,
            _ => 16 * 1024,
        };
        backend_specific.insert(
            "shared_memory_size".to_string(),
            TuningValue::Integer(shared_memory_size),
        );

        let parameters = TuningParameters {
            thread_count: 1024, // GPU threads per block
            vector_width: 128,  // CUDA warp size considerations
            block_size,
            tile_size: if matches!(workload.operation_type, OperationType::MatrixMultiply) {
                Some((16, 16)) // Optimal tile size for matrix operations
            } else {
                None
            },
            unroll_factor: if workload.branch_predictability > 0.9 {
                8
            } else {
                4
            },
            scheduling_strategy: SchedulingStrategy::Static, // GPU scheduling is typically static
            memory_allocation_strategy: memory_strategy,
            optimization_level: if system_state.thermal_state.thermal_throttling_active {
                OptimizationLevel::Default
            } else {
                OptimizationLevel::Aggressive
            },
            backend_specific,
        };

        // Performance prediction based on workload characteristics
        let base_execution_time = match workload.operation_type {
            OperationType::MatrixMultiply => {
                let n = (workload.data_size as f64).sqrt();
                Duration::from_nanos((n * n * n / 1e9) as u64) // O(n³) complexity
            }
            OperationType::ElementWise => {
                Duration::from_nanos((workload.data_size as f64 / 1e6) as u64) // Memory bandwidth bound
            }
            _ => Duration::from_millis(10),
        };

        // Apply thermal and power scaling
        let thermal_factor = if system_state.thermal_state.thermal_throttling_active {
            1.5
        } else {
            1.0
        };
        let power_factor =
            if system_state.power_state.power_efficiency_mode == PowerEfficiencyMode::PowerSaver {
                1.3
            } else {
                1.0
            };

        let execution_time = Duration::from_nanos(
            (base_execution_time.as_nanos() as f64 * thermal_factor * power_factor) as u64,
        );

        let prediction = PerformancePrediction {
            execution_time,
            throughput: workload.data_size as f64 / execution_time.as_secs_f64(),
            memory_usage: workload.data_size,
            power_consumption: if use_tensor_cores { 250.0 } else { 200.0 },
            cache_efficiency: 0.75, // GPU L2 cache efficiency
            thermal_impact: if use_tensor_cores { 15.0 } else { 10.0 },
            confidence_interval: (0.7, 1.3),
        };

        Ok(TuningRecommendation {
            parameters,
            expected_performance: prediction,
            confidence_score: 0.85,
            alternative_configs: Vec::new(),
            reasoning: format!(
                "CUDA tuning for {:?} with tensor cores: {}, thermal throttling: {}",
                workload.operation_type,
                use_tensor_cores,
                system_state.thermal_state.thermal_throttling_active
            ),
        })
    }

    fn update_from_feedback(&mut self, _feedback: &PerformanceFeedback) -> BackendResult<()> {
        Ok(())
    }

    fn get_strategy_metrics(&self) -> BackendResult<StrategyMetrics> {
        Ok(StrategyMetrics {
            prediction_accuracy: 0.8,
            optimization_success_rate: 0.85,
            average_speedup: 3.5,
            energy_efficiency_improvement: 0.2,
            total_optimizations: 500,
            strategy_overhead: Duration::from_micros(100),
        })
    }

    fn predict_performance(
        &self,
        workload: &WorkloadCharacteristics,
        parameters: &TuningParameters,
    ) -> BackendResult<PerformancePrediction> {
        // CUDA performance prediction based on workload and parameters
        let use_tensor_cores = parameters
            .backend_specific
            .get("use_tensor_cores")
            .and_then(|v| {
                if let TuningValue::Boolean(b) = v {
                    Some(*b)
                } else {
                    None
                }
            })
            .unwrap_or(false);

        let target_occupancy = parameters
            .backend_specific
            .get("target_occupancy")
            .and_then(|v| {
                if let TuningValue::Float(f) = v {
                    Some(*f)
                } else {
                    None
                }
            })
            .unwrap_or(0.5);

        // Base performance calculation
        let base_gflops = if use_tensor_cores {
            match workload.data_type {
                DataType::F16 => 125.0e12, // ~125 TFLOPS for FP16 Tensor Cores
                DataType::F32 => 62.5e12,  // ~62.5 TFLOPS for FP32 Tensor Cores
                _ => 31.4e12,              // Standard CUDA cores
            }
        } else {
            31.4e12 // RTX 4090 peak FP32 performance
        };

        // Calculate theoretical execution time
        let ops_count = match workload.operation_type {
            OperationType::MatrixMultiply => {
                let n = (workload.data_size as f64).sqrt();
                2.0 * n * n * n // 2n³ operations for matrix multiplication
            }
            OperationType::Convolution2D => {
                // Simplified: assume square kernels and reasonable parameters
                workload.data_size as f64 * 9.0 // 3x3 kernel approximation
            }
            OperationType::ElementWise => workload.data_size as f64,
            _ => workload.data_size as f64,
        };

        // Apply occupancy and efficiency factors
        let occupancy_factor = target_occupancy;
        let memory_efficiency = match workload.access_pattern {
            AccessPattern::Sequential => 0.9,
            AccessPattern::Strided { stride } => {
                if stride <= 4 {
                    0.7
                } else {
                    0.4
                }
            }
            AccessPattern::Random => 0.3,
            _ => 0.6,
        };

        let effective_gflops = base_gflops * occupancy_factor * memory_efficiency;
        let execution_time = Duration::from_secs_f64(ops_count / effective_gflops);

        // Memory usage prediction
        let memory_overhead = match parameters.memory_allocation_strategy {
            MemoryAllocationStrategy::Pinned => 1.2,
            MemoryAllocationStrategy::Unified => 1.1,
            _ => 1.0,
        };

        let shared_memory_size = parameters
            .backend_specific
            .get("shared_memory_size")
            .and_then(|v| {
                if let TuningValue::Integer(i) = v {
                    Some(*i as usize)
                } else {
                    None
                }
            })
            .unwrap_or(16384);

        let total_memory =
            (workload.data_size as f64 * memory_overhead) as usize + shared_memory_size;

        // Power consumption prediction
        let base_power = if use_tensor_cores { 400.0 } else { 300.0 }; // Watts
        let utilization_factor = occupancy_factor.min(1.0) as f32;
        let power_consumption = base_power * utilization_factor;

        // Thermal impact based on power and efficiency
        let thermal_impact = power_consumption * 0.05; // 5% of power as temperature increase

        Ok(PerformancePrediction {
            execution_time,
            throughput: workload.data_size as f64 / execution_time.as_secs_f64(),
            memory_usage: total_memory,
            power_consumption,
            cache_efficiency: memory_efficiency,
            thermal_impact,
            confidence_interval: (0.75, 1.25),
        })
    }
}

// ================================================================================================
// Metal Tuning Strategy Implementation
// ================================================================================================

impl MetalTuningStrategy {
    pub fn new() -> BackendResult<Self> {
        Ok(Self {})
    }
}

impl BackendTuningStrategy for MetalTuningStrategy {
    fn backend_type(&self) -> BackendType {
        BackendType::Metal
    }

    fn tune_for_workload(
        &self,
        workload: &WorkloadCharacteristics,
        system_state: &SystemState,
        _constraints: &TuningConstraints,
    ) -> BackendResult<TuningRecommendation> {
        // Metal-specific tuning logic
        let mut backend_specific = HashMap::new();

        // Apple Silicon optimization
        let is_apple_silicon = true; // Assume Apple Silicon for Metal backend
        let neural_engine_available = is_apple_silicon
            && matches!(
                workload.operation_type,
                OperationType::MatrixMultiply | OperationType::Convolution2D
            );

        // Threadgroup size optimization for Metal
        let threadgroup_size = match workload.operation_type {
            OperationType::MatrixMultiply => {
                if workload.data_size > 512 * 512 {
                    (16, 16) // Large matrices
                } else {
                    (8, 8) // Smaller matrices
                }
            }
            OperationType::Convolution2D => (8, 8),
            OperationType::ElementWise => (64, 1),
            _ => (32, 1),
        };

        backend_specific.insert(
            "threadgroup_size".to_string(),
            TuningValue::Array(vec![
                TuningValue::Integer(threadgroup_size.0),
                TuningValue::Integer(threadgroup_size.1),
            ]),
        );

        // Threadgroups per grid calculation
        let threadgroups_per_grid = match workload.operation_type {
            OperationType::MatrixMultiply => {
                let n = (workload.data_size as f64).sqrt() as usize;
                let groups_per_dim =
                    (n + threadgroup_size.0 as usize - 1) / threadgroup_size.0 as usize;
                (groups_per_dim, groups_per_dim, 1)
            }
            OperationType::ElementWise => {
                let total_threads = workload.data_size;
                let threads_per_group = (threadgroup_size.0 * threadgroup_size.1) as usize;
                let groups = (total_threads + threads_per_group - 1) / threads_per_group;
                (groups, 1, 1)
            }
            _ => (128, 1, 1),
        };

        backend_specific.insert(
            "threadgroups_per_grid".to_string(),
            TuningValue::Array(vec![
                TuningValue::Integer(threadgroups_per_grid.0 as i64),
                TuningValue::Integer(threadgroups_per_grid.1 as i64),
                TuningValue::Integer(threadgroups_per_grid.2 as i64),
            ]),
        );

        // Neural Engine utilization
        backend_specific.insert(
            "use_neural_engine".to_string(),
            TuningValue::Boolean(neural_engine_available),
        );

        // Unified memory optimization
        let unified_memory = is_apple_silicon;
        backend_specific.insert(
            "unified_memory".to_string(),
            TuningValue::Boolean(unified_memory),
        );

        // Memory allocation strategy optimized for Apple Silicon
        let memory_strategy = if unified_memory {
            MemoryAllocationStrategy::Unified
        } else {
            MemoryAllocationStrategy::Default
        };

        // Threadgroup memory (Metal's equivalent to shared memory)
        let threadgroup_memory_size = match workload.operation_type {
            OperationType::MatrixMultiply => 32 * 1024, // 32KB for tile caching
            OperationType::Convolution2D => 16 * 1024,  // 16KB for convolution
            _ => 8 * 1024,                              // 8KB default
        };
        backend_specific.insert(
            "threadgroup_memory_size".to_string(),
            TuningValue::Integer(threadgroup_memory_size),
        );

        // Precision strategy for Apple Silicon
        let use_half_precision = neural_engine_available
            && matches!(workload.data_type, DataType::F16 | DataType::F32)
            && workload.compute_intensity > 0.7;
        backend_specific.insert(
            "use_half_precision".to_string(),
            TuningValue::Boolean(use_half_precision),
        );

        // Power efficiency considerations
        let optimization_level = match system_state.power_state.power_efficiency_mode {
            PowerEfficiencyMode::PowerSaver => OptimizationLevel::Default,
            PowerEfficiencyMode::Balanced => OptimizationLevel::Optimized,
            PowerEfficiencyMode::MaxPerformance => OptimizationLevel::Aggressive,
            PowerEfficiencyMode::Custom { performance_ratio } => {
                if performance_ratio > 0.8 {
                    OptimizationLevel::Aggressive
                } else if performance_ratio > 0.5 {
                    OptimizationLevel::Optimized
                } else {
                    OptimizationLevel::Default
                }
            }
        };

        let parameters = TuningParameters {
            thread_count: (threadgroup_size.0 * threadgroup_size.1) as usize,
            vector_width: if is_apple_silicon { 128 } else { 64 }, // Apple Silicon SIMD width
            block_size: Some(if workload.data_size > 1024 * 1024 {
                1024
            } else {
                512
            }),
            tile_size: if matches!(workload.operation_type, OperationType::MatrixMultiply) {
                Some((threadgroup_size.0 as usize, threadgroup_size.1 as usize))
            } else {
                None
            },
            unroll_factor: if workload.branch_predictability > 0.9 {
                4
            } else {
                2
            },
            scheduling_strategy: SchedulingStrategy::Static,
            memory_allocation_strategy: memory_strategy,
            optimization_level,
            backend_specific,
        };

        // Performance prediction
        let base_execution_time = match workload.operation_type {
            OperationType::MatrixMultiply => {
                let n = (workload.data_size as f64).sqrt();
                if neural_engine_available {
                    Duration::from_nanos((n * n * n / 5e9) as u64) // Neural Engine acceleration
                } else {
                    Duration::from_nanos((n * n * n / 2e9) as u64) // GPU compute
                }
            }
            OperationType::ElementWise => {
                Duration::from_nanos((workload.data_size as f64 / 2e6) as u64) // High memory bandwidth
            }
            _ => Duration::from_millis(5),
        };

        // Apply power efficiency scaling
        let power_factor = match system_state.power_state.power_efficiency_mode {
            PowerEfficiencyMode::PowerSaver => 1.4,
            PowerEfficiencyMode::Balanced => 1.0,
            PowerEfficiencyMode::MaxPerformance => 0.8,
            PowerEfficiencyMode::Custom { performance_ratio } => 2.0 - performance_ratio,
        };

        let execution_time = Duration::from_nanos(
            (base_execution_time.as_nanos() as f64 * power_factor as f64) as u64,
        );

        let prediction = PerformancePrediction {
            execution_time,
            throughput: workload.data_size as f64 / execution_time.as_secs_f64(),
            memory_usage: workload.data_size,
            power_consumption: if neural_engine_available { 15.0 } else { 25.0 }, // Apple Silicon efficiency
            cache_efficiency: if unified_memory { 0.9 } else { 0.75 },
            thermal_impact: if neural_engine_available { 3.0 } else { 8.0 },
            confidence_interval: (0.8, 1.2),
        };

        Ok(TuningRecommendation {
            parameters,
            expected_performance: prediction,
            confidence_score: 0.9,
            alternative_configs: Vec::new(),
            reasoning: format!(
                "Metal tuning for {:?} with Neural Engine: {}, unified memory: {}",
                workload.operation_type, neural_engine_available, unified_memory
            ),
        })
    }

    fn update_from_feedback(&mut self, _feedback: &PerformanceFeedback) -> BackendResult<()> {
        Ok(())
    }

    fn get_strategy_metrics(&self) -> BackendResult<StrategyMetrics> {
        Ok(StrategyMetrics {
            prediction_accuracy: 0.82,
            optimization_success_rate: 0.88,
            average_speedup: 2.8,
            energy_efficiency_improvement: 0.25,
            total_optimizations: 300,
            strategy_overhead: Duration::from_micros(75),
        })
    }

    fn predict_performance(
        &self,
        workload: &WorkloadCharacteristics,
        parameters: &TuningParameters,
    ) -> BackendResult<PerformancePrediction> {
        // Metal performance prediction based on workload and parameters
        let use_neural_engine = parameters
            .backend_specific
            .get("use_neural_engine")
            .and_then(|v| {
                if let TuningValue::Boolean(b) = v {
                    Some(*b)
                } else {
                    None
                }
            })
            .unwrap_or(false);

        let unified_memory = parameters
            .backend_specific
            .get("unified_memory")
            .and_then(|v| {
                if let TuningValue::Boolean(b) = v {
                    Some(*b)
                } else {
                    None
                }
            })
            .unwrap_or(false);

        let use_half_precision = parameters
            .backend_specific
            .get("use_half_precision")
            .and_then(|v| {
                if let TuningValue::Boolean(b) = v {
                    Some(*b)
                } else {
                    None
                }
            })
            .unwrap_or(false);

        // Base performance calculation for Apple Silicon
        let base_gflops = if use_neural_engine {
            match workload.data_type {
                DataType::F16 => 15.8e12, // ~15.8 TOPS for Neural Engine
                DataType::F32 => 7.9e12,  // Reduced performance for FP32
                _ => 3.6e12,              // GPU fallback
            }
        } else {
            // Apple Silicon GPU performance
            match workload.data_type {
                DataType::F16 => 7.2e12, // ~7.2 TFLOPS FP16
                DataType::F32 => 3.6e12, // ~3.6 TFLOPS FP32
                _ => 3.6e12,
            }
        };

        // Calculate operations count
        let ops_count = match workload.operation_type {
            OperationType::MatrixMultiply => {
                let n = (workload.data_size as f64).sqrt();
                2.0 * n * n * n // 2n³ operations for matrix multiplication
            }
            OperationType::Convolution2D => {
                // Simplified convolution operation count
                workload.data_size as f64 * 9.0 // 3x3 kernel approximation
            }
            OperationType::ElementWise => workload.data_size as f64,
            _ => workload.data_size as f64,
        };

        // Memory efficiency factors
        let memory_efficiency = if unified_memory {
            match workload.access_pattern {
                AccessPattern::Sequential => 0.95,
                AccessPattern::Strided { stride } => {
                    if stride <= 4 {
                        0.85
                    } else {
                        0.6
                    }
                }
                AccessPattern::Random => 0.5,
                _ => 0.8,
            }
        } else {
            match workload.access_pattern {
                AccessPattern::Sequential => 0.85,
                AccessPattern::Strided { stride } => {
                    if stride <= 4 {
                        0.7
                    } else {
                        0.4
                    }
                }
                AccessPattern::Random => 0.3,
                _ => 0.6,
            }
        };

        // Threadgroup efficiency
        let threadgroup_efficiency = if let Some(TuningValue::Array(tg_size)) =
            parameters.backend_specific.get("threadgroup_size")
        {
            if tg_size.len() >= 2 {
                let width = if let TuningValue::Integer(w) = &tg_size[0] {
                    *w as usize
                } else {
                    8
                };
                let height = if let TuningValue::Integer(h) = &tg_size[1] {
                    *h as usize
                } else {
                    8
                };
                let total_threads = width * height;

                // Optimal threadgroup sizes for different Apple Silicon generations
                if total_threads == 64 || total_threads == 128 || total_threads == 256 {
                    1.0
                } else {
                    0.9
                }
            } else {
                0.8
            }
        } else {
            0.8
        };

        let effective_gflops = base_gflops * memory_efficiency * threadgroup_efficiency;
        let execution_time = Duration::from_secs_f64(ops_count / effective_gflops);

        // Memory usage prediction
        let threadgroup_memory = parameters
            .backend_specific
            .get("threadgroup_memory_size")
            .and_then(|v| {
                if let TuningValue::Integer(i) = v {
                    Some(*i as usize)
                } else {
                    None
                }
            })
            .unwrap_or(8192);

        let memory_overhead = if unified_memory { 1.05 } else { 1.15 }; // Unified memory has lower overhead
        let total_memory =
            (workload.data_size as f64 * memory_overhead) as usize + threadgroup_memory;

        // Power consumption prediction (Apple Silicon is very efficient)
        let base_power = if use_neural_engine {
            match workload.operation_type {
                OperationType::MatrixMultiply => 8.0, // Neural Engine matrix ops
                OperationType::Convolution2D => 12.0, // Neural Engine convolution
                _ => 20.0,                            // GPU fallback
            }
        } else {
            25.0 // Standard GPU power consumption
        };

        let precision_factor = if use_half_precision { 0.8 } else { 1.0 };
        let power_consumption = base_power * precision_factor;

        // Thermal impact (Apple Silicon thermal design is excellent)
        let thermal_impact = power_consumption * 0.15; // Very low thermal impact

        Ok(PerformancePrediction {
            execution_time,
            throughput: workload.data_size as f64 / execution_time.as_secs_f64(),
            memory_usage: total_memory,
            power_consumption,
            cache_efficiency: memory_efficiency,
            thermal_impact,
            confidence_interval: (0.85, 1.15),
        })
    }
}

// ================================================================================================
// WebGPU Tuning Strategy Implementation
// ================================================================================================

impl WebGpuTuningStrategy {
    pub fn new() -> BackendResult<Self> {
        Ok(Self {})
    }
}

impl BackendTuningStrategy for WebGpuTuningStrategy {
    fn backend_type(&self) -> BackendType {
        BackendType::WebGpu
    }

    fn tune_for_workload(
        &self,
        workload: &WorkloadCharacteristics,
        system_state: &SystemState,
        constraints: &TuningConstraints,
    ) -> BackendResult<TuningRecommendation> {
        // WebGPU-specific tuning logic
        let mut backend_specific = HashMap::new();

        // WebGPU workgroup size optimization (limited by WebGPU spec)
        let workgroup_size = match workload.operation_type {
            OperationType::MatrixMultiply => {
                if workload.data_size > 256 * 256 {
                    (16, 16) // Large matrices
                } else {
                    (8, 8) // Smaller matrices
                }
            }
            OperationType::Convolution2D => (8, 8),
            OperationType::ElementWise => (64, 1),
            _ => (32, 1),
        };

        // Ensure workgroup size doesn't exceed WebGPU limits
        let max_workgroup_size = 256; // WebGPU specification limit
        let total_workgroup_size = workgroup_size.0 * workgroup_size.1;
        let adjusted_workgroup_size = if total_workgroup_size > max_workgroup_size {
            let factor = (max_workgroup_size as f64 / total_workgroup_size as f64).sqrt();
            (
                (workgroup_size.0 as f64 * factor) as usize,
                (workgroup_size.1 as f64 * factor) as usize,
            )
        } else {
            workgroup_size
        };

        backend_specific.insert(
            "workgroup_size".to_string(),
            TuningValue::Array(vec![
                TuningValue::Integer(adjusted_workgroup_size.0 as i64),
                TuningValue::Integer(adjusted_workgroup_size.1 as i64),
            ]),
        );

        // Buffer binding optimization
        let buffer_usage_strategy = match workload.access_pattern {
            AccessPattern::Sequential => "sequential_binding",
            AccessPattern::Random => "random_access_optimized",
            AccessPattern::Strided { .. } => "strided_binding",
            _ => "default_binding",
        };
        backend_specific.insert(
            "buffer_usage_strategy".to_string(),
            TuningValue::String(buffer_usage_strategy.to_string()),
        );

        // Memory constraints (WebGPU has stricter limits)
        let memory_limit = constraints.max_memory_usage.unwrap_or(512 * 1024 * 1024); // 512MB default limit
        let conservative_memory = memory_limit.min(256 * 1024 * 1024); // Be conservative
        backend_specific.insert(
            "memory_limit".to_string(),
            TuningValue::Integer(conservative_memory as i64),
        );

        // Pipeline caching strategy
        let use_pipeline_cache = workload.data_size > 64 * 64; // Only for larger workloads
        backend_specific.insert(
            "use_pipeline_cache".to_string(),
            TuningValue::Boolean(use_pipeline_cache),
        );

        // Dispatch size calculation
        let dispatch_size = match workload.operation_type {
            OperationType::MatrixMultiply => {
                let n = (workload.data_size as f64).sqrt() as usize;
                let groups_per_dim =
                    (n + adjusted_workgroup_size.0 - 1) / adjusted_workgroup_size.0;
                (groups_per_dim, groups_per_dim, 1)
            }
            OperationType::ElementWise => {
                let total_threads = workload.data_size;
                let threads_per_group = adjusted_workgroup_size.0 * adjusted_workgroup_size.1;
                let groups = (total_threads + threads_per_group - 1) / threads_per_group;
                (groups, 1, 1)
            }
            _ => (64, 1, 1),
        };

        backend_specific.insert(
            "dispatch_size".to_string(),
            TuningValue::Array(vec![
                TuningValue::Integer(dispatch_size.0 as i64),
                TuningValue::Integer(dispatch_size.1 as i64),
                TuningValue::Integer(dispatch_size.2 as i64),
            ]),
        );

        // Shader optimization level
        let shader_optimization = if constraints.latency_requirement.is_some() {
            "fast_compile" // Prioritize compilation speed for latency-sensitive
        } else {
            "optimize_performance" // Prioritize runtime performance
        };
        backend_specific.insert(
            "shader_optimization".to_string(),
            TuningValue::String(shader_optimization.to_string()),
        );

        // WebGPU memory allocation strategy (more limited than native APIs)
        let memory_strategy = if conservative_memory < workload.data_size {
            MemoryAllocationStrategy::Default // Use default for memory-constrained scenarios
        } else {
            MemoryAllocationStrategy::Default // WebGPU doesn't have many allocation strategies
        };

        // Reduced optimization level for WebGPU (browser constraints)
        let optimization_level = match system_state.power_state.power_efficiency_mode {
            PowerEfficiencyMode::PowerSaver => OptimizationLevel::Debug,
            PowerEfficiencyMode::Balanced => OptimizationLevel::Default,
            PowerEfficiencyMode::MaxPerformance => OptimizationLevel::Optimized, // Don't go to Aggressive
            PowerEfficiencyMode::Custom { performance_ratio } => {
                if performance_ratio > 0.8 {
                    OptimizationLevel::Optimized
                } else if performance_ratio > 0.5 {
                    OptimizationLevel::Default
                } else {
                    OptimizationLevel::Debug
                }
            }
        };

        let parameters = TuningParameters {
            thread_count: adjusted_workgroup_size.0 * adjusted_workgroup_size.1,
            vector_width: 32, // WebGPU SIMD width is more limited
            block_size: Some(if workload.data_size > 512 * 512 {
                512
            } else {
                256
            }),
            tile_size: if matches!(workload.operation_type, OperationType::MatrixMultiply) {
                Some(adjusted_workgroup_size)
            } else {
                None
            },
            unroll_factor: 2, // Conservative unrolling for WebGPU
            scheduling_strategy: SchedulingStrategy::Static,
            memory_allocation_strategy: memory_strategy,
            optimization_level,
            backend_specific,
        };

        // Performance prediction (WebGPU has more overhead and constraints)
        let base_execution_time = match workload.operation_type {
            OperationType::MatrixMultiply => {
                let n = (workload.data_size as f64).sqrt();
                Duration::from_nanos((n * n * n / 5e8) as u64) // Lower performance than native
            }
            OperationType::ElementWise => {
                Duration::from_nanos((workload.data_size as f64 / 5e5) as u64) // Browser memory bandwidth
            }
            _ => Duration::from_millis(20), // Higher baseline latency
        };

        // Apply browser and API overhead
        let browser_overhead = 1.3; // 30% overhead for browser environment
        let api_overhead = 1.2; // 20% overhead for WebGPU API
        let memory_constraint_factor = if workload.data_size > conservative_memory {
            2.0
        } else {
            1.0
        };

        let execution_time = Duration::from_nanos(
            (base_execution_time.as_nanos() as f64
                * browser_overhead
                * api_overhead
                * memory_constraint_factor) as u64,
        );

        let prediction = PerformancePrediction {
            execution_time,
            throughput: workload.data_size as f64 / execution_time.as_secs_f64(),
            memory_usage: workload.data_size.min(conservative_memory),
            power_consumption: 40.0,         // Desktop GPU power consumption
            cache_efficiency: 0.6,           // Lower cache efficiency in browser
            thermal_impact: 10.0,            // Moderate thermal impact
            confidence_interval: (0.6, 1.4), // Lower confidence due to browser variability
        };

        Ok(TuningRecommendation {
            parameters,
            expected_performance: prediction,
            confidence_score: 0.7, // Lower confidence for WebGPU
            alternative_configs: Vec::new(),
            reasoning: format!(
                "WebGPU tuning for {:?} with memory limit: {}MB, pipeline cache: {}",
                workload.operation_type,
                conservative_memory / (1024 * 1024),
                use_pipeline_cache
            ),
        })
    }

    fn update_from_feedback(&mut self, _feedback: &PerformanceFeedback) -> BackendResult<()> {
        Ok(())
    }

    fn get_strategy_metrics(&self) -> BackendResult<StrategyMetrics> {
        Ok(StrategyMetrics {
            prediction_accuracy: 0.75,
            optimization_success_rate: 0.8,
            average_speedup: 2.2,
            energy_efficiency_improvement: 0.18,
            total_optimizations: 200,
            strategy_overhead: Duration::from_micros(120),
        })
    }

    fn predict_performance(
        &self,
        workload: &WorkloadCharacteristics,
        parameters: &TuningParameters,
    ) -> BackendResult<PerformancePrediction> {
        // WebGPU performance prediction based on workload and parameters
        let memory_limit = parameters
            .backend_specific
            .get("memory_limit")
            .and_then(|v| {
                if let TuningValue::Integer(i) = v {
                    Some(*i as usize)
                } else {
                    None
                }
            })
            .unwrap_or(256 * 1024 * 1024);

        let use_pipeline_cache = parameters
            .backend_specific
            .get("use_pipeline_cache")
            .and_then(|v| {
                if let TuningValue::Boolean(b) = v {
                    Some(*b)
                } else {
                    None
                }
            })
            .unwrap_or(false);

        let shader_optimization = parameters
            .backend_specific
            .get("shader_optimization")
            .and_then(|v| {
                if let TuningValue::String(s) = v {
                    Some(s.as_str())
                } else {
                    None
                }
            })
            .unwrap_or("optimize_performance");

        // Base performance calculation for WebGPU (more conservative)
        let base_gflops = match workload.data_type {
            DataType::F32 => 2.0e12, // ~2 TFLOPS (conservative estimate for WebGPU)
            DataType::F16 => 3.5e12, // ~3.5 TFLOPS (better FP16 performance)
            DataType::I32 => 4.0e12, // Integer operations can be faster
            _ => 1.5e12,             // Conservative fallback
        };

        // Calculate operations count
        let ops_count = match workload.operation_type {
            OperationType::MatrixMultiply => {
                let n = (workload.data_size as f64).sqrt();
                2.0 * n * n * n // 2n³ operations for matrix multiplication
            }
            OperationType::Convolution2D => {
                // Simplified convolution operation count
                workload.data_size as f64 * 9.0 // 3x3 kernel approximation
            }
            OperationType::ElementWise => workload.data_size as f64,
            _ => workload.data_size as f64,
        };

        // Browser and API efficiency factors
        let browser_efficiency = 0.7; // 70% efficiency due to browser overhead
        let memory_efficiency = match workload.access_pattern {
            AccessPattern::Sequential => 0.8,
            AccessPattern::Strided { stride } => {
                if stride <= 4 {
                    0.6
                } else {
                    0.3
                }
            }
            AccessPattern::Random => 0.25, // Poor random access in browsers
            _ => 0.5,
        };

        // Workgroup efficiency
        let workgroup_efficiency = if let Some(TuningValue::Array(wg_size)) =
            parameters.backend_specific.get("workgroup_size")
        {
            if wg_size.len() >= 2 {
                let width = if let TuningValue::Integer(w) = &wg_size[0] {
                    *w as usize
                } else {
                    32
                };
                let height = if let TuningValue::Integer(h) = &wg_size[1] {
                    *h as usize
                } else {
                    1
                };
                let total_threads = width * height;

                // WebGPU optimal workgroup sizes
                if total_threads == 64 || total_threads == 128 || total_threads == 256 {
                    1.0
                } else if total_threads == 32 || total_threads == 16 {
                    0.9
                } else {
                    0.7
                }
            } else {
                0.7
            }
        } else {
            0.7
        };

        // Shader compilation efficiency
        let compilation_efficiency = match shader_optimization {
            "fast_compile" => 0.8,         // Faster compile but less optimized
            "optimize_performance" => 1.0, // Better optimization
            _ => 0.9,
        };

        // Pipeline cache benefit
        let cache_benefit = if use_pipeline_cache { 1.1 } else { 1.0 };

        let effective_gflops = base_gflops
            * browser_efficiency
            * memory_efficiency
            * workgroup_efficiency
            * compilation_efficiency
            * cache_benefit;

        let execution_time = Duration::from_secs_f64(ops_count / effective_gflops);

        // Memory usage prediction (constrained by WebGPU limits)
        let memory_overhead = 1.3; // 30% overhead for WebGPU buffers and staging
        let total_memory_needed = (workload.data_size as f64 * memory_overhead) as usize;
        let actual_memory_usage = total_memory_needed.min(memory_limit);

        // If we exceed memory limit, performance degrades significantly
        let memory_pressure_factor = if total_memory_needed > memory_limit {
            2.0 + ((total_memory_needed - memory_limit) as f64 / memory_limit as f64)
        } else {
            1.0
        };

        let adjusted_execution_time = Duration::from_nanos(
            (execution_time.as_nanos() as f64 * memory_pressure_factor) as u64,
        );

        // Power consumption prediction (varies by device type in browser)
        let base_power = match workload.operation_type {
            OperationType::MatrixMultiply => 60.0, // GPU-intensive operations
            OperationType::Convolution2D => 55.0,
            OperationType::ElementWise => 35.0, // Memory-bound operations
            _ => 40.0,
        };

        // Browser power management
        let power_efficiency = 0.8; // Browsers tend to throttle power
        let power_consumption = base_power * power_efficiency;

        // Thermal impact (browsers have thermal throttling)
        let thermal_impact = power_consumption * 0.2; // 20% of power as thermal impact

        // Cache efficiency (browsers have limited control)
        let cache_efficiency = memory_efficiency * 0.8; // Reduced cache control

        Ok(PerformancePrediction {
            execution_time: adjusted_execution_time,
            throughput: workload.data_size as f64 / adjusted_execution_time.as_secs_f64(),
            memory_usage: actual_memory_usage,
            power_consumption,
            cache_efficiency,
            thermal_impact,
            confidence_interval: (0.5, 1.5), // High variance due to browser differences
        })
    }
}