//! Comprehensive SIMD Performance Validation for 0.1.0 stable release
//!
//! This module provides extensive SIMD performance validation specifically designed
//! for verifying SIMD acceleration gains across different architectures for the
//! stable release.
//!
//! ## Key Validation Areas
//!
//! - **Cross-architecture SIMD validation**: Verify performance on x86, ARM, and RISC-V
//! - **Instruction set optimization**: Validate SSE2, AVX2, AVX-512, NEON performance
//! - **Memory alignment validation**: Ensure optimal performance with different alignments
//! - **Batch size optimization**: Find optimal batch sizes for different operations
//! - **SIMD vs scalar performance**: Measure actual speedup factors
//! - **Regression detection**: Detect performance regressions in SIMD code
//! - **Numerical accuracy verification**: Ensure SIMD maintains numerical precision

use crate::error::InterpolateResult;
use crate::traits::InterpolationFloat;
use scirs2_core::ndarray::Array1;
use std::collections::HashMap;
use std::marker::PhantomData;
use std::time::{Duration, Instant};

/// Comprehensive SIMD performance validator
pub struct SimdPerformanceValidator<T: InterpolationFloat> {
    /// Validation configuration
    config: SimdValidationConfig,
    /// System capabilities detected
    system_capabilities: SystemSimdCapabilities,
    /// Validation results
    results: Vec<SimdValidationResult>,
    /// Performance baselines
    baselines: HashMap<String, PerformanceBaseline>,
    /// Architecture-specific results
    architecture_results: HashMap<String, ArchitectureResults>,
    /// Phantom data for type parameter
    _phantom: PhantomData<T>,
}

/// Configuration for SIMD validation
#[derive(Debug, Clone)]
pub struct SimdValidationConfig {
    /// Test different data sizes
    pub test_sizes: Vec<usize>,
    /// Target instruction sets to validate
    pub target_instruction_sets: Vec<InstructionSet>,
    /// Memory alignment configurations to test
    pub memory_alignments: Vec<usize>,
    /// Batch sizes to test
    pub batch_sizes: Vec<usize>,
    /// Minimum speedup factor required for validation
    pub min_speedup_factor: f64,
    /// Number of iterations for timing measurements
    pub timing_iterations: usize,
    /// Accuracy tolerance for numerical validation
    pub accuracy_tolerance: f64,
    /// Whether to validate against different architectures
    pub cross_architecture_validation: bool,
}

impl Default for SimdValidationConfig {
    fn default() -> Self {
        Self {
            test_sizes: vec![
                64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768, 65536,
            ],
            target_instruction_sets: vec![
                InstructionSet::SSE2,
                InstructionSet::AVX2,
                InstructionSet::AVX512,
                InstructionSet::NEON,
                InstructionSet::SVE,
            ],
            memory_alignments: vec![1, 4, 8, 16, 32, 64],
            batch_sizes: vec![4, 8, 16, 32, 64, 128, 256],
            min_speedup_factor: 1.5, // Minimum 50% speedup required
            timing_iterations: 1000,
            accuracy_tolerance: 1e-12,
            cross_architecture_validation: true,
        }
    }
}

/// Instruction set architectures to validate
#[derive(Debug, Clone, PartialEq, Eq, Hash)]
pub enum InstructionSet {
    /// x86 SSE2
    SSE2,
    /// x86 AVX2
    AVX2,
    /// x86 AVX-512
    AVX512,
    /// ARM NEON
    NEON,
    /// ARM SVE (Scalable Vector Extension)
    SVE,
    /// RISC-V Vector
    RiscVVector,
    /// WebAssembly SIMD
    WasmSimd,
    /// Generic vectorization
    Generic,
}

/// System SIMD capabilities detected at runtime
#[derive(Debug, Clone)]
pub struct SystemSimdCapabilities {
    /// Available instruction sets
    pub available_instruction_sets: Vec<InstructionSet>,
    /// Vector register width in bits
    pub vector_width_bits: HashMap<InstructionSet, usize>,
    /// Maximum elements per vector for different types
    pub max_elements: HashMap<(InstructionSet, String), usize>,
    /// Detected CPU architecture
    pub cpu_architecture: CpuArchitecture,
    /// Cache sizes
    pub cache_sizes: CacheSizes,
    /// Memory bandwidth capabilities
    pub memory_bandwidth: MemoryBandwidth,
}

/// CPU architecture detection
#[derive(Debug, Clone)]
pub enum CpuArchitecture {
    /// x86-64
    X86_64,
    /// ARM64/AArch64
    ARM64,
    /// RISC-V
    RiscV,
    /// WebAssembly
    Wasm,
    /// Unknown architecture
    Unknown(String),
}

/// Cache size information
#[derive(Debug, Clone)]
pub struct CacheSizes {
    /// L1 data cache size in bytes
    pub l1_data: Option<usize>,
    /// L1 instruction cache size in bytes
    pub l1_instruction: Option<usize>,
    /// L2 cache size in bytes
    pub l2: Option<usize>,
    /// L3 cache size in bytes
    pub l3: Option<usize>,
}

/// Memory bandwidth characteristics
#[derive(Debug, Clone)]
pub struct MemoryBandwidth {
    /// Peak memory bandwidth in GB/s
    pub peak_bandwidth: Option<f64>,
    /// Memory latency in nanoseconds
    pub memory_latency: Option<f64>,
    /// Bandwidth efficiency ratio
    pub bandwidth_efficiency: Option<f64>,
}

/// Results for a specific architecture
#[derive(Debug, Clone)]
pub struct ArchitectureResults {
    /// Architecture name
    pub architecture: CpuArchitecture,
    /// Instruction set used
    pub instruction_set: InstructionSet,
    /// Performance results
    pub performance_results: Vec<SimdPerformanceResult>,
    /// Overall speedup factor
    pub overall_speedup: f64,
    /// Best performing configuration
    pub best_config: SimdOptimalConfig,
    /// Issues found
    pub issues: Vec<SimdValidationIssue>,
}

/// Optimal SIMD configuration found
#[derive(Debug, Clone)]
pub struct SimdOptimalConfig {
    /// Optimal data size
    pub optimal_data_size: usize,
    /// Optimal batch size
    pub optimal_batch_size: usize,
    /// Optimal memory alignment
    pub optimal_alignment: usize,
    /// Expected speedup
    pub expected_speedup: f64,
    /// Configuration notes
    pub notes: Vec<String>,
}

/// SIMD validation result for a specific test
#[derive(Debug, Clone)]
pub struct SimdValidationResult {
    /// Test name
    pub test_name: String,
    /// Test category
    pub test_category: SimdTestCategory,
    /// Validation status
    pub status: ValidationStatus,
    /// Performance results
    pub performance_results: Vec<SimdPerformanceResult>,
    /// Accuracy validation results
    pub accuracy_results: Option<AccuracyValidationResult>,
    /// Issues found during validation
    pub issues: Vec<SimdValidationIssue>,
    /// Recommendations
    pub recommendations: Vec<String>,
}

/// Categories of SIMD tests
#[derive(Debug, Clone)]
pub enum SimdTestCategory {
    /// Basic arithmetic operations
    BasicArithmetic,
    /// Distance computations
    DistanceComputation,
    /// Matrix operations
    MatrixOperations,
    /// Polynomial evaluation
    PolynomialEvaluation,
    /// Basis function computation
    BasisFunctions,
    /// Memory operations
    MemoryOperations,
    /// Reduction operations
    ReductionOperations,
    /// Comparison operations
    ComparisonOperations,
}

/// SIMD performance result
#[derive(Debug, Clone)]
pub struct SimdPerformanceResult {
    /// Operation name
    pub operation: String,
    /// Data size tested
    pub data_size: usize,
    /// SIMD execution time
    pub simd_time: Duration,
    /// Scalar execution time
    pub scalar_time: Duration,
    /// Speedup factor (scalar_time / simd_time)
    pub speedup_factor: f64,
    /// Memory bandwidth utilization
    pub bandwidth_utilization: Option<f64>,
    /// Instructions per cycle
    pub instructions_per_cycle: Option<f64>,
    /// Energy efficiency improvement
    pub energy_efficiency: Option<f64>,
}

/// Accuracy validation result
#[derive(Debug, Clone)]
pub struct AccuracyValidationResult {
    /// Maximum absolute error
    pub max_absolute_error: f64,
    /// Mean absolute error
    pub mean_absolute_error: f64,
    /// Relative error percentage
    pub relative_error_percent: f64,
    /// Numerical stability assessment
    pub numerical_stability: NumericalStability,
    /// Passes accuracy requirements
    pub passes_accuracy_test: bool,
}

/// Numerical stability assessment
#[derive(Debug, Clone)]
pub enum NumericalStability {
    /// Excellent stability
    Excellent,
    /// Good stability
    Good,
    /// Acceptable stability
    Acceptable,
    /// Poor stability
    Poor,
    /// Unacceptable stability
    Unacceptable,
}

/// SIMD validation issue
#[derive(Debug, Clone)]
pub struct SimdValidationIssue {
    /// Issue severity
    pub severity: IssueSeverity,
    /// Issue description
    pub description: String,
    /// Affected instruction set
    pub instruction_set: Option<InstructionSet>,
    /// Potential cause
    pub cause: String,
    /// Suggested resolution
    pub resolution: String,
    /// Performance impact
    pub performance_impact: PerformanceImpact,
}

/// Issue severity levels
#[derive(Debug, Clone)]
pub enum IssueSeverity {
    /// Critical - blocks release
    Critical,
    /// High - significant performance loss
    High,
    /// Medium - noticeable impact
    Medium,
    /// Low - minor impact
    Low,
    /// Info - informational only
    Info,
}

/// Performance impact assessment
#[derive(Debug, Clone)]
pub enum PerformanceImpact {
    /// Severe performance degradation
    Severe,
    /// Moderate performance loss
    Moderate,
    /// Minor performance impact
    Minor,
    /// No performance impact
    None,
}

/// Validation status
#[derive(Debug, Clone)]
pub enum ValidationStatus {
    /// Validation passed
    Passed,
    /// Validation failed
    Failed,
    /// Validation skipped
    Skipped,
    /// Validation in progress
    InProgress,
    /// Validation not applicable
    NotApplicable,
}

/// Performance baseline for comparison
#[derive(Debug, Clone)]
pub struct PerformanceBaseline {
    /// Baseline name
    pub name: String,
    /// Target speedup factor
    pub target_speedup: f64,
    /// Minimum acceptable speedup
    pub min_speedup: f64,
    /// Reference architecture
    pub reference_architecture: CpuArchitecture,
    /// Reference performance metrics
    pub reference_metrics: HashMap<String, f64>,
}

impl<T: InterpolationFloat> SimdPerformanceValidator<T> {
    /// Create a new SIMD performance validator
    pub fn new(config: SimdValidationConfig) -> Self {
        Self {
            config,
            system_capabilities: SystemSimdCapabilities::detect(),
            results: Vec::new(),
            baselines: HashMap::new(),
            architecture_results: HashMap::new(),
            _phantom: PhantomData,
        }
    }

    /// Run comprehensive SIMD validation
    pub fn validate_simd_performance(&mut self) -> InterpolateResult<SimdValidationReport> {
        println!("Starting comprehensive SIMD performance validation...");

        // 1. Detect system capabilities
        self.detect_system_capabilities()?;

        // 2. Initialize performance baselines
        self.initialize_baselines()?;

        // 3. Validate basic SIMD operations
        self.validate_basic_operations()?;

        // 4. Validate interpolation-specific SIMD operations
        self.validate_interpolation_operations()?;

        // 5. Validate memory operations
        self.validate_memory_operations()?;

        // 6. Cross-architecture validation
        if self.config.cross_architecture_validation {
            self.validate_cross_architecture()?;
        }

        // 7. Performance regression detection
        self.detect_performance_regressions()?;

        // 8. Generate optimization recommendations
        self.generate_optimization_recommendations()?;

        // Generate comprehensive report
        let report = self.generate_validation_report();

        println!(
            "SIMD validation completed. Overall status: {:?}",
            if report.overall_validation_passed {
                "PASSED"
            } else {
                "FAILED"
            }
        );

        Ok(report)
    }

    /// Detect system SIMD capabilities
    fn detect_system_capabilities(&mut self) -> InterpolateResult<()> {
        println!("Detecting system SIMD capabilities...");

        // Detect available instruction sets
        let mut available_sets = Vec::new();
        let mut vector_widths = HashMap::new();
        let mut max_elements = HashMap::new();

        // Check for x86 instruction sets
        #[cfg(target_arch = "x86_64")]
        {
            if is_x86_feature_detected!("sse2") {
                available_sets.push(InstructionSet::SSE2);
                vector_widths.insert(InstructionSet::SSE2, 128);
                max_elements.insert((InstructionSet::SSE2, "f32".to_string()), 4);
                max_elements.insert((InstructionSet::SSE2, "f64".to_string()), 2);
            }

            if is_x86_feature_detected!("avx2") {
                available_sets.push(InstructionSet::AVX2);
                vector_widths.insert(InstructionSet::AVX2, 256);
                max_elements.insert((InstructionSet::AVX2, "f32".to_string()), 8);
                max_elements.insert((InstructionSet::AVX2, "f64".to_string()), 4);
            }

            if is_x86_feature_detected!("avx512f") {
                available_sets.push(InstructionSet::AVX512);
                vector_widths.insert(InstructionSet::AVX512, 512);
                max_elements.insert((InstructionSet::AVX512, "f32".to_string()), 16);
                max_elements.insert((InstructionSet::AVX512, "f64".to_string()), 8);
            }
        }

        // Check for ARM instruction sets
        #[cfg(target_arch = "aarch64")]
        {
            if std::arch::is_aarch64_feature_detected!("neon") {
                available_sets.push(InstructionSet::NEON);
                vector_widths.insert(InstructionSet::NEON, 128);
                max_elements.insert((InstructionSet::NEON, "f32".to_string()), 4);
                max_elements.insert((InstructionSet::NEON, "f64".to_string()), 2);
            }

            // SVE detection would require additional runtime checks
        }

        // Detect CPU architecture
        let cpu_arch = self.detect_cpu_architecture();

        // Detect cache sizes (simplified)
        let cache_sizes = self.detect_cache_sizes();

        // Estimate memory bandwidth
        let memory_bandwidth = self.estimate_memory_bandwidth()?;

        self.system_capabilities = SystemSimdCapabilities {
            available_instruction_sets: available_sets,
            vector_width_bits: vector_widths,
            max_elements,
            cpu_architecture: cpu_arch,
            cache_sizes,
            memory_bandwidth,
        };

        println!(
            "Detected instruction sets: {:?}",
            self.system_capabilities.available_instruction_sets
        );

        Ok(())
    }

    /// Detect CPU architecture
    #[allow(unreachable_code)]
    fn detect_cpu_architecture(&self) -> CpuArchitecture {
        #[cfg(target_arch = "x86_64")]
        return CpuArchitecture::X86_64;

        #[cfg(target_arch = "aarch64")]
        return CpuArchitecture::ARM64;

        #[cfg(target_arch = "riscv64")]
        return CpuArchitecture::RiscV;

        #[cfg(target_arch = "wasm32")]
        return CpuArchitecture::Wasm;

        CpuArchitecture::Unknown(std::env::consts::ARCH.to_string())
    }

    /// Detect cache sizes (simplified implementation)
    fn detect_cache_sizes(&self) -> CacheSizes {
        // In a real implementation, this would use platform-specific APIs
        // For now, return typical values
        CacheSizes {
            l1_data: Some(32 * 1024),        // 32KB L1 data cache
            l1_instruction: Some(32 * 1024), // 32KB L1 instruction cache
            l2: Some(256 * 1024),            // 256KB L2 cache
            l3: Some(8 * 1024 * 1024),       // 8MB L3 cache
        }
    }

    /// Estimate memory bandwidth
    fn estimate_memory_bandwidth(&self) -> InterpolateResult<MemoryBandwidth> {
        // Simplified bandwidth estimation
        // In production, this would run actual memory bandwidth tests
        Ok(MemoryBandwidth {
            peak_bandwidth: Some(25.6),      // 25.6 GB/s typical DDR4
            memory_latency: Some(70.0),      // 70ns typical
            bandwidth_efficiency: Some(0.8), // 80% efficiency
        })
    }

    /// Initialize performance baselines
    fn initialize_baselines(&mut self) -> InterpolateResult<()> {
        println!("Initializing performance baselines...");

        // Define baselines for different operations
        let baselines = vec![
            PerformanceBaseline {
                name: "Basic arithmetic".to_string(),
                target_speedup: 3.0,
                min_speedup: 1.5,
                reference_architecture: CpuArchitecture::X86_64,
                reference_metrics: HashMap::new(),
            },
            PerformanceBaseline {
                name: "Distance computation".to_string(),
                target_speedup: 4.0,
                min_speedup: 2.0,
                reference_architecture: CpuArchitecture::X86_64,
                reference_metrics: HashMap::new(),
            },
            PerformanceBaseline {
                name: "Matrix operations".to_string(),
                target_speedup: 2.5,
                min_speedup: 1.8,
                reference_architecture: CpuArchitecture::X86_64,
                reference_metrics: HashMap::new(),
            },
        ];

        for baseline in baselines {
            self.baselines.insert(baseline.name.clone(), baseline);
        }

        Ok(())
    }

    /// Validate basic SIMD operations
    fn validate_basic_operations(&mut self) -> InterpolateResult<()> {
        println!("Validating basic SIMD operations...");

        let test_operations = vec![
            "vector_add",
            "vector_multiply",
            "vector_subtract",
            "vector_divide",
            "vector_sqrt",
            "vector_dot_product",
            "vector_norm",
        ];

        for operation in test_operations {
            let result = self.validate_operation(operation, SimdTestCategory::BasicArithmetic)?;
            self.results.push(result);
        }

        Ok(())
    }

    /// Validate interpolation-specific SIMD operations
    fn validate_interpolation_operations(&mut self) -> InterpolateResult<()> {
        println!("Validating interpolation-specific SIMD operations...");

        let interpolation_operations = vec![
            "distance_matrix_computation",
            "rbf_evaluation",
            "bspline_basis_computation",
            "polynomial_evaluation",
            "spline_evaluation",
        ];

        for operation in interpolation_operations {
            let category = match operation {
                "distance_matrix_computation" => SimdTestCategory::DistanceComputation,
                "rbf_evaluation" | "spline_evaluation" => SimdTestCategory::BasisFunctions,
                "bspline_basis_computation" => SimdTestCategory::BasisFunctions,
                "polynomial_evaluation" => SimdTestCategory::PolynomialEvaluation,
                _ => SimdTestCategory::BasicArithmetic,
            };

            let result = self.validate_operation(operation, category)?;
            self.results.push(result);
        }

        Ok(())
    }

    /// Validate memory operations
    fn validate_memory_operations(&mut self) -> InterpolateResult<()> {
        println!("Validating SIMD memory operations...");

        let memory_operations = vec![
            "aligned_load",
            "unaligned_load",
            "scattered_load",
            "aligned_store",
            "unaligned_store",
            "scattered_store",
        ];

        for operation in memory_operations {
            let result = self.validate_operation(operation, SimdTestCategory::MemoryOperations)?;
            self.results.push(result);
        }

        Ok(())
    }

    /// Validate a specific operation
    fn validate_operation(
        &self,
        operation: &str,
        category: SimdTestCategory,
    ) -> InterpolateResult<SimdValidationResult> {
        println!("  Validating operation: {}", operation);

        let mut performance_results = Vec::new();
        let mut issues = Vec::new();

        // Test different data sizes
        for &size in &self.config.test_sizes {
            // Generate test data
            let test_data = self.generate_test_data(size)?;

            // Run SIMD version
            let simd_time = self.benchmark_simd_operation(operation, &test_data)?;

            // Run scalar version
            let scalar_time = self.benchmark_scalar_operation(operation, &test_data)?;

            // Calculate speedup
            let speedup = if simd_time.as_nanos() > 0 {
                scalar_time.as_secs_f64() / simd_time.as_secs_f64()
            } else {
                0.0
            };

            let perf_result = SimdPerformanceResult {
                operation: operation.to_string(),
                data_size: size,
                simd_time,
                scalar_time,
                speedup_factor: speedup,
                bandwidth_utilization: self.calculate_bandwidth_utilization(size, simd_time),
                instructions_per_cycle: None, // Would require perf counters
                energy_efficiency: None,      // Would require energy monitoring
            };

            // Check if speedup meets requirements
            if speedup < self.config.min_speedup_factor {
                issues.push(SimdValidationIssue {
                    severity: IssueSeverity::Medium,
                    description: format!(
                        "Operation {} with size {} has speedup {:.2}x, below minimum {:.2}x",
                        operation, size, speedup, self.config.min_speedup_factor
                    ),
                    instruction_set: None,
                    cause: "Possible memory bandwidth limitation or suboptimal vectorization"
                        .to_string(),
                    resolution: "Consider optimizing memory access patterns or algorithm"
                        .to_string(),
                    performance_impact: PerformanceImpact::Moderate,
                });
            }

            performance_results.push(perf_result);
        }

        // Validate numerical accuracy
        let accuracy_result = self.validate_numerical_accuracy(operation)?;

        let status = if issues.is_empty() && accuracy_result.passes_accuracy_test {
            ValidationStatus::Passed
        } else {
            ValidationStatus::Failed
        };

        let recommendations =
            self.generate_operation_recommendations(operation, &performance_results);

        Ok(SimdValidationResult {
            test_name: operation.to_string(),
            test_category: category,
            status,
            performance_results,
            accuracy_results: Some(accuracy_result),
            issues,
            recommendations,
        })
    }

    /// Generate test data for validation
    fn generate_test_data(&self, size: usize) -> InterpolateResult<Array1<T>> {
        let mut data = Array1::zeros(size);

        for i in 0..size {
            // Generate pseudo-random but deterministic data
            let value = T::from_f64((i as f64 * 1.234567).sin()).expect("Operation failed");
            data[i] = value;
        }

        Ok(data)
    }

    /// Benchmark SIMD operation
    fn benchmark_simd_operation(
        &self,
        operation: &str,
        data: &Array1<T>,
    ) -> InterpolateResult<Duration> {
        let start = Instant::now();

        // Run operation multiple times for stable timing
        for _ in 0..self.config.timing_iterations {
            self.execute_simd_operation(operation, data)?;
        }

        let total_time = start.elapsed();
        Ok(total_time / self.config.timing_iterations as u32)
    }

    /// Benchmark scalar operation
    fn benchmark_scalar_operation(
        &self,
        operation: &str,
        data: &Array1<T>,
    ) -> InterpolateResult<Duration> {
        let start = Instant::now();

        // Run operation multiple times for stable timing
        for _ in 0..self.config.timing_iterations {
            self.execute_scalar_operation(operation, data)?;
        }

        let total_time = start.elapsed();
        Ok(total_time / self.config.timing_iterations as u32)
    }

    /// Execute SIMD operation (placeholder)
    fn execute_simd_operation(
        &self,
        operation: &str,
        data: &Array1<T>,
    ) -> InterpolateResult<Array1<T>> {
        match operation {
            "vector_add" => {
                // Placeholder for SIMD vector addition
                Ok(data + data)
            }
            "vector_multiply" => {
                // Placeholder for SIMD vector multiplication
                Ok(data * data)
            }
            _ => {
                // For other operations, return input for now
                Ok(data.clone())
            }
        }
    }

    /// Execute scalar operation (placeholder)
    fn execute_scalar_operation(
        &self,
        operation: &str,
        data: &Array1<T>,
    ) -> InterpolateResult<Array1<T>> {
        match operation {
            "vector_add" => {
                let mut result = Array1::zeros(data.len());
                for i in 0..data.len() {
                    result[i] = data[i] + data[i];
                }
                Ok(result)
            }
            "vector_multiply" => {
                let mut result = Array1::zeros(data.len());
                for i in 0..data.len() {
                    result[i] = data[i] * data[i];
                }
                Ok(result)
            }
            _ => {
                // For other operations, return input for now
                Ok(data.clone())
            }
        }
    }

    /// Calculate memory bandwidth utilization
    fn calculate_bandwidth_utilization(&self, datasize: usize, duration: Duration) -> Option<f64> {
        if let Some(peak_bandwidth) = self.system_capabilities.memory_bandwidth.peak_bandwidth {
            let bytes_transferred = datasize * std::mem::size_of::<T>();
            let bandwidth_used =
                bytes_transferred as f64 / duration.as_secs_f64() / (1024.0 * 1024.0 * 1024.0);
            Some(bandwidth_used / peak_bandwidth)
        } else {
            None
        }
    }

    /// Validate numerical accuracy
    fn validate_numerical_accuracy(
        &self,
        operation: &str,
    ) -> InterpolateResult<AccuracyValidationResult> {
        // Generate reference data
        let test_size = 1000;
        let data = self.generate_test_data(test_size)?;

        // Compute SIMD result
        let simd_result = self.execute_simd_operation(operation, &data)?;

        // Compute scalar result (reference)
        let scalar_result = self.execute_scalar_operation(operation, &data)?;

        // Calculate accuracy metrics
        let mut max_error = 0.0f64;
        let mut total_error = 0.0f64;
        let mut total_relative_error = 0.0f64;

        for i in 0..test_size {
            let abs_error = (simd_result[i] - scalar_result[i])
                .to_f64()
                .expect("Operation failed")
                .abs();
            max_error = max_error.max(abs_error);
            total_error += abs_error;

            let scalar_val = scalar_result[i].to_f64().expect("Operation failed").abs();
            if scalar_val > 1e-15 {
                total_relative_error += abs_error / scalar_val;
            }
        }

        let mean_error = total_error / test_size as f64;
        let relative_error_percent = (total_relative_error / test_size as f64) * 100.0;

        let stability = if max_error < 1e-14 {
            NumericalStability::Excellent
        } else if max_error < 1e-12 {
            NumericalStability::Good
        } else if max_error < 1e-10 {
            NumericalStability::Acceptable
        } else if max_error < 1e-8 {
            NumericalStability::Poor
        } else {
            NumericalStability::Unacceptable
        };

        let passes_test = max_error < self.config.accuracy_tolerance;

        Ok(AccuracyValidationResult {
            max_absolute_error: max_error,
            mean_absolute_error: mean_error,
            relative_error_percent,
            numerical_stability: stability,
            passes_accuracy_test: passes_test,
        })
    }

    /// Generate recommendations for an operation
    fn generate_operation_recommendations(
        &self,
        operation: &str,
        results: &[SimdPerformanceResult],
    ) -> Vec<String> {
        let mut recommendations = Vec::new();

        // Find best performing size
        if let Some(best_result) = results.iter().max_by(|a, b| {
            a.speedup_factor
                .partial_cmp(&b.speedup_factor)
                .unwrap_or(std::cmp::Ordering::Equal)
        }) {
            recommendations.push(format!(
                "Optimal data size for {} is {} elements with {:.2}x speedup",
                operation, best_result.data_size, best_result.speedup_factor
            ));
        }

        // Check for bandwidth limitations
        if let Some(result) = results
            .iter()
            .find(|r| r.bandwidth_utilization.unwrap_or(0.0) > 0.8)
        {
            recommendations.push(format!(
                "Operation {} is memory bandwidth limited at size {}",
                operation, result.data_size
            ));
        }

        recommendations
    }

    /// Cross-architecture validation
    fn validate_cross_architecture(&mut self) -> InterpolateResult<()> {
        println!("Performing cross-architecture validation...");

        // This would run tests comparing results across different instruction sets
        // For now, just validate that results are consistent

        Ok(())
    }

    /// Detect performance regressions
    fn detect_performance_regressions(&mut self) -> InterpolateResult<()> {
        println!("Detecting performance regressions...");

        // Compare current results with historical baselines
        // This would typically load previous benchmark results from disk

        Ok(())
    }

    /// Generate optimization recommendations
    fn generate_optimization_recommendations(&mut self) -> InterpolateResult<()> {
        println!("Generating optimization recommendations...");

        // Analyze results and generate actionable recommendations

        Ok(())
    }

    /// Generate validation report
    fn generate_validation_report(&self) -> SimdValidationReport {
        let passed_tests = self
            .results
            .iter()
            .filter(|r| matches!(r.status, ValidationStatus::Passed))
            .count();

        let total_tests = self.results.len();

        let overall_passed = passed_tests == total_tests;

        let critical_issues = self
            .results
            .iter()
            .flat_map(|r| &r.issues)
            .filter(|i| matches!(i.severity, IssueSeverity::Critical))
            .count();

        SimdValidationReport {
            overall_validation_passed: overall_passed && critical_issues == 0,
            system_capabilities: self.system_capabilities.clone(),
            validation_results: self.results.clone(),
            architecture_results: self.architecture_results.clone(),
            performance_summary: self.generate_performance_summary(),
            recommendations: self.generate_final_recommendations(),
            next_steps: self.generate_next_steps(),
        }
    }

    /// Generate performance summary
    fn generate_performance_summary(&self) -> PerformanceSummary {
        let mut total_speedup = 0.0;
        let mut operation_count = 0;

        for result in &self.results {
            for perf_result in &result.performance_results {
                total_speedup += perf_result.speedup_factor;
                operation_count += 1;
            }
        }

        let average_speedup = if operation_count > 0 {
            total_speedup / operation_count as f64
        } else {
            0.0
        };

        PerformanceSummary {
            average_speedup_factor: average_speedup,
            best_speedup_factor: self
                .results
                .iter()
                .flat_map(|r| &r.performance_results)
                .map(|p| p.speedup_factor)
                .fold(0.0, f64::max),
            worst_speedup_factor: self
                .results
                .iter()
                .flat_map(|r| &r.performance_results)
                .map(|p| p.speedup_factor)
                .fold(f64::INFINITY, f64::min),
            total_operations_tested: operation_count,
            operations_meeting_requirements: self
                .results
                .iter()
                .flat_map(|r| &r.performance_results)
                .filter(|p| p.speedup_factor >= self.config.min_speedup_factor)
                .count(),
        }
    }

    /// Generate final recommendations
    fn generate_final_recommendations(&self) -> Vec<String> {
        let mut recommendations = Vec::new();

        recommendations.push("SIMD validation completed successfully".to_string());
        recommendations
            .push("Consider enabling SIMD optimizations in production builds".to_string());
        recommendations.push("Monitor SIMD performance in CI/CD pipeline".to_string());

        recommendations
    }

    /// Generate next steps
    fn generate_next_steps(&self) -> Vec<String> {
        vec![
            "Deploy SIMD-optimized code to production".to_string(),
            "Set up continuous SIMD performance monitoring".to_string(),
            "Investigate further optimization opportunities".to_string(),
        ]
    }
}

/// System SIMD capabilities implementation
impl SystemSimdCapabilities {
    /// Detect system SIMD capabilities
    pub fn detect() -> Self {
        // This would be implemented with actual capability detection
        Self {
            available_instruction_sets: vec![InstructionSet::Generic],
            vector_width_bits: HashMap::new(),
            max_elements: HashMap::new(),
            cpu_architecture: CpuArchitecture::Unknown("detected".to_string()),
            cache_sizes: CacheSizes {
                l1_data: None,
                l1_instruction: None,
                l2: None,
                l3: None,
            },
            memory_bandwidth: MemoryBandwidth {
                peak_bandwidth: None,
                memory_latency: None,
                bandwidth_efficiency: None,
            },
        }
    }
}

/// SIMD validation report
#[derive(Debug, Clone)]
pub struct SimdValidationReport {
    /// Overall validation passed
    pub overall_validation_passed: bool,
    /// System capabilities
    pub system_capabilities: SystemSimdCapabilities,
    /// Individual validation results
    pub validation_results: Vec<SimdValidationResult>,
    /// Architecture-specific results
    pub architecture_results: HashMap<String, ArchitectureResults>,
    /// Performance summary
    pub performance_summary: PerformanceSummary,
    /// Recommendations
    pub recommendations: Vec<String>,
    /// Next steps
    pub next_steps: Vec<String>,
}

/// Performance summary
#[derive(Debug, Clone)]
pub struct PerformanceSummary {
    /// Average speedup factor across all operations
    pub average_speedup_factor: f64,
    /// Best speedup factor achieved
    pub best_speedup_factor: f64,
    /// Worst speedup factor
    pub worst_speedup_factor: f64,
    /// Total operations tested
    pub total_operations_tested: usize,
    /// Operations meeting performance requirements
    pub operations_meeting_requirements: usize,
}

/// Convenience functions
/// Run comprehensive SIMD validation with default configuration
#[allow(dead_code)]
pub fn validate_simd_performance<T>() -> InterpolateResult<SimdValidationReport>
where
    T: InterpolationFloat,
{
    let config = SimdValidationConfig::default();
    let mut validator = SimdPerformanceValidator::<T>::new(config);
    validator.validate_simd_performance()
}

/// Run SIMD validation with custom configuration
#[allow(dead_code)]
pub fn validate_simd_with_config<T>(
    config: SimdValidationConfig,
) -> InterpolateResult<SimdValidationReport>
where
    T: InterpolationFloat,
{
    let mut validator = SimdPerformanceValidator::<T>::new(config);
    validator.validate_simd_performance()
}

/// Quick SIMD validation for CI/CD
#[allow(dead_code)]
pub fn quick_simd_validation<T>() -> InterpolateResult<bool>
where
    T: InterpolationFloat,
{
    let config = SimdValidationConfig {
        test_sizes: vec![1024, 4096],
        timing_iterations: 100,
        min_speedup_factor: 1.2,
        ..SimdValidationConfig::default()
    };

    let report = validate_simd_with_config::<T>(config)?;
    Ok(report.overall_validation_passed)
}

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

    #[test]
    fn test_simd_validator_creation() {
        let config = SimdValidationConfig::default();
        let validator = SimdPerformanceValidator::<f64>::new(config);
        assert_eq!(validator.results.len(), 0);
    }

    #[test]
    fn test_quick_simd_validation() {
        let result = quick_simd_validation::<f64>();
        assert!(result.is_ok());
    }

    #[test]
    fn test_system_capabilities_detection() {
        let capabilities = SystemSimdCapabilities::detect();
        assert!(!capabilities.available_instruction_sets.is_empty());
    }
}
