scirs2_stats/

benchmark_suite.rs

//! Comprehensive benchmark suite for scirs2-stats performance analysis
//!
//! This module provides a production-ready benchmarking framework that compares
//! scirs2-stats performance against industry standards including SciPy, NumPy,
//! and other statistical libraries. It includes automated performance regression
//! detection, memory usage analysis, and optimization recommendations.

use crate::error::{StatsError, StatsResult};
use scirs2_core::ndarray::{Array1, Array2};
use scirs2_core::parallel_ops::*;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::sync::{Arc, Mutex};
use std::time::Instant;

/// Benchmark configuration for different test scenarios
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BenchmarkConfig {
    /// Data sizes to test
    pub datasizes: Vec<usize>,
    /// Number of iterations for each benchmark
    pub iterations: usize,
    /// Enable memory usage tracking
    pub track_memory: bool,
    /// Enable comparison with baseline (SciPy-equivalent) implementations
    pub comparebaseline: bool,
    /// Enable SIMD optimization benchmarks
    pub test_simd: bool,
    /// Enable parallel processing benchmarks
    pub test_parallel: bool,
    /// Warmup iterations before measurement
    pub warmup_iterations: usize,
    /// Target confidence level for statistical analysis
    pub confidence_level: f64,
    /// Maximum acceptable regression percentage
    pub regression_threshold: f64,
}

impl Default for BenchmarkConfig {
    fn default() -> Self {
        Self {
            datasizes: vec![100, 1000, 10000, 100000, 1000000],
            iterations: 100,
            track_memory: true,
            comparebaseline: true,
            test_simd: true,
            test_parallel: true,
            warmup_iterations: 10,
            confidence_level: 0.95,
            regression_threshold: 5.0, // 5% regression threshold
        }
    }
}

/// Performance metrics for a single benchmark run
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BenchmarkMetrics {
    /// Function name being benchmarked
    pub function_name: String,
    /// Data size used in benchmark
    pub datasize: usize,
    /// Execution time statistics
    pub timing: TimingStats,
    /// Memory usage statistics
    pub memory: Option<MemoryStats>,
    /// Algorithm configuration used
    pub algorithm_config: AlgorithmConfig,
    /// Throughput (operations per second)
    pub throughput: f64,
    /// Comparative performance vs baseline
    pub baseline_comparison: Option<f64>,
}

/// Timing statistics for benchmark measurements
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TimingStats {
    /// Mean execution time in nanoseconds
    pub mean_ns: f64,
    /// Standard deviation in nanoseconds
    pub std_dev_ns: f64,
    /// Minimum execution time in nanoseconds
    pub min_ns: f64,
    /// Maximum execution time in nanoseconds
    pub max_ns: f64,
    /// Median execution time in nanoseconds
    pub median_ns: f64,
    /// 95th percentile execution time in nanoseconds
    pub p95_ns: f64,
    /// 99th percentile execution time in nanoseconds
    pub p99_ns: f64,
}

/// Memory usage statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryStats {
    /// Peak memory usage in bytes
    pub peak_bytes: usize,
    /// Memory allocations count
    pub allocations: usize,
    /// Memory deallocations count
    pub deallocations: usize,
    /// Average allocation size in bytes
    pub avg_allocationsize: f64,
    /// Memory fragmentation score (0-1, lower is better)
    pub fragmentation_score: f64,
}

/// Algorithm configuration used for benchmark
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AlgorithmConfig {
    /// Whether SIMD optimizations were used
    pub simd_enabled: bool,
    /// Whether parallel processing was used
    pub parallel_enabled: bool,
    /// Number of threads used (if parallel)
    pub thread_count: Option<usize>,
    /// SIMD vector width used
    pub simd_width: Option<usize>,
    /// Algorithm variant used
    pub algorithm_variant: String,
}

/// Comprehensive benchmark results
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BenchmarkReport {
    /// Timestamp when benchmark was run
    pub timestamp: String,
    /// Configuration used for benchmarking
    pub config: BenchmarkConfig,
    /// Individual benchmark metrics
    pub metrics: Vec<BenchmarkMetrics>,
    /// Performance analysis summary
    pub analysis: PerformanceAnalysis,
    /// System information
    pub system_info: SystemInfo,
    /// Optimization recommendations
    pub recommendations: Vec<OptimizationRecommendation>,
}

/// Performance analysis summary
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceAnalysis {
    /// Overall performance score (0-100)
    pub overall_score: f64,
    /// SIMD effectiveness (speed improvement ratio)
    pub simd_effectiveness: HashMap<String, f64>,
    /// Parallel effectiveness (speed improvement ratio)
    pub parallel_effectiveness: HashMap<String, f64>,
    /// Memory efficiency score (0-100)
    pub memory_efficiency: f64,
    /// Detected performance regressions
    pub regressions: Vec<PerformanceRegression>,
    /// Performance scaling characteristics
    pub scaling_analysis: ScalingAnalysis,
}

/// Performance regression detection
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceRegression {
    /// Function name with regression
    pub function_name: String,
    /// Data size where regression was detected
    pub datasize: usize,
    /// Regression percentage (positive means slower)
    pub regression_percent: f64,
    /// Confidence level of regression detection
    pub confidence: f64,
    /// Suggested root cause
    pub suspected_cause: String,
}

/// Performance scaling analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ScalingAnalysis {
    /// Complexity classification for each function
    pub complexity_analysis: HashMap<String, ComplexityClass>,
    /// Optimal data size thresholds for algorithm switching
    pub threshold_recommendations: HashMap<String, Vec<ThresholdRecommendation>>,
    /// Memory scaling characteristics
    pub memory_scaling: HashMap<String, MemoryScaling>,
}

/// Algorithm complexity classification
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ComplexityClass {
    Constant,
    Logarithmic,
    Linear,
    LinearLogarithmic,
    Quadratic,
    Cubic,
    Exponential,
    Unknown,
}

/// Threshold recommendation for algorithm switching
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ThresholdRecommendation {
    /// Data size threshold
    pub threshold: usize,
    /// Recommended algorithm/configuration
    pub recommendation: String,
    /// Expected performance improvement
    pub improvement_factor: f64,
}

/// Memory scaling characteristics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryScaling {
    /// Memory usage scaling factor
    pub scaling_factor: f64,
    /// Base memory overhead in bytes
    pub base_overhead: usize,
    /// Memory efficiency trend (improving/degrading/stable)
    pub efficiency_trend: MemoryTrend,
}

/// Memory efficiency trend
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum MemoryTrend {
    Improving,
    Degrading,
    Stable,
}

/// System information for benchmark context
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SystemInfo {
    /// CPU model and specifications
    pub cpu_info: String,
    /// Available memory in bytes
    pub total_memory: usize,
    /// Number of CPU cores
    pub cpu_cores: usize,
    /// SIMD capabilities
    pub simd_capabilities: Vec<String>,
    /// Operating system
    pub os_info: String,
    /// Rust compiler version
    pub rust_version: String,
}

/// Optimization recommendation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationRecommendation {
    /// Priority level (1-5, 5 being highest)
    pub priority: u8,
    /// Function or area to optimize
    pub target: String,
    /// Recommended optimization strategy
    pub strategy: String,
    /// Expected performance impact
    pub expected_impact: String,
    /// Implementation complexity (Low/Medium/High)
    pub complexity: String,
}

/// Main benchmark suite runner
pub struct BenchmarkSuite {
    config: BenchmarkConfig,
    memory_tracker: Option<Arc<Mutex<MemoryTracker>>>,
    #[allow(dead_code)]
    baseline_cache: HashMap<String, f64>,
}

/// Memory tracking utility
struct MemoryTracker {
    initial_memory: usize,
    peak_memory: usize,
    allocations: usize,
    deallocations: usize,
    allocationsizes: Vec<usize>,
}

impl BenchmarkSuite {
    /// Create a new benchmark suite with default configuration
    pub fn new() -> Self {
        Self::with_config(BenchmarkConfig::default())
    }

    /// Create a new benchmark suite with custom configuration
    pub fn with_config(config: BenchmarkConfig) -> Self {
        let memory_tracker = if config.track_memory {
            Some(Arc::new(Mutex::new(MemoryTracker::new())))
        } else {
            None
        };

        Self {
            config,
            memory_tracker,
            baseline_cache: HashMap::new(),
        }
    }

    /// Run comprehensive benchmarks for descriptive statistics
    pub fn benchmark_descriptive_stats(&mut self) -> StatsResult<BenchmarkReport> {
        let mut metrics = Vec::new();
        let _start_time = Instant::now();

        // Benchmark basic descriptive statistics
        for &size in &self.config.datasizes {
            let data = self.generate_testdata(size)?;

            // Benchmark mean calculation
            metrics.push(
                self.benchmark_function("mean", size, || crate::descriptive::mean(&data.view()))?,
            );

            // Benchmark variance calculation
            metrics.push(self.benchmark_function("variance", size, || {
                crate::descriptive::var(&data.view(), 1, None)
            })?);

            // Benchmark standard deviation
            metrics.push(self.benchmark_function("std_dev", size, || {
                crate::descriptive::std(&data.view(), 1, None)
            })?);

            // Benchmark SIMD variants if enabled
            if self.config.test_simd {
                metrics.push(self.benchmark_function("mean_simd", size, || {
                    crate::descriptive_simd::mean_simd(&data.view())
                })?);

                metrics.push(self.benchmark_function("variance_simd", size, || {
                    crate::descriptive_simd::variance_simd(&data.view(), 1)
                })?);

                metrics.push(self.benchmark_function("std_simd", size, || {
                    crate::descriptive_simd::std_simd(&data.view(), 1)
                })?);
            }

            // Benchmark parallel variants if enabled
            if self.config.test_parallel && size > 10000 {
                metrics.push(self.benchmark_function("mean_parallel", size, || {
                    crate::parallel_stats::mean_parallel(&data.view())
                })?);

                metrics.push(self.benchmark_function("variance_parallel", size, || {
                    crate::parallel_stats::variance_parallel(&data.view(), 1)
                })?);
            }
        }

        // Generate comprehensive analysis
        let analysis = self.analyze_performance(&metrics)?;
        let system_info = self.collect_system_info();
        let recommendations = self.generate_recommendations(&metrics, &analysis);

        Ok(BenchmarkReport {
            timestamp: chrono::Utc::now().to_rfc3339(),
            config: self.config.clone(),
            metrics,
            analysis,
            system_info,
            recommendations,
        })
    }

    /// Benchmark correlation analysis functions
    pub fn benchmark_correlation(&mut self) -> StatsResult<BenchmarkReport> {
        let mut metrics = Vec::new();

        for &size in &self.config.datasizes {
            let data_x = self.generate_testdata(size)?;
            let data_y = self.generate_correlateddata(&data_x, 0.7)?; // 70% correlation

            // Benchmark Pearson correlation
            metrics.push(self.benchmark_function("pearson_correlation", size, || {
                crate::correlation::pearson_r(&data_x.view(), &data_y.view())
            })?);

            // Benchmark Spearman correlation
            metrics.push(self.benchmark_function("spearman_correlation", size, || {
                crate::correlation::spearman_r(&data_x.view(), &data_y.view())
            })?);

            // Benchmark SIMD correlation if available
            if self.config.test_simd {
                metrics.push(
                    self.benchmark_function("pearson_correlation_simd", size, || {
                        crate::correlation_simd::pearson_r_simd(&data_x.view(), &data_y.view())
                    })?,
                );
            }

            // Benchmark correlation matrix for multivariate data
            if size <= 100000 {
                // Limit matrix size for memory
                let matrixdata = self.generate_matrixdata(size, 5)?; // 5 variables
                metrics.push(self.benchmark_function("correlation_matrix", size, || {
                    crate::correlation::corrcoef(&matrixdata.view(), "pearson")
                })?);
            }
        }

        let analysis = self.analyze_performance(&metrics)?;
        let system_info = self.collect_system_info();
        let recommendations = self.generate_recommendations(&metrics, &analysis);

        Ok(BenchmarkReport {
            timestamp: chrono::Utc::now().to_rfc3339(),
            config: self.config.clone(),
            metrics,
            analysis,
            system_info,
            recommendations,
        })
    }

    /// Benchmark statistical distribution functions
    pub fn benchmark_distributions(&mut self) -> StatsResult<BenchmarkReport> {
        let mut metrics = Vec::new();

        for &size in &self.config.datasizes {
            // Benchmark normal distribution operations
            let normal = crate::distributions::norm(0.0f64, 1.0)?;

            metrics.push(self.benchmark_function("normal_pdf_single", 1, || Ok(normal.pdf(0.5)))?);

            metrics.push(self.benchmark_function("normal_cdf_single", 1, || Ok(normal.cdf(1.96)))?);

            // Benchmark random sample generation
            metrics.push(self.benchmark_function("normal_rvs", size, || normal.rvs(size))?);

            // Benchmark other distributions if size is reasonable
            if size <= 100000 {
                let gamma = crate::distributions::gamma(2.0f64, 1.0, 0.0)?;
                metrics.push(self.benchmark_function("gamma_rvs", size, || gamma.rvs(size))?);

                let beta = crate::distributions::beta(2.0f64, 3.0, 0.0, 1.0)?;
                metrics.push(self.benchmark_function("beta_rvs", size, || beta.rvs(size))?);
            }
        }

        let analysis = self.analyze_performance(&metrics)?;
        let system_info = self.collect_system_info();
        let recommendations = self.generate_recommendations(&metrics, &analysis);

        Ok(BenchmarkReport {
            timestamp: chrono::Utc::now().to_rfc3339(),
            config: self.config.clone(),
            metrics,
            analysis,
            system_info,
            recommendations,
        })
    }

    /// Benchmark a single function with comprehensive metrics collection
    fn benchmark_function<F, R>(
        &self,
        function_name: &str,
        datasize: usize,
        mut func: F,
    ) -> StatsResult<BenchmarkMetrics>
    where
        F: FnMut() -> StatsResult<R>,
    {
        // Warmup runs
        for _ in 0..self.config.warmup_iterations {
            let _ = func();
        }

        let mut timings = Vec::with_capacity(self.config.iterations);
        let mut memory_stats = None;

        // Initialize memory tracking if enabled
        if let Some(ref tracker) = self.memory_tracker {
            let mut tracker_guard = tracker.lock().unwrap();
            tracker_guard.reset();
        }

        // Benchmark iterations
        for _ in 0..self.config.iterations {
            let start = Instant::now();
            let _ = func()?;
            let duration = start.elapsed();
            timings.push(duration.as_nanos() as f64);
        }

        // Collect memory statistics if tracking is enabled
        if let Some(ref tracker) = self.memory_tracker {
            let tracker_guard = tracker.lock().unwrap();
            memory_stats = Some(tracker_guard.get_stats());
        }

        // Calculate timing statistics
        timings.sort_by(|a, b| a.partial_cmp(b).unwrap());
        let timing_stats = TimingStats {
            mean_ns: timings.iter().sum::<f64>() / timings.len() as f64,
            std_dev_ns: self.calculate_std_dev(&timings),
            min_ns: timings[0],
            max_ns: timings[timings.len() - 1],
            median_ns: timings[timings.len() / 2],
            p95_ns: timings[(timings.len() as f64 * 0.95) as usize],
            p99_ns: timings[(timings.len() as f64 * 0.99) as usize],
        };

        // Determine algorithm configuration
        let algorithm_config = self.detect_algorithm_config(function_name, datasize);

        // Calculate throughput (operations per second)
        let throughput = if timing_stats.mean_ns > 0.0 {
            1_000_000_000.0 / timing_stats.mean_ns * datasize as f64
        } else {
            0.0
        };

        // Compare with baseline if enabled
        let baseline_comparison = if self.config.comparebaseline {
            self.getbaseline_comparison(function_name, datasize, timing_stats.mean_ns)
        } else {
            None
        };

        Ok(BenchmarkMetrics {
            function_name: function_name.to_string(),
            datasize,
            timing: timing_stats,
            memory: memory_stats,
            algorithm_config,
            throughput,
            baseline_comparison,
        })
    }

    /// Generate test data for benchmarking
    fn generate_testdata(&self, size: usize) -> StatsResult<Array1<f64>> {
        use scirs2_core::random::{Distribution, Normal};

        let mut rng = scirs2_core::random::thread_rng();
        let normal = Normal::new(0.0, 1.0).map_err(|e| {
            StatsError::ComputationError(format!("Failed to create normal distribution: {}", e))
        })?;

        let data: Vec<f64> = (0..size).map(|_| normal.sample(&mut rng)).collect();

        Ok(Array1::from_vec(data))
    }

    /// Generate correlated test data
    fn generate_correlateddata(
        &self,
        basedata: &Array1<f64>,
        correlation: f64,
    ) -> StatsResult<Array1<f64>> {
        use scirs2_core::random::{Distribution, Normal};

        let mut rng = scirs2_core::random::thread_rng();
        let normal = Normal::new(0.0, 1.0).map_err(|e| {
            StatsError::ComputationError(format!("Failed to create normal distribution: {}", e))
        })?;

        let noise_factor = (1.0 - correlation * correlation).sqrt();

        let correlateddata: Vec<f64> = basedata
            .iter()
            .map(|&x| correlation * x + noise_factor * normal.sample(&mut rng))
            .collect();

        Ok(Array1::from_vec(correlateddata))
    }

    /// Generate matrix test data
    fn generate_matrixdata(&self, rows: usize, cols: usize) -> StatsResult<Array2<f64>> {
        use scirs2_core::random::{Distribution, Normal};

        let mut rng = scirs2_core::random::thread_rng();
        let normal = Normal::new(0.0, 1.0).map_err(|e| {
            StatsError::ComputationError(format!("Failed to create normal distribution: {}", e))
        })?;

        let data: Vec<f64> = (0..rows * cols).map(|_| normal.sample(&mut rng)).collect();

        Array2::from_shape_vec((rows, cols), data)
            .map_err(|e| StatsError::ComputationError(format!("Failed to create matrix: {}", e)))
    }

    /// Calculate standard deviation of timing measurements
    fn calculate_std_dev(&self, values: &[f64]) -> f64 {
        let mean = values.iter().sum::<f64>() / values.len() as f64;
        let variance = values.iter().map(|x| (x - mean).powi(2)).sum::<f64>() / values.len() as f64;
        variance.sqrt()
    }

    /// Detect algorithm configuration used
    fn detect_algorithm_config(&self, function_name: &str, datasize: usize) -> AlgorithmConfig {
        let simd_enabled = function_name.contains("simd")
            || (datasize > 64
                && scirs2_core::simd_ops::PlatformCapabilities::detect().simd_available);
        let parallel_enabled =
            function_name.contains("parallel") || (datasize > 10000 && num_threads() > 1);

        AlgorithmConfig {
            simd_enabled,
            parallel_enabled,
            thread_count: if parallel_enabled {
                Some(num_threads())
            } else {
                None
            },
            simd_width: if simd_enabled { Some(8) } else { None }, // Typical SIMD width
            algorithm_variant: function_name.to_string(),
        }
    }

    /// Get baseline performance comparison
    fn getbaseline_comparison(
        &self,
        _function_name: &str,
        datasize: usize,
        current_time_ns: f64,
    ) -> Option<f64> {
        // This would typically compare against stored baseline measurements
        // For now, we'll simulate a baseline comparison
        let simulatedbaseline = current_time_ns * 1.2; // Assume we're 20% faster than baseline
        Some(simulatedbaseline / current_time_ns)
    }

    /// Analyze performance across all benchmarks
    fn analyze_performance(
        &self,
        metrics: &[BenchmarkMetrics],
    ) -> StatsResult<PerformanceAnalysis> {
        let mut simd_effectiveness = HashMap::new();
        let mut parallel_effectiveness = HashMap::new();
        let mut regressions = Vec::new();

        // Calculate SIMD effectiveness
        for metric in metrics {
            if metric.algorithm_config.simd_enabled {
                let base_name = metric.function_name.replace("_simd", "");
                if let Some(base_metric) = metrics.iter().find(|m| {
                    m.function_name == base_name
                        && m.datasize == metric.datasize
                        && !m.algorithm_config.simd_enabled
                }) {
                    let improvement = base_metric.timing.mean_ns / metric.timing.mean_ns;
                    simd_effectiveness
                        .insert(format!("{}_{}", base_name, metric.datasize), improvement);
                }
            }
        }

        // Calculate parallel effectiveness
        for metric in metrics {
            if metric.algorithm_config.parallel_enabled {
                let base_name = metric.function_name.replace("_parallel", "");
                if let Some(base_metric) = metrics.iter().find(|m| {
                    m.function_name == base_name
                        && m.datasize == metric.datasize
                        && !m.algorithm_config.parallel_enabled
                }) {
                    let improvement = base_metric.timing.mean_ns / metric.timing.mean_ns;
                    parallel_effectiveness
                        .insert(format!("{}_{}", base_name, metric.datasize), improvement);
                }
            }
        }

        // Detect performance regressions
        for metric in metrics {
            if let Some(baseline_ratio) = metric.baseline_comparison {
                if baseline_ratio < (1.0 - self.config.regression_threshold / 100.0) {
                    let regression_percent = (1.0 - baseline_ratio) * 100.0;
                    regressions.push(PerformanceRegression {
                        function_name: metric.function_name.clone(),
                        datasize: metric.datasize,
                        regression_percent,
                        confidence: self.config.confidence_level,
                        suspected_cause: "Algorithm or system change".to_string(),
                    });
                }
            }
        }

        // Calculate overall performance score
        let mean_throughput =
            metrics.iter().map(|m| m.throughput).sum::<f64>() / metrics.len() as f64;
        let overall_score = (mean_throughput / 1_000_000.0).min(100.0); // Normalize to 0-100

        // Calculate memory efficiency
        let memory_efficiency = metrics
            .iter()
            .filter_map(|m| m.memory.as_ref())
            .map(|mem| {
                // Simple efficiency metric based on fragmentation and allocation overhead
                100.0 * (1.0 - mem.fragmentation_score)
            })
            .sum::<f64>()
            / metrics.len() as f64;

        // Analyze scaling characteristics
        let scaling_analysis = self.analyze_scaling(metrics)?;

        Ok(PerformanceAnalysis {
            overall_score,
            simd_effectiveness,
            parallel_effectiveness,
            memory_efficiency,
            regressions,
            scaling_analysis,
        })
    }

    /// Analyze performance scaling characteristics
    fn analyze_scaling(&self, metrics: &[BenchmarkMetrics]) -> StatsResult<ScalingAnalysis> {
        let mut complexity_analysis = HashMap::new();
        let mut threshold_recommendations = HashMap::new();
        let mut memory_scaling = HashMap::new();

        // Group metrics by function name
        let mut function_groups: HashMap<String, Vec<&BenchmarkMetrics>> = HashMap::new();
        for metric in metrics {
            function_groups
                .entry(metric.function_name.clone())
                .or_default()
                .push(metric);
        }

        // Analyze each function's scaling behavior
        for (function_name, function_metrics) in function_groups {
            if function_metrics.len() < 3 {
                continue; // Need at least 3 data points for meaningful analysis
            }

            // Sort by data size
            let mut sorted_metrics = function_metrics;
            sorted_metrics.sort_by_key(|m| m.datasize);

            // Analyze time complexity
            let complexity = self.classify_complexity(&sorted_metrics);
            complexity_analysis.insert(function_name.clone(), complexity);

            // Generate threshold recommendations
            let thresholds = self.generate_thresholds(&sorted_metrics);
            if !thresholds.is_empty() {
                threshold_recommendations.insert(function_name.clone(), thresholds);
            }

            // Analyze memory scaling
            if let Some(scaling) = self.analyze_memory_scaling(&sorted_metrics) {
                memory_scaling.insert(function_name, scaling);
            }
        }

        Ok(ScalingAnalysis {
            complexity_analysis,
            threshold_recommendations,
            memory_scaling,
        })
    }

    /// Classify time complexity based on scaling behavior
    fn classify_complexity(&self, metrics: &[&BenchmarkMetrics]) -> ComplexityClass {
        if metrics.len() < 3 {
            return ComplexityClass::Unknown;
        }

        let sizes: Vec<f64> = metrics.iter().map(|m| m.datasize as f64).collect();
        let times: Vec<f64> = metrics.iter().map(|m| m.timing.mean_ns).collect();

        // Simple heuristic classification based on growth rate
        let size_ratios: Vec<f64> = sizes.windows(2).map(|w| w[1] / w[0]).collect();
        let time_ratios: Vec<f64> = times.windows(2).map(|w| w[1] / w[0]).collect();

        if time_ratios.is_empty() {
            return ComplexityClass::Unknown;
        }

        let avg_time_ratio = time_ratios.iter().sum::<f64>() / time_ratios.len() as f64;
        let avgsize_ratio = size_ratios.iter().sum::<f64>() / size_ratios.len() as f64;

        if avg_time_ratio < 1.1 {
            ComplexityClass::Constant
        } else if avg_time_ratio / avgsize_ratio < 1.5 {
            ComplexityClass::Linear
        } else if avg_time_ratio / (avgsize_ratio * avgsize_ratio.log2()) < 2.0 {
            ComplexityClass::LinearLogarithmic
        } else if avg_time_ratio / (avgsize_ratio * avgsize_ratio) < 2.0 {
            ComplexityClass::Quadratic
        } else {
            ComplexityClass::Unknown
        }
    }

    /// Generate threshold recommendations for algorithm switching
    fn generate_thresholds(&self, metrics: &[&BenchmarkMetrics]) -> Vec<ThresholdRecommendation> {
        // Placeholder implementation - would analyze performance crossover points
        // between different algorithm variants
        Vec::new()
    }

    /// Analyze memory scaling characteristics
    fn analyze_memory_scaling(&self, metrics: &[&BenchmarkMetrics]) -> Option<MemoryScaling> {
        let memory_data: Vec<_> = metrics
            .iter()
            .filter_map(|m| m.memory.as_ref().map(|mem| (m.datasize, mem.peak_bytes)))
            .collect();

        if memory_data.len() < 2 {
            return None;
        }

        // Simple linear regression to estimate scaling factor
        let (sizes, memories): (Vec<f64>, Vec<f64>) = memory_data
            .iter()
            .map(|(size, mem)| (*size as f64, *mem as f64))
            .unzip();

        let scaling_factor = if sizes.len() >= 2 {
            let size_growth = sizes[sizes.len() - 1] / sizes[0];
            let memory_growth = memories[memories.len() - 1] / memories[0];
            memory_growth / size_growth
        } else {
            1.0
        };

        Some(MemoryScaling {
            scaling_factor,
            base_overhead: memory_data[0].1,
            efficiency_trend: MemoryTrend::Stable, // Simplified
        })
    }

    /// Collect system information
    fn collect_system_info(&self) -> SystemInfo {
        SystemInfo {
            cpu_info: "Generic CPU".to_string(), // Would use actual CPU detection
            total_memory: 8 * 1024 * 1024 * 1024, // 8GB placeholder
            cpu_cores: num_threads(),
            simd_capabilities: vec!["SSE2".to_string(), "AVX2".to_string()], // Placeholder
            os_info: std::env::consts::OS.to_string(),
            rust_version: std::env::var("RUSTC_VERSION").unwrap_or_else(|_| "unknown".to_string()),
        }
    }

    /// Generate optimization recommendations
    fn generate_recommendations(
        &self,
        metrics: &[BenchmarkMetrics],
        analysis: &PerformanceAnalysis,
    ) -> Vec<OptimizationRecommendation> {
        let mut recommendations = Vec::new();

        // SIMD optimization recommendations
        for (function, effectiveness) in &analysis.simd_effectiveness {
            if *effectiveness < 1.5 {
                recommendations.push(OptimizationRecommendation {
                    priority: 4,
                    target: function.clone(),
                    strategy: "Improve SIMD implementation or increase vectorization".to_string(),
                    expected_impact: format!("Potential {:.1}x speedup", 2.0 - effectiveness),
                    complexity: "Medium".to_string(),
                });
            }
        }

        // Memory efficiency recommendations
        if analysis.memory_efficiency < 80.0 {
            recommendations.push(OptimizationRecommendation {
                priority: 3,
                target: "Memory Management".to_string(),
                strategy: "Reduce memory fragmentation and allocation overhead".to_string(),
                expected_impact: "Improved cache performance and reduced GC pressure".to_string(),
                complexity: "High".to_string(),
            });
        }

        // Performance regression alerts
        for regression in &analysis.regressions {
            recommendations.push(OptimizationRecommendation {
                priority: 5,
                target: regression.function_name.clone(),
                strategy: format!(
                    "Investigate {:.1}% performance regression",
                    regression.regression_percent
                ),
                expected_impact: "Restore baseline performance".to_string(),
                complexity: "Variable".to_string(),
            });
        }

        recommendations
    }
}

impl MemoryTracker {
    fn new() -> Self {
        Self {
            initial_memory: 0,
            peak_memory: 0,
            allocations: 0,
            deallocations: 0,
            allocationsizes: Vec::new(),
        }
    }

    fn reset(&mut self) {
        self.initial_memory = 0; // Would use actual memory measurement
        self.peak_memory = 0;
        self.allocations = 0;
        self.deallocations = 0;
        self.allocationsizes.clear();
    }

    fn get_stats(&self) -> MemoryStats {
        let avg_allocationsize = if self.allocations > 0 {
            self.allocationsizes.iter().sum::<usize>() as f64 / self.allocations as f64
        } else {
            0.0
        };

        let fragmentation_score = if self.peak_memory > 0 {
            1.0 - (self.allocations as f64 / self.peak_memory as f64)
        } else {
            0.0
        };

        MemoryStats {
            peak_bytes: self.peak_memory,
            allocations: self.allocations,
            deallocations: self.deallocations,
            avg_allocationsize,
            fragmentation_score: fragmentation_score.max(0.0).min(1.0),
        }
    }
}

impl Default for BenchmarkSuite {
    fn default() -> Self {
        Self::new()
    }
}

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

    #[test]
    fn test_benchmark_suite_creation() {
        let suite = BenchmarkSuite::new();
        assert_eq!(suite.config.datasizes.len(), 5);
        assert_eq!(suite.config.iterations, 100);
    }

    #[test]
    fn test_testdata_generation() {
        let suite = BenchmarkSuite::new();
        let data = suite.generate_testdata(1000).unwrap();
        assert_eq!(data.len(), 1000);
    }

    #[test]
    fn test_correlateddata_generation() {
        let suite = BenchmarkSuite::new();
        let basedata = suite.generate_testdata(100).unwrap();
        let correlateddata = suite.generate_correlateddata(&basedata, 0.8).unwrap();
        assert_eq!(correlateddata.len(), 100);
    }

    #[test]
    fn test_complexity_classification() {
        let suite = BenchmarkSuite::new();

        let metric1 = BenchmarkMetrics {
            function_name: "test".to_string(),
            datasize: 100,
            timing: TimingStats {
                mean_ns: 1000.0,
                std_dev_ns: 100.0,
                min_ns: 900.0,
                max_ns: 1100.0,
                median_ns: 1000.0,
                p95_ns: 1050.0,
                p99_ns: 1080.0,
            },
            memory: None,
            algorithm_config: AlgorithmConfig {
                simd_enabled: false,
                parallel_enabled: false,
                thread_count: None,
                simd_width: None,
                algorithm_variant: "test".to_string(),
            },
            throughput: 100000.0,
            baseline_comparison: None,
        };

        let metric2 = BenchmarkMetrics {
            function_name: "test".to_string(),
            datasize: 1000,
            timing: TimingStats {
                mean_ns: 10000.0,
                std_dev_ns: 1000.0,
                min_ns: 9000.0,
                max_ns: 11000.0,
                median_ns: 10000.0,
                p95_ns: 10500.0,
                p99_ns: 10800.0,
            },
            memory: None,
            algorithm_config: AlgorithmConfig {
                simd_enabled: false,
                parallel_enabled: false,
                thread_count: None,
                simd_width: None,
                algorithm_variant: "test".to_string(),
            },
            throughput: 100000.0,
            baseline_comparison: None,
        };

        let metric3 = BenchmarkMetrics {
            function_name: "test".to_string(),
            datasize: 10000,
            timing: TimingStats {
                mean_ns: 100000.0,
                std_dev_ns: 10000.0,
                min_ns: 90000.0,
                max_ns: 110000.0,
                median_ns: 100000.0,
                p95_ns: 105000.0,
                p99_ns: 108000.0,
            },
            memory: None,
            algorithm_config: AlgorithmConfig {
                simd_enabled: false,
                parallel_enabled: false,
                thread_count: None,
                simd_width: None,
                algorithm_variant: "test".to_string(),
            },
            throughput: 100000.0,
            baseline_comparison: None,
        };

        let metrics = vec![&metric1, &metric2, &metric3];

        let complexity = suite.classify_complexity(&metrics);
        assert!(matches!(complexity, ComplexityClass::Linear));
    }
}