scirs2-linalg 0.4.2

//! MPI Performance Profiling and Optimization
//!
//! This module provides comprehensive performance profiling, monitoring, and optimization
//! capabilities for MPI operations including trace collection, performance analysis,
//! and adaptive parameter tuning.

use super::MPIConfig;
use std::collections::HashMap;

/// Performance optimizer for MPI operations
#[derive(Debug)]
pub struct MPIPerformanceOptimizer {
    config: MPIConfig,
    benchmark_results: HashMap<String, BenchmarkResult>,
    adaptive_parameters: AdaptiveParameters,
    profiler: MPIProfiler,
}

/// Benchmark result for MPI operations
#[derive(Debug, Clone)]
pub struct BenchmarkResult {
    operation: String,
    datasize: usize,
    process_count: i32,
    bandwidth: f64,
    latency: f64,
    efficiency: f64,
    optimal_parameters: HashMap<String, f64>,
}

/// Adaptive parameters for MPI optimization
#[derive(Debug, Clone)]
pub struct AdaptiveParameters {
    eager_threshold: usize,
    pipeline_chunksize: usize,
    collective_algorithm_map: HashMap<String, String>,
    message_aggregation_threshold: usize,
}

/// MPI profiler for performance analysis
#[derive(Debug)]
pub struct MPIProfiler {
    trace_buffer: Vec<MPITraceEvent>,
    timeline: MPITimeline,
    statistics: MPIProfilingStats,
    active_measurements: HashMap<String, MPIMeasurement>,
}

/// MPI trace event
#[derive(Debug, Clone)]
pub struct MPITraceEvent {
    timestamp: std::time::Instant,
    event_type: MPIEventType,
    process_rank: i32,
    communicator: String,
    datasize: usize,
    partner_rank: Option<i32>,
    operation_id: String,
}

/// Types of MPI events
#[derive(Debug, Clone, Copy)]
pub enum MPIEventType {
    SendStart,
    SendComplete,
    RecvStart,
    RecvComplete,
    CollectiveStart,
    CollectiveComplete,
    BarrierStart,
    BarrierComplete,
    WaitStart,
    WaitComplete,
}

/// MPI timeline for visualization
#[derive(Debug)]
pub struct MPITimeline {
    events: Vec<MPITraceEvent>,
    critical_path: Vec<String>,
    load_balance_analysis: LoadBalanceAnalysis,
}

/// Load balance analysis
#[derive(Debug, Clone)]
pub struct LoadBalanceAnalysis {
    imbalance_factor: f64,
    bottleneck_processes: Vec<i32>,
    idle_time_per_process: HashMap<i32, f64>,
    communication_volume_per_process: HashMap<i32, usize>,
}

/// MPI profiling statistics
#[derive(Debug, Default)]
pub struct MPIProfilingStats {
    total_communication_time: f64,
    total_computation_time: f64,
    communication_efficiency: f64,
    load_balance_efficiency: f64,
    network_utilization: f64,
}

/// Active measurement for profiling
#[derive(Debug)]
pub struct MPIMeasurement {
    measurement_id: String,
    start_time: std::time::Instant,
    operation_type: String,
    expected_duration: Option<f64>,
}

/// Performance analysis report
#[derive(Debug)]
pub struct PerformanceReport {
    pub summary: PerformanceSummary,
    pub bottlenecks: Vec<PerformanceBottleneck>,
    pub recommendations: Vec<OptimizationRecommendation>,
    pub detailed_metrics: DetailedMetrics,
}

/// Summary of performance metrics
#[derive(Debug)]
pub struct PerformanceSummary {
    pub total_execution_time: f64,
    pub communication_time: f64,
    pub computation_time: f64,
    pub efficiency_score: f64,
    pub scalability_factor: f64,
}

/// Performance bottleneck identification
#[derive(Debug)]
pub struct PerformanceBottleneck {
    pub bottleneck_type: BottleneckType,
    pub severity: f64,
    pub affected_processes: Vec<i32>,
    pub description: String,
    pub suggested_fixes: Vec<String>,
}

/// Types of performance bottlenecks
#[derive(Debug, Clone, Copy)]
pub enum BottleneckType {
    CommunicationLatency,
    BandwidthUtilization,
    LoadImbalance,
    Synchronization,
    Memory,
    Computation,
}

/// Optimization recommendation
#[derive(Debug)]
pub struct OptimizationRecommendation {
    pub recommendation_type: RecommendationType,
    pub priority: Priority,
    pub expected_improvement: f64,
    pub implementation_effort: ImplementationEffort,
    pub description: String,
}

/// Types of optimization recommendations
#[derive(Debug, Clone, Copy)]
pub enum RecommendationType {
    AlgorithmChange,
    ParameterTuning,
    TopologyOptimization,
    LoadBalancing,
    CommunicationPattern,
    MemoryOptimization,
}

/// Priority levels for recommendations
#[derive(Debug, Clone, Copy)]
pub enum Priority {
    Low,
    Medium,
    High,
    Critical,
}

/// Implementation effort estimation
#[derive(Debug, Clone, Copy)]
pub enum ImplementationEffort {
    Minimal,
    Low,
    Medium,
    High,
    Extensive,
}

/// Detailed performance metrics
#[derive(Debug)]
pub struct DetailedMetrics {
    pub per_process_stats: HashMap<i32, ProcessStats>,
    pub communication_matrix: HashMap<(i32, i32), CommunicationStats>,
    pub operation_breakdown: HashMap<String, OperationStats>,
    pub timeline_analysis: TimelineAnalysis,
}

/// Statistics for individual processes
#[derive(Debug)]
pub struct ProcessStats {
    pub rank: i32,
    pub cpu_utilization: f64,
    pub memory_usage: f64,
    pub communication_time: f64,
    pub computation_time: f64,
    pub idle_time: f64,
    pub message_count: usize,
    pub bytes_transferred: usize,
}

/// Communication statistics between process pairs
#[derive(Debug)]
pub struct CommunicationStats {
    pub message_count: usize,
    pub total_bytes: usize,
    pub average_latency: f64,
    pub bandwidth_utilization: f64,
    pub contention_events: usize,
}

/// Statistics for specific operations
#[derive(Debug)]
pub struct OperationStats {
    pub operation_name: String,
    pub call_count: usize,
    pub total_time: f64,
    pub average_time: f64,
    pub min_time: f64,
    pub max_time: f64,
    pub variance: f64,
}

/// Timeline analysis results
#[derive(Debug)]
pub struct TimelineAnalysis {
    pub critical_path_length: f64,
    pub parallel_efficiency: f64,
    pub load_balance_factor: f64,
    pub synchronization_overhead: f64,
    pub communication_overlap: f64,
}

impl MPIPerformanceOptimizer {
    /// Create a new performance optimizer
    pub fn new(config: MPIConfig) -> Self {
        Self {
            config,
            benchmark_results: HashMap::new(),
            adaptive_parameters: AdaptiveParameters::new(),
            profiler: MPIProfiler::new(),
        }
    }

    /// Run performance benchmarks
    pub fn run_benchmarks(&mut self) -> Result<Vec<BenchmarkResult>, String> {
        // Implementation would run various MPI operation benchmarks
        Ok(Vec::new())
    }

    /// Optimize parameters based on workload
    pub fn optimize_parameters(&mut self, workload_profile: &WorkloadProfile) -> AdaptiveParameters {
        // Implementation would analyze workload and optimize parameters
        self.adaptive_parameters.clone()
    }

    /// Get current benchmark results
    pub fn get_benchmark_results(&self) -> &HashMap<String, BenchmarkResult> {
        &self.benchmark_results
    }

    /// Get adaptive parameters
    pub fn get_adaptive_parameters(&self) -> &AdaptiveParameters {
        &self.adaptive_parameters
    }

    /// Get profiler
    pub fn get_profiler(&self) -> &MPIProfiler {
        &self.profiler
    }

    /// Get mutable profiler
    pub fn get_profiler_mut(&mut self) -> &mut MPIProfiler {
        &mut self.profiler
    }

    /// Generate performance report
    pub fn generate_report(&self) -> PerformanceReport {
        let summary = PerformanceSummary {
            total_execution_time: 0.0,
            communication_time: self.profiler.statistics.total_communication_time,
            computation_time: self.profiler.statistics.total_computation_time,
            efficiency_score: self.profiler.statistics.communication_efficiency,
            scalability_factor: 1.0,
        };

        PerformanceReport {
            summary,
            bottlenecks: Vec::new(),
            recommendations: Vec::new(),
            detailed_metrics: DetailedMetrics {
                per_process_stats: HashMap::new(),
                communication_matrix: HashMap::new(),
                operation_breakdown: HashMap::new(),
                timeline_analysis: TimelineAnalysis {
                    critical_path_length: 0.0,
                    parallel_efficiency: 0.0,
                    load_balance_factor: 0.0,
                    synchronization_overhead: 0.0,
                    communication_overlap: 0.0,
                },
            },
        }
    }
}

impl MPIProfiler {
    /// Create a new profiler
    pub fn new() -> Self {
        Self {
            trace_buffer: Vec::new(),
            timeline: MPITimeline::new(),
            statistics: MPIProfilingStats::default(),
            active_measurements: HashMap::new(),
        }
    }

    /// Start measuring an operation
    pub fn start_measurement(&mut self, operation_id: String, operation_type: String) {
        let measurement = MPIMeasurement {
            measurement_id: operation_id.clone(),
            start_time: std::time::Instant::now(),
            operation_type,
            expected_duration: None,
        };
        self.active_measurements.insert(operation_id, measurement);
    }

    /// End measurement of an operation
    pub fn end_measurement(&mut self, operation_id: &str) -> Option<f64> {
        if let Some(measurement) = self.active_measurements.remove(operation_id) {
            let duration = measurement.start_time.elapsed().as_secs_f64();
            self.statistics.total_communication_time += duration;
            Some(duration)
        } else {
            None
        }
    }

    /// Record a trace event
    pub fn record_event(&mut self, event: MPITraceEvent) {
        self.trace_buffer.push(event.clone());
        self.timeline.add_event(event);
    }

    /// Get trace buffer
    pub fn get_trace_buffer(&self) -> &[MPITraceEvent] {
        &self.trace_buffer
    }

    /// Get timeline
    pub fn get_timeline(&self) -> &MPITimeline {
        &self.timeline
    }

    /// Get statistics
    pub fn get_statistics(&self) -> &MPIProfilingStats {
        &self.statistics
    }

    /// Clear all profiling data
    pub fn clear(&mut self) {
        self.trace_buffer.clear();
        self.timeline.clear();
        self.statistics = MPIProfilingStats::default();
        self.active_measurements.clear();
    }

    /// Analyze performance
    pub fn analyze_performance(&mut self) -> LoadBalanceAnalysis {
        // Implementation would analyze trace data for load balance
        LoadBalanceAnalysis {
            imbalance_factor: 0.0,
            bottleneck_processes: Vec::new(),
            idle_time_per_process: HashMap::new(),
            communication_volume_per_process: HashMap::new(),
        }
    }
}

impl MPITimeline {
    /// Create a new timeline
    pub fn new() -> Self {
        Self {
            events: Vec::new(),
            critical_path: Vec::new(),
            load_balance_analysis: LoadBalanceAnalysis {
                imbalance_factor: 0.0,
                bottleneck_processes: Vec::new(),
                idle_time_per_process: HashMap::new(),
                communication_volume_per_process: HashMap::new(),
            },
        }
    }

    /// Add an event to the timeline
    pub fn add_event(&mut self, event: MPITraceEvent) {
        self.events.push(event);
    }

    /// Get events
    pub fn get_events(&self) -> &[MPITraceEvent] {
        &self.events
    }

    /// Clear timeline
    pub fn clear(&mut self) {
        self.events.clear();
        self.critical_path.clear();
    }

    /// Analyze critical path
    pub fn analyze_critical_path(&mut self) {
        // Implementation would analyze events to find critical path
        self.critical_path.clear();
    }
}

impl AdaptiveParameters {
    /// Create new adaptive parameters
    pub fn new() -> Self {
        Self {
            eager_threshold: 12 * 1024, // 12KB
            pipeline_chunksize: 64 * 1024, // 64KB
            collective_algorithm_map: HashMap::new(),
            message_aggregation_threshold: 1024, // 1KB
        }
    }

    /// Get eager threshold
    pub fn eager_threshold(&self) -> usize {
        self.eager_threshold
    }

    /// Set eager threshold
    pub fn set_eager_threshold(&mut self, threshold: usize) {
        self.eager_threshold = threshold;
    }

    /// Get pipeline chunk size
    pub fn pipeline_chunksize(&self) -> usize {
        self.pipeline_chunksize
    }

    /// Set pipeline chunk size
    pub fn set_pipeline_chunksize(&mut self, size: usize) {
        self.pipeline_chunksize = size;
    }

    /// Get collective algorithm for operation
    pub fn get_collective_algorithm(&self, operation: &str) -> Option<&String> {
        self.collective_algorithm_map.get(operation)
    }

    /// Set collective algorithm for operation
    pub fn set_collective_algorithm(&mut self, operation: String, algorithm: String) {
        self.collective_algorithm_map.insert(operation, algorithm);
    }
}

impl MPITraceEvent {
    /// Create a new trace event
    pub fn new(
        event_type: MPIEventType,
        process_rank: i32,
        communicator: String,
        datasize: usize,
        operation_id: String,
    ) -> Self {
        Self {
            timestamp: std::time::Instant::now(),
            event_type,
            process_rank,
            communicator,
            datasize,
            partner_rank: None,
            operation_id,
        }
    }

    /// Set partner rank
    pub fn with_partner_rank(mut self, partner_rank: i32) -> Self {
        self.partner_rank = Some(partner_rank);
        self
    }

    /// Get timestamp
    pub fn timestamp(&self) -> std::time::Instant {
        self.timestamp
    }

    /// Get event type
    pub fn event_type(&self) -> MPIEventType {
        self.event_type
    }

    /// Get process rank
    pub fn process_rank(&self) -> i32 {
        self.process_rank
    }

    /// Get data size
    pub fn datasize(&self) -> usize {
        self.datasize
    }

    /// Get operation ID
    pub fn operation_id(&self) -> &str {
        &self.operation_id
    }
}

/// Workload profile for optimization
#[derive(Debug)]
pub struct WorkloadProfile {
    pub communication_patterns: Vec<String>,
    pub data_sizes: Vec<usize>,
    pub process_counts: Vec<i32>,
    pub operation_frequencies: HashMap<String, f64>,
}

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

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

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