scirs2-linalg 0.4.0

//! Parallel processing utilities for linear algebra operations
//!
//! This module provides utilities for managing worker threads across various
//! linear algebra operations, ensuring consistent behavior and optimal performance.

use std::sync::Mutex;

// Submodules for advanced parallel processing
pub mod thread_pools;
pub mod work_stealing;

// Extracted module declarations
pub mod adaptive;
pub mod advanced_work_stealing;
pub mod affinity;
pub mod algorithms;
pub mod iter;
pub mod numa;
pub mod scheduler;
pub mod thread_pool;

// Re-export submodule types
pub use thread_pools::{
    get_global_manager, AdvancedPerformanceStats, AdvancedPerformanceThreadPool,
    AdvancedThreadPoolConfig, AffinityStrategy, AnomalySeverity, AnomalyType,
    CacheAllocationPolicy, DecompositionType, DynamicSizingConfig, DynamicThreadManager,
    IterativeSolverType, MemoryMetrics, MonitoringConfig, OperationType, PerformanceAnomaly,
    PredictionModelParams, ProfileMetrics, ResourceIsolationConfig, ResourceUsagePattern,
    ScalingDecision, ScalingReason, ScopedThreadPool, ThreadPoolConfig, ThreadPoolManager,
    ThreadPoolProfile, ThreadPoolProfiler, ThreadPoolStats, WorkloadAdaptationConfig,
    WorkloadCharacteristics, WorkloadPattern, WorkloadPredictor,
};
pub use work_stealing::{
    AdaptiveChunking, AdaptiveChunkingStats, CacheAwareStrategy, CacheAwareWorkStealer,
    CacheLocalityOptimizer, CacheOptimizationRecommendations, ChunkPerformance,
    LoadBalancingParams, MatrixOperationType, MemoryAccessPattern, NumaTopology,
    OptimizedSchedulerStats, OptimizedWorkStealingScheduler, PerformanceMonitor, PerformanceStats,
    SchedulerStats, StealingStrategy, WorkComplexity, WorkItem, WorkPriority,
    WorkStealingScheduler,
};

// Re-export matrix operations
pub use work_stealing::matrix_ops::{
    parallel_band_solve, parallel_block_gemm, parallel_cholesky_work_stealing,
    parallel_eigvalsh_work_stealing, parallel_gemm_work_stealing, parallel_hessenberg_reduction,
    parallel_lu_work_stealing, parallel_matvec_work_stealing, parallel_power_iteration,
    parallel_qr_work_stealing, parallel_svd_work_stealing,
};

// Re-export cache-aware operations
pub use work_stealing::parallel_gemm_cache_aware;

/// Global worker configuration
static GLOBAL_WORKERS: Mutex<Option<usize>> = Mutex::new(None);

/// Set the global worker thread count for all operations
///
/// This affects operations that don't explicitly specify a worker count.
/// If set to None, operations will use system defaults.
///
/// # Arguments
///
/// * `workers` - Number of worker threads (None = use system default)
///
/// # Examples
///
/// ```
/// use scirs2_linalg::parallel::set_global_workers;
///
/// // Use 4 threads for all operations
/// set_global_workers(Some(4));
///
/// // Reset to system default
/// set_global_workers(None);
/// ```
#[allow(dead_code)]
pub fn set_global_workers(workers: Option<usize>) {
    if let Ok(mut global) = GLOBAL_WORKERS.lock() {
        *global = workers;

        // Set OpenMP environment variable if specified
        if let Some(num_workers) = workers {
            std::env::set_var("OMP_NUM_THREADS", num_workers.to_string());
        } else {
            // Remove the environment variable to use system default
            std::env::remove_var("OMP_NUM_THREADS");
        }
    }
}

/// Get the current global worker thread count
///
/// # Returns
///
/// * Current global worker count (None = system default)
#[allow(dead_code)]
pub fn get_global_workers() -> Option<usize> {
    GLOBAL_WORKERS.lock().ok().and_then(|global| *global)
}

/// Configure worker threads for an operation
///
/// This function determines the appropriate number of worker threads to use,
/// considering both the operation-specific setting and global configuration.
///
/// # Arguments
///
/// * `workers` - Operation-specific worker count
///
/// # Returns
///
/// * Effective worker count to use
#[allow(dead_code)]
pub fn configure_workers(workers: Option<usize>) -> Option<usize> {
    match workers {
        Some(count) => {
            // Operation-specific setting takes precedence
            std::env::set_var("OMP_NUM_THREADS", count.to_string());
            Some(count)
        }
        None => {
            // Use global setting if available
            let global_workers = get_global_workers();
            if let Some(count) = global_workers {
                std::env::set_var("OMP_NUM_THREADS", count.to_string());
            }
            global_workers
        }
    }
}

/// Worker configuration for batched operations
#[derive(Debug, Clone)]
pub struct WorkerConfig {
    /// Number of worker threads
    pub workers: Option<usize>,
    /// Threshold for using parallel processing
    pub parallel_threshold: usize,
    /// Chunk size for batched operations
    pub chunksize: usize,
}

impl Default for WorkerConfig {
    fn default() -> Self {
        Self {
            workers: None,
            parallel_threshold: 1000,
            chunksize: 64,
        }
    }
}

impl WorkerConfig {
    /// Create a new worker configuration
    pub fn new() -> Self {
        Self::default()
    }

    /// Set the number of worker threads
    pub fn with_workers(mut self, workers: usize) -> Self {
        self.workers = Some(workers);
        self
    }

    /// Set the parallel processing threshold
    pub fn with_threshold(mut self, threshold: usize) -> Self {
        self.parallel_threshold = threshold;
        self
    }

    /// Set the chunk size for batched operations
    pub fn with_chunksize(mut self, chunksize: usize) -> Self {
        self.chunksize = chunksize;
        self
    }

    /// Apply this configuration for the current operation
    pub fn apply(&self) {
        configure_workers(self.workers);
    }
}

/// Scoped worker configuration
///
/// Temporarily sets worker configuration and restores the previous
/// configuration when dropped.
pub struct ScopedWorkers {
    previous_workers: Option<usize>,
}

impl ScopedWorkers {
    /// Create a scoped worker configuration
    ///
    /// # Arguments
    ///
    /// * `workers` - Number of worker threads for this scope
    ///
    /// # Returns
    ///
    /// * ScopedWorkers guard that restores previous configuration on drop
    pub fn new(workers: Option<usize>) -> Self {
        let previous_workers = get_global_workers();
        set_global_workers(workers);
        Self { previous_workers }
    }
}

impl Drop for ScopedWorkers {
    fn drop(&mut self) {
        set_global_workers(self.previous_workers);
    }
}