Module parallel 

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.

Re-exports

pub use thread_pools::get_global_manager;

pub use thread_pools::AdvancedPerformanceStats;

pub use thread_pools::AdvancedPerformanceThreadPool;

pub use thread_pools::AdvancedThreadPoolConfig;

pub use thread_pools::AffinityStrategy;

pub use thread_pools::AnomalySeverity;

pub use thread_pools::AnomalyType;

pub use thread_pools::CacheAllocationPolicy;

pub use thread_pools::DecompositionType;

pub use thread_pools::DynamicSizingConfig;

pub use thread_pools::DynamicThreadManager;

pub use thread_pools::IterativeSolverType;

pub use thread_pools::MemoryMetrics;

pub use thread_pools::MonitoringConfig;

pub use thread_pools::OperationType;

pub use thread_pools::PerformanceAnomaly;

pub use thread_pools::PredictionModelParams;

pub use thread_pools::ProfileMetrics;

pub use thread_pools::ResourceIsolationConfig;

pub use thread_pools::ResourceUsagePattern;

pub use thread_pools::ScalingDecision;

pub use thread_pools::ScalingReason;

pub use thread_pools::ScopedThreadPool;

pub use thread_pools::ThreadPoolConfig;

pub use thread_pools::ThreadPoolManager;

pub use thread_pools::ThreadPoolProfile;

pub use thread_pools::ThreadPoolProfiler;

pub use thread_pools::ThreadPoolStats;

pub use thread_pools::WorkloadAdaptationConfig;

pub use thread_pools::WorkloadCharacteristics;

pub use thread_pools::WorkloadPattern;

pub use thread_pools::WorkloadPredictor;

pub use work_stealing::AdaptiveChunking;

pub use work_stealing::AdaptiveChunkingStats;

pub use work_stealing::CacheAwareStrategy;

pub use work_stealing::CacheAwareWorkStealer;

pub use work_stealing::CacheLocalityOptimizer;

pub use work_stealing::CacheOptimizationRecommendations;

pub use work_stealing::ChunkPerformance;

pub use work_stealing::LoadBalancingParams;

pub use work_stealing::MatrixOperationType;

pub use work_stealing::MemoryAccessPattern;

pub use work_stealing::NumaTopology;

pub use work_stealing::OptimizedSchedulerStats;

pub use work_stealing::OptimizedWorkStealingScheduler;

pub use work_stealing::PerformanceMonitor;

pub use work_stealing::PerformanceStats;

pub use work_stealing::SchedulerStats;

pub use work_stealing::StealingStrategy;

pub use work_stealing::WorkComplexity;

pub use work_stealing::WorkItem;

pub use work_stealing::WorkPriority;

pub use work_stealing::WorkStealingScheduler;

pub use work_stealing::matrix_ops::parallel_band_solve;

pub use work_stealing::matrix_ops::parallel_block_gemm;

pub use work_stealing::matrix_ops::parallel_cholesky_work_stealing;

pub use work_stealing::matrix_ops::parallel_eigvalsh_work_stealing;

pub use work_stealing::matrix_ops::parallel_gemm_work_stealing;

pub use work_stealing::matrix_ops::parallel_hessenberg_reduction;

pub use work_stealing::matrix_ops::parallel_lu_work_stealing;

pub use work_stealing::matrix_ops::parallel_matvec_work_stealing;

pub use work_stealing::matrix_ops::parallel_power_iteration;

pub use work_stealing::matrix_ops::parallel_qr_work_stealing;

pub use work_stealing::matrix_ops::parallel_svd_work_stealing;

pub use work_stealing::parallel_gemm_cache_aware;

Modules

adaptive

Adaptive algorithm selection based on data size and worker configuration

advanced_work_stealing

Advanced work-stealing algorithms with intelligent scheduling

affinity

CPU affinity and thread pinning

algorithms

Parallel linear algebra algorithms

iter

Parallel iterator utilities for matrix operations

numa

NUMA-aware parallel computing

scheduler

Work-stealing scheduler optimizations

thread_pool

Thread pool configurations for linear algebra operations

thread_pools

Enhanced thread pool configurations and management

work_stealing

Work-stealing scheduler implementation for dynamic load balancing

Structs

ScopedWorkers

Scoped worker configuration

WorkerConfig

Worker configuration for batched operations

Functions

configure_workers

Configure worker threads for an operation

get_global_workers

Get the current global worker thread count

set_global_workers

Set the global worker thread count for all operations