scirs2-linalg 0.4.2

//! Advanced GPU acceleration features and optimizations
//!
//! This module implements cutting-edge GPU acceleration techniques including:
//! - Dynamic kernel fusion for complex operation chains
//! - Multi-GPU tensor core optimization
//! - Predictive memory bandwidth optimization
//! - Asynchronous operation pipelining with dependency resolution
//! - Advanced scheduling algorithms for optimal resource utilization
//!
//! ## Module Organization
//!
//! - **kernels**: Kernel fusion engine and operation graph management
//! - **memory**: Advanced memory management and bandwidth prediction
//! - **optimization**: Multi-GPU coordination and optimization strategies
//! - **scheduling**: Tensor core scheduling and performance monitoring
//!
//! ## Usage
//!
//! ```rust,no_run
//! # fn main() -> Result<(), Box<dyn std::error::Error>> {
//! use scirs2_linalg::gpu::advanced::*;
//!
//! // Create kernel fusion engine
//! let fusion_engine = AdvancedGpuKernelFusion::<f32>::new()?;
//!
//! // Set up multi-GPU coordinator
//! let mut coordinator = AdvancedMultiGpuCoordinator::new()?;
//!
//! // Create tensor core scheduler
//! let mut scheduler = AdvancedGpuTensorCoreScheduler::<f32>::new()?;
//! # Ok(())
//! # }
//! ```

// Declare submodules
pub mod kernels;
pub mod memory;
pub mod optimization;
pub mod scheduling;

// Re-export main types for convenience
pub use kernels::{
    AdvancedGpuKernelFusion, DependencyEdge, DependencyType, ElementType, FusionCandidate,
    FusionOptimizationParams, FusionRuleSet, FusionStrategy, GpuOperationType, KernelFusionEngine,
    KernelSpecification, MemoryLayout, MemoryRequirements, OperationDependencyGraph, OperationNode,
    PerformanceModel, TensorShape,
};

pub use memory::{
    BandwidthMeasurement, BandwidthPredictionModel, BandwidthPredictor, GCStats, GCStrategy,
    GpuMemoryManager, MemoryAccessPattern, MemoryAllocationStrategy, MemoryBlock,
    MemoryGarbageCollector, MemoryPool, MemoryPoolType, MemoryStats, TensorCorePrecision,
};

pub use optimization::{
    AdvancedMultiGpuCoordinator, BandwidthAllocationPolicy, BandwidthAllocator,
    CommOptimizationAlgorithm, CommunicationPattern, DynamicLoadBalancer, GpuConnection, GpuInfo,
    GpuTopologyMap, GpuWorkPartition, IntelligentPartitioner, InterGpuCommOptimizer,
    InterGpuConnectionType, LoadBalancingAlgorithm, LoadMonitor, MigrationCostModel,
    MigrationPolicy, MigrationStrategy, MigrationTrigger, PartitioningCostModel,
    PartitioningPerformanceRecord, PartitioningStrategy, WorkloadCharacteristics,
};

pub use scheduling::{
    AdvancedGpuTensorCoreScheduler, OperationAnalysis, SchedulingStats, TensorCoreOpType,
    TensorCoreOperation, TensorCorePerformanceMonitor, TensorCoreSchedulingAlgorithm,
    TensorCoreUnit,
};

use crate::error::{LinalgError, LinalgResult};

/// Unified advanced GPU acceleration framework
pub struct AdvancedGpuAccelerationFramework<T>
where
    T: scirs2_core::numeric::Float
        + scirs2_core::numeric::NumAssign
        + scirs2_core::numeric::Zero
        + Send
        + Sync
        + std::fmt::Debug
        + 'static,
{
    /// Kernel fusion engine
    pub fusion_engine: AdvancedGpuKernelFusion<T>,
    /// Multi-GPU coordinator
    pub multi_gpu_coordinator: AdvancedMultiGpuCoordinator,
    /// Tensor core scheduler
    pub tensor_scheduler: AdvancedGpuTensorCoreScheduler<T>,
    /// Memory manager
    pub memory_manager: GpuMemoryManager,
    /// Bandwidth predictor
    pub bandwidth_predictor: BandwidthPredictor,
}

impl<T> AdvancedGpuAccelerationFramework<T>
where
    T: scirs2_core::numeric::Float
        + scirs2_core::numeric::NumAssign
        + scirs2_core::numeric::Zero
        + Send
        + Sync
        + std::fmt::Debug
        + 'static,
{
    /// Create a new advanced GPU acceleration framework
    pub fn new(gpu_id: usize) -> LinalgResult<Self> {
        Ok(Self {
            fusion_engine: AdvancedGpuKernelFusion::new()?,
            multi_gpu_coordinator: AdvancedMultiGpuCoordinator::new()?,
            tensor_scheduler: AdvancedGpuTensorCoreScheduler::new()?,
            memory_manager: GpuMemoryManager::new(gpu_id)?,
            bandwidth_predictor: BandwidthPredictor::new(),
        })
    }

    /// Perform comprehensive GPU optimization
    pub fn optimize_execution(&mut self) -> LinalgResult<()> {
        // 1. Analyze fusion opportunities
        let fusion_candidates = self.fusion_engine.analyze_fusion_opportunities()?;

        // 2. Optimize multi-GPU communication
        self.multi_gpu_coordinator.optimize_communication()?;

        // 3. Balance load across GPUs
        self.multi_gpu_coordinator.balance_load()?;

        // 4. Collect garbage memory
        self.memory_manager.collect_garbage()?;

        Ok(())
    }

    /// Get comprehensive performance statistics
    pub fn get_performance_stats(&self) -> AdvancedPerformanceStats {
        AdvancedPerformanceStats {
            scheduling_stats: self.tensor_scheduler.get_performance_stats(),
            memory_stats: self.memory_manager.get_memory_stats(),
            bandwidth_prediction_accuracy: self.bandwidth_predictor.accuracy,
            total_fusion_candidates: self
                .fusion_engine
                .operation_graph
                .read()
                .expect("Operation failed")
                .fusion_candidates
                .len(),
        }
    }
}

/// Comprehensive performance statistics
#[derive(Debug, Clone)]
pub struct AdvancedPerformanceStats {
    /// Tensor core scheduling statistics
    pub scheduling_stats: SchedulingStats,
    /// Memory management statistics
    pub memory_stats: MemoryStats,
    /// Bandwidth prediction accuracy
    pub bandwidth_prediction_accuracy: f64,
    /// Total number of fusion candidates identified
    pub total_fusion_candidates: usize,
}

/// Initialize the global advanced GPU acceleration framework
pub fn initialize_advanced_gpu_acceleration<T>(
    gpu_id: usize,
) -> LinalgResult<AdvancedGpuAccelerationFramework<T>>
where
    T: scirs2_core::numeric::Float
        + scirs2_core::numeric::NumAssign
        + scirs2_core::numeric::Zero
        + Send
        + Sync
        + std::fmt::Debug
        + 'static,
{
    AdvancedGpuAccelerationFramework::new(gpu_id)
}

/// Get optimization recommendations based on current state
pub fn get_optimization_recommendations(
    stats: &AdvancedPerformanceStats,
) -> Vec<OptimizationRecommendation> {
    let mut recommendations = Vec::new();

    // Memory recommendations
    if stats.memory_stats.fragmentation_count > 100 {
        recommendations.push(OptimizationRecommendation {
            category: RecommendationCategory::Memory,
            description: "High memory fragmentation detected. Consider running garbage collection."
                .to_string(),
            priority: RecommendationPriority::High,
            estimated_benefit: 0.3,
        });
    }

    // Scheduling recommendations
    if stats.scheduling_stats.tensor_core_utilization < 0.5 {
        recommendations.push(OptimizationRecommendation {
            category: RecommendationCategory::Scheduling,
            description: "Low tensor core utilization. Consider batching smaller operations."
                .to_string(),
            priority: RecommendationPriority::Medium,
            estimated_benefit: 0.4,
        });
    }

    // Bandwidth prediction recommendations
    if stats.bandwidth_prediction_accuracy < 0.7 {
        recommendations.push(OptimizationRecommendation {
            category: RecommendationCategory::Prediction,
            description: "Low bandwidth prediction accuracy. Consider updating prediction models."
                .to_string(),
            priority: RecommendationPriority::Low,
            estimated_benefit: 0.2,
        });
    }

    // Fusion recommendations
    if stats.total_fusion_candidates > 50 {
        recommendations.push(OptimizationRecommendation {
            category: RecommendationCategory::Fusion,
            description:
                "Many fusion opportunities available. Enable aggressive fusion optimization."
                    .to_string(),
            priority: RecommendationPriority::High,
            estimated_benefit: 0.5,
        });
    }

    recommendations
}

/// Optimization recommendation
#[derive(Debug, Clone)]
pub struct OptimizationRecommendation {
    /// Recommendation category
    pub category: RecommendationCategory,
    /// Description of the recommendation
    pub description: String,
    /// Priority level
    pub priority: RecommendationPriority,
    /// Estimated performance benefit (0.0-1.0)
    pub estimated_benefit: f64,
}

/// Categories of optimization recommendations
#[derive(Debug, Clone, PartialEq)]
pub enum RecommendationCategory {
    /// Memory management optimization
    Memory,
    /// Scheduling optimization
    Scheduling,
    /// Prediction model optimization
    Prediction,
    /// Kernel fusion optimization
    Fusion,
    /// Multi-GPU coordination
    MultiGpu,
}

/// Priority levels for recommendations
#[derive(Debug, Clone, PartialEq, PartialOrd)]
pub enum RecommendationPriority {
    /// Low priority recommendation
    Low,
    /// Medium priority recommendation
    Medium,
    /// High priority recommendation
    High,
    /// Critical priority recommendation
    Critical,
}

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

    #[test]
    fn test_advanced_framework_creation() {
        let framework = AdvancedGpuAccelerationFramework::<f32>::new(0).expect("Operation failed");
        assert_eq!(framework.memory_manager.gpu_id, 0);
    }

    #[test]
    fn test_optimization_recommendations() {
        let stats = AdvancedPerformanceStats {
            scheduling_stats: SchedulingStats {
                average_throughput: 100.0,
                average_latency: 0.01,
                total_operations_scheduled: 1000,
                tensor_core_utilization: 0.3, // Low utilization
            },
            memory_stats: MemoryStats {
                total_allocated: 1024 * 1024,
                total_free: 512 * 1024,
                fragmentation_count: 150, // High fragmentation
                pool_count: 4,
                gc_stats: GCStats::new(),
            },
            bandwidth_prediction_accuracy: 0.85,
            total_fusion_candidates: 25,
        };

        let recommendations = get_optimization_recommendations(&stats);
        assert!(!recommendations.is_empty());

        // Should have memory and scheduling recommendations
        assert!(recommendations
            .iter()
            .any(|r| r.category == RecommendationCategory::Memory));
        assert!(recommendations
            .iter()
            .any(|r| r.category == RecommendationCategory::Scheduling));
    }

    #[test]
    fn test_initialize_advanced_gpu_acceleration() {
        let framework = initialize_advanced_gpu_acceleration::<f32>(0).expect("Operation failed");
        assert_eq!(framework.memory_manager.gpu_id, 0);
    }
}