scirs2-linalg 0.4.2

//! Advanced GPU optimization strategies and multi-GPU coordination
//!
//! This module implements sophisticated optimization techniques including:
//! - Multi-GPU tensor core optimization
//! - Intelligent workload partitioning across multiple GPUs
//! - Dynamic load balancing with migration policies
//! - Inter-GPU communication optimization

use super::kernels::{GpuOperationType, TensorShape};
use crate::error::{LinalgError, LinalgResult};
use crate::gpu::GpuDeviceType;
use scirs2_core::ndarray::Array2;
use std::collections::{HashMap, VecDeque};
use std::time::Instant;

/// Advanced multi-GPU coordinator for optimization
#[derive(Debug)]
pub struct AdvancedMultiGpuCoordinator {
    /// GPU topology map
    gpu_topology: GpuTopologyMap,
    /// Intelligent workload partitioner
    workload_partitioner: IntelligentPartitioner,
    /// Dynamic load balancer
    load_balancer: DynamicLoadBalancer,
    /// Inter-GPU communication optimizer
    communication_optimizer: InterGpuCommOptimizer,
    /// GPU memory managers
    memory_managers: HashMap<usize, super::memory::GpuMemoryManager>,
}

/// GPU topology mapping for optimization decisions
#[derive(Debug)]
pub struct GpuTopologyMap {
    /// Available GPUs
    pub gpus: Vec<GpuInfo>,
    /// Inter-GPU connections
    pub connections: Vec<GpuConnection>,
    /// Memory bandwidth matrix
    pub bandwidth_matrix: Array2<f64>,
    /// Latency matrix
    pub latency_matrix: Array2<f64>,
}

/// GPU information for optimization
#[derive(Debug, Clone)]
pub struct GpuInfo {
    /// GPU ID
    pub id: usize,
    /// GPU type
    pub gpu_type: GpuDeviceType,
    /// Memory size in bytes
    pub memory_size: usize,
    /// Compute capability
    pub compute_capability: (u32, u32),
    /// Number of SMs/CUs
    pub multiprocessor_count: u32,
    /// Tensor core support
    pub tensor_core_support: bool,
    /// Current utilization
    pub utilization: f64,
}

/// GPU connection information for communication optimization
#[derive(Debug, Clone)]
pub struct GpuConnection {
    /// Source GPU ID
    pub from_gpu: usize,
    /// Target GPU ID
    pub to_gpu: usize,
    /// Connection type
    pub connection_type: InterGpuConnectionType,
    /// Bandwidth in GB/s
    pub bandwidth: f64,
    /// Latency in microseconds
    pub latency: f64,
}

/// Types of inter-GPU connections
#[derive(Debug, Clone, PartialEq)]
pub enum InterGpuConnectionType {
    /// NVIDIA NVLink
    NVLink,
    /// PCIe connection
    PCIe,
    /// InfiniBand network
    InfiniBand,
    /// Ethernet network
    Ethernet,
    /// Direct Memory Access
    DirectMemoryAccess,
}

/// Intelligent workload partitioner
#[derive(Debug)]
pub struct IntelligentPartitioner {
    /// Partitioning strategies
    strategies: Vec<PartitioningStrategy>,
    /// Cost models for different partitioning schemes
    cost_models: HashMap<String, PartitioningCostModel>,
    /// Historical performance data
    performance_history: VecDeque<PartitioningPerformanceRecord>,
}

/// Workload partitioning strategies
#[derive(Debug, Clone)]
pub enum PartitioningStrategy {
    /// Partition by data dimension
    DataParallel,
    /// Partition by model dimension
    ModelParallel,
    /// Pipeline parallel execution
    PipelineParallel,
    /// Hybrid partitioning
    Hybrid,
    /// Dynamic adaptive partitioning
    Adaptive,
}

/// Cost model for partitioning decisions
#[derive(Debug)]
pub struct PartitioningCostModel {
    /// Computation cost estimation
    pub computation_cost_fn: fn(&TensorShape, &[GpuInfo]) -> f64,
    /// Communication cost estimation
    pub communication_cost_fn: fn(&TensorShape, &GpuTopologyMap) -> f64,
    /// Memory cost estimation
    pub memory_cost_fn: fn(&TensorShape, &[GpuInfo]) -> f64,
}

/// Performance record for partitioning
#[derive(Debug, Clone)]
pub struct PartitioningPerformanceRecord {
    /// Workload characteristics
    pub workload: WorkloadCharacteristics,
    /// Partitioning used
    pub partitioning: PartitioningStrategy,
    /// Execution time
    pub execution_time: f64,
    /// Memory usage
    pub memory_usage: usize,
    /// Communication overhead
    pub communication_overhead: f64,
    /// Timestamp
    pub timestamp: Instant,
}

/// Workload characteristics for optimization
#[derive(Debug, Clone)]
pub struct WorkloadCharacteristics {
    /// Operation types
    pub operation_types: Vec<GpuOperationType>,
    /// Data sizes
    pub data_sizes: Vec<TensorShape>,
    /// Computation intensity
    pub computation_intensity: f64,
    /// Memory intensity
    pub memory_intensity: f64,
}

/// Dynamic load balancer
#[derive(Debug)]
pub struct DynamicLoadBalancer {
    /// Load balancing algorithms
    algorithms: Vec<LoadBalancingAlgorithm>,
    /// Load monitoring
    load_monitor: LoadMonitor,
    /// Migration policies
    migration_policies: Vec<MigrationPolicy>,
}

/// Load balancing algorithms
#[derive(Debug, Clone)]
pub enum LoadBalancingAlgorithm {
    /// Simple round-robin distribution
    RoundRobin,
    /// Assign to least loaded GPU
    LeastLoaded,
    /// Weighted round-robin based on capabilities
    WeightedRoundRobin,
    /// Power-aware load balancing
    PowerAware,
    /// Predictive least loaded
    PredictiveLeastLoaded,
    /// Machine learning driven balancing
    MLDriven,
}

/// Load monitor for GPUs
#[derive(Debug)]
pub struct LoadMonitor {
    /// GPU utilization history
    pub utilization_history: HashMap<usize, VecDeque<f64>>,
    /// Memory usage history
    pub memory_history: HashMap<usize, VecDeque<usize>>,
    /// Temperature history
    pub temperature_history: HashMap<usize, VecDeque<f64>>,
    /// Power consumption history
    pub power_history: HashMap<usize, VecDeque<f64>>,
}

/// Migration policy for load balancing
#[derive(Debug)]
pub struct MigrationPolicy {
    /// Trigger conditions
    pub trigger_conditions: Vec<MigrationTrigger>,
    /// Migration cost model
    pub cost_model: MigrationCostModel,
    /// Migration strategy
    pub strategy: MigrationStrategy,
}

/// Triggers for workload migration
#[derive(Debug, Clone)]
pub enum MigrationTrigger {
    /// Utilization imbalance threshold
    UtilizationImbalance(f64),
    /// Memory pressure threshold
    MemoryPressure(f64),
    /// Temperature threshold
    TemperatureThreshold(f64),
    /// Power limit threshold
    PowerLimit(f64),
    /// Performance degradation threshold
    PerformanceDegradation(f64),
}

/// Cost model for migration decisions
#[derive(Debug)]
pub struct MigrationCostModel {
    /// Data transfer cost
    pub transfer_cost_fn: fn(usize, &GpuConnection) -> f64,
    /// Interruption cost
    pub interruption_cost: f64,
    /// Setup cost on new GPU
    pub setup_cost: f64,
}

/// Migration strategies
#[derive(Debug, Clone)]
pub enum MigrationStrategy {
    /// Immediate migration
    Immediate,
    /// Gradual migration
    Gradual,
    /// Checkpoint-based migration
    Checkpoint,
    /// Background migration
    Background,
}

/// Inter-GPU communication optimizer
#[derive(Debug)]
pub struct InterGpuCommOptimizer {
    /// Communication patterns
    patterns: Vec<CommunicationPattern>,
    /// Optimization algorithms
    algorithms: Vec<CommOptimizationAlgorithm>,
    /// Bandwidth allocation
    bandwidth_allocator: BandwidthAllocator,
}

/// Communication patterns for optimization
#[derive(Debug, Clone)]
pub struct CommunicationPattern {
    /// Source GPU
    pub source: usize,
    /// Destination GPU
    pub destination: usize,
    /// Data size
    pub data_size: usize,
    /// Frequency
    pub frequency: f64,
    /// Latency sensitivity
    pub latency_sensitive: bool,
}

/// Communication optimization algorithms
#[derive(Debug, Clone)]
pub enum CommOptimizationAlgorithm {
    /// All-reduce communication
    AllReduce,
    /// All-gather communication
    AllGather,
    /// Broadcast communication
    Broadcast,
    /// Reduce-scatter communication
    ReduceScatter,
    /// Point-to-point communication
    PointToPoint,
    /// Tree-based communication
    Tree,
    /// Ring-based communication
    Ring,
    /// Butterfly communication
    Butterfly,
}

/// Bandwidth allocator for inter-GPU communication
#[derive(Debug)]
pub struct BandwidthAllocator {
    /// Total available bandwidth per connection
    pub available_bandwidth: HashMap<(usize, usize), f64>,
    /// Current allocations
    pub current_allocations: HashMap<(usize, usize), f64>,
    /// Allocation policies
    pub policies: Vec<BandwidthAllocationPolicy>,
}

/// Bandwidth allocation policies
#[derive(Debug, Clone)]
pub enum BandwidthAllocationPolicy {
    /// Fair share allocation
    FairShare,
    /// Priority-based allocation
    PriorityBased,
    /// Deadline-driven allocation
    DeadlineDriven,
    /// Throughput optimal allocation
    ThroughputOptimal,
}

impl AdvancedMultiGpuCoordinator {
    /// Create a new multi-GPU coordinator
    pub fn new() -> LinalgResult<Self> {
        Ok(Self {
            gpu_topology: GpuTopologyMap::detect()?,
            workload_partitioner: IntelligentPartitioner::new(),
            load_balancer: DynamicLoadBalancer::new(),
            communication_optimizer: InterGpuCommOptimizer::new(),
            memory_managers: HashMap::new(),
        })
    }

    /// Execute multi-GPU optimization
    pub fn execute_multi_gpu_fusion<T>(
        &mut self,
        fusion_plan: &[super::kernels::FusionCandidate],
    ) -> LinalgResult<Vec<Array2<T>>>
    where
        T: Clone + scirs2_core::numeric::Zero,
    {
        // Simplified multi-GPU execution
        let mut results = Vec::new();

        for candidate in fusion_plan {
            // Partition work across available GPUs
            let partition = self.workload_partitioner.partition_workload(candidate)?;

            // Execute on each GPU
            for gpu_work in partition {
                let result = self.execute_on_gpu(gpu_work)?;
                results.push(result);
            }
        }

        Ok(results)
    }

    /// Execute work on a specific GPU
    fn execute_on_gpu<T>(&self, _work: GpuWorkPartition) -> LinalgResult<Array2<T>>
    where
        T: Clone + scirs2_core::numeric::Zero,
    {
        // Simplified GPU execution
        Ok(Array2::zeros((1, 1)))
    }

    /// Optimize inter-GPU communication
    pub fn optimize_communication(&mut self) -> LinalgResult<()> {
        // Analyze communication patterns
        let patterns = self.communication_optimizer.analyze_patterns()?;

        // Apply optimization algorithms
        for pattern in patterns {
            self.communication_optimizer.optimize_pattern(&pattern)?;
        }

        Ok(())
    }

    /// Balance load across GPUs
    pub fn balance_load(&mut self) -> LinalgResult<()> {
        // Check for load imbalance
        if self.load_balancer.detect_imbalance()? {
            // Apply load balancing
            self.load_balancer.rebalance(&self.gpu_topology)?;
        }

        Ok(())
    }
}

/// GPU work partition for multi-GPU execution
#[derive(Debug)]
pub struct GpuWorkPartition {
    /// GPU ID
    pub gpu_id: usize,
    /// Operations to execute
    pub operations: Vec<usize>,
    /// Data slices
    pub data_slices: Vec<(usize, usize)>,
}

impl GpuTopologyMap {
    /// Detect GPU topology
    pub fn detect() -> LinalgResult<Self> {
        // Simplified GPU topology detection
        Ok(Self {
            gpus: Vec::new(),
            connections: Vec::new(),
            bandwidth_matrix: Array2::zeros((0, 0)),
            latency_matrix: Array2::zeros((0, 0)),
        })
    }

    /// Get optimal GPU for operation
    pub fn get_optimal_gpu(&self, workload: &WorkloadCharacteristics) -> Option<usize> {
        // Simplified GPU selection based on utilization
        self.gpus
            .iter()
            .min_by(|a, b| {
                a.utilization
                    .partial_cmp(&b.utilization)
                    .expect("Operation failed")
            })
            .map(|gpu| gpu.id)
    }
}

impl IntelligentPartitioner {
    /// Create a new intelligent partitioner
    pub fn new() -> Self {
        Self {
            strategies: vec![PartitioningStrategy::DataParallel],
            cost_models: HashMap::new(),
            performance_history: VecDeque::new(),
        }
    }

    /// Partition workload across GPUs
    pub fn partition_workload(
        &self,
        _candidate: &super::kernels::FusionCandidate,
    ) -> LinalgResult<Vec<GpuWorkPartition>> {
        // Simplified partitioning
        Ok(vec![GpuWorkPartition {
            gpu_id: 0,
            operations: vec![0],
            data_slices: vec![(0, 1000)],
        }])
    }

    /// Select optimal partitioning strategy
    pub fn select_strategy(&self, workload: &WorkloadCharacteristics) -> PartitioningStrategy {
        // Simplified strategy selection
        match workload.computation_intensity {
            x if x > 0.8 => PartitioningStrategy::ModelParallel,
            x if x > 0.5 => PartitioningStrategy::DataParallel,
            _ => PartitioningStrategy::Hybrid,
        }
    }
}

impl DynamicLoadBalancer {
    /// Create a new dynamic load balancer
    pub fn new() -> Self {
        Self {
            algorithms: vec![LoadBalancingAlgorithm::LeastLoaded],
            load_monitor: LoadMonitor::new(),
            migration_policies: Vec::new(),
        }
    }

    /// Detect load imbalance
    pub fn detect_imbalance(&self) -> LinalgResult<bool> {
        // Simplified imbalance detection
        Ok(false)
    }

    /// Rebalance load across GPUs
    pub fn rebalance(&mut self, _topology: &GpuTopologyMap) -> LinalgResult<()> {
        // Simplified rebalancing
        Ok(())
    }
}

impl LoadMonitor {
    /// Create a new load monitor
    pub fn new() -> Self {
        Self {
            utilization_history: HashMap::new(),
            memory_history: HashMap::new(),
            temperature_history: HashMap::new(),
            power_history: HashMap::new(),
        }
    }

    /// Record GPU metrics
    pub fn record_metrics(&mut self, gpu_id: usize, utilization: f64, memory_usage: usize) {
        // Record utilization
        self.utilization_history
            .entry(gpu_id)
            .or_default()
            .push_back(utilization);

        // Record memory usage
        self.memory_history
            .entry(gpu_id)
            .or_default()
            .push_back(memory_usage);

        // Keep history size manageable
        if let Some(history) = self.utilization_history.get_mut(&gpu_id) {
            if history.len() > 1000 {
                history.pop_front();
            }
        }
    }

    /// Get average utilization for GPU
    pub fn get_average_utilization(&self, gpu_id: usize) -> f64 {
        if let Some(history) = self.utilization_history.get(&gpu_id) {
            if history.is_empty() {
                0.0
            } else {
                history.iter().sum::<f64>() / history.len() as f64
            }
        } else {
            0.0
        }
    }
}

impl InterGpuCommOptimizer {
    /// Create a new communication optimizer
    pub fn new() -> Self {
        Self {
            patterns: Vec::new(),
            algorithms: vec![CommOptimizationAlgorithm::AllReduce],
            bandwidth_allocator: BandwidthAllocator::new(),
        }
    }

    /// Analyze communication patterns
    pub fn analyze_patterns(&self) -> LinalgResult<Vec<CommunicationPattern>> {
        // Simplified pattern analysis
        Ok(self.patterns.clone())
    }

    /// Optimize communication pattern
    pub fn optimize_pattern(&mut self, _pattern: &CommunicationPattern) -> LinalgResult<()> {
        // Simplified optimization
        Ok(())
    }
}

impl BandwidthAllocator {
    /// Create a new bandwidth allocator
    pub fn new() -> Self {
        Self {
            available_bandwidth: HashMap::new(),
            current_allocations: HashMap::new(),
            policies: vec![BandwidthAllocationPolicy::FairShare],
        }
    }

    /// Allocate bandwidth for connection
    pub fn allocate_bandwidth(
        &mut self,
        connection: (usize, usize),
        requested: f64,
    ) -> LinalgResult<f64> {
        let available = self.available_bandwidth.get(&connection).unwrap_or(&0.0);
        let current = self.current_allocations.get(&connection).unwrap_or(&0.0);

        let allocatable = (available - current).max(0.0);
        let allocated = requested.min(allocatable);

        self.current_allocations
            .insert(connection, current + allocated);

        Ok(allocated)
    }
}

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

    #[test]
    fn test_multi_gpu_coordinator_creation() {
        let coordinator = AdvancedMultiGpuCoordinator::new().expect("Operation failed");
        assert!(coordinator.gpu_topology.gpus.is_empty());
    }

    #[test]
    fn test_intelligent_partitioner() {
        let partitioner = IntelligentPartitioner::new();
        assert_eq!(partitioner.strategies.len(), 1);
    }

    #[test]
    fn test_load_monitor() {
        let mut monitor = LoadMonitor::new();
        monitor.record_metrics(0, 0.5, 1024);
        assert_eq!(monitor.get_average_utilization(0), 0.5);
    }

    #[test]
    fn test_bandwidth_allocator() {
        let mut allocator = BandwidthAllocator::new();
        allocator.available_bandwidth.insert((0, 1), 100.0);

        let allocated = allocator
            .allocate_bandwidth((0, 1), 50.0)
            .expect("Operation failed");
        assert_eq!(allocated, 50.0);
    }
}