oxirs-vec 0.2.4

//! FAISS GPU Integration for Massive Performance Acceleration
//!
//! This module provides comprehensive GPU acceleration integration with FAISS GPU capabilities,
//! enabling massive performance improvements for large-scale vector operations.
//!
//! Features:
//! - Multi-GPU support with automatic load balancing
//! - GPU memory management and optimization
//! - Asynchronous GPU operations with streaming
//! - GPU-CPU hybrid processing
//! - Dynamic workload distribution
//! - GPU performance monitoring and tuning

use crate::{
    faiss_integration::{FaissConfig, FaissSearchParams},
    gpu::GpuExecutionConfig,
};
use anyhow::{Error as AnyhowError, Result};
use serde::{Deserialize, Serialize};
use std::collections::{BTreeMap, HashMap, VecDeque};
use std::sync::{
    atomic::{AtomicUsize, Ordering},
    Arc, Mutex, RwLock,
};
use std::time::{Duration, Instant};
use tokio::sync::oneshot;
use tracing::{debug, error, info, span, warn, Level};

/// GPU configuration for FAISS integration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FaissGpuConfig {
    /// GPU device IDs to use
    pub device_ids: Vec<i32>,
    /// Memory allocation per device (bytes)
    pub memory_per_device: usize,
    /// Enable multi-GPU distributed processing
    pub enable_multi_gpu: bool,
    /// GPU memory management strategy
    pub memory_strategy: GpuMemoryStrategy,
    /// Compute stream configuration
    pub stream_config: GpuStreamConfig,
    /// Performance optimization settings
    pub optimization: GpuOptimizationConfig,
    /// Error handling and recovery
    pub error_handling: GpuErrorConfig,
}

/// GPU memory management strategies
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum GpuMemoryStrategy {
    /// Pre-allocate fixed memory pools
    FixedPool,
    /// Dynamic allocation as needed
    Dynamic,
    /// Unified memory management
    Unified,
    /// Memory streaming for large datasets
    Streaming { chunk_size: usize },
}

/// GPU compute stream configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GpuStreamConfig {
    /// Number of compute streams per device
    pub streams_per_device: usize,
    /// Enable stream overlapping
    pub enable_overlapping: bool,
    /// Stream priority levels
    pub priority_levels: Vec<i32>,
    /// Synchronization strategy
    pub sync_strategy: SyncStrategy,
}

/// Stream synchronization strategies
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum SyncStrategy {
    /// Block until completion
    Blocking,
    /// Non-blocking with callbacks
    NonBlocking,
    /// Event-based synchronization
    EventBased,
    /// Cooperative synchronization
    Cooperative,
}

/// GPU optimization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GpuOptimizationConfig {
    /// Enable Tensor Core utilization
    pub enable_tensor_cores: bool,
    /// Enable mixed precision (FP16/FP32)
    pub enable_mixed_precision: bool,
    /// Memory coalescing optimization
    pub enable_coalescing: bool,
    /// Kernel fusion optimization
    pub enable_kernel_fusion: bool,
    /// Cache optimization settings
    pub cache_config: GpuCacheConfig,
    /// Batch processing optimization
    pub batch_optimization: BatchOptimizationConfig,
}

/// GPU cache configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GpuCacheConfig {
    /// L1 cache configuration
    pub l1_cache_config: CacheConfig,
    /// Shared memory configuration
    pub shared_memory_config: CacheConfig,
    /// Enable cache prefetching
    pub enable_prefetching: bool,
    /// Cache line size optimization
    pub cache_line_size: usize,
}

/// Cache configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum CacheConfig {
    /// Prefer L1 cache
    PreferL1,
    /// Prefer shared memory
    PreferShared,
    /// Equal allocation
    Equal,
    /// Disable cache
    Disabled,
}

/// Batch optimization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BatchOptimizationConfig {
    /// Optimal batch size for each operation
    pub optimal_batch_sizes: HashMap<String, usize>,
    /// Enable dynamic batch sizing
    pub enable_dynamic_batching: bool,
    /// Batch coalescence threshold
    pub coalescence_threshold: usize,
    /// Maximum batch size
    pub max_batch_size: usize,
}

/// GPU error handling configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GpuErrorConfig {
    /// Enable automatic error recovery
    pub enable_auto_recovery: bool,
    /// Maximum retry attempts
    pub max_retries: usize,
    /// Fallback to CPU on GPU failure
    pub fallback_to_cpu: bool,
    /// Error logging level
    pub error_logging_level: String,
}

impl Default for FaissGpuConfig {
    fn default() -> Self {
        Self {
            device_ids: vec![0],
            memory_per_device: 2 * 1024 * 1024 * 1024, // 2GB
            enable_multi_gpu: false,
            memory_strategy: GpuMemoryStrategy::Dynamic,
            stream_config: GpuStreamConfig {
                streams_per_device: 4,
                enable_overlapping: true,
                priority_levels: vec![0, 1, 2],
                sync_strategy: SyncStrategy::NonBlocking,
            },
            optimization: GpuOptimizationConfig {
                enable_tensor_cores: true,
                enable_mixed_precision: true,
                enable_coalescing: true,
                enable_kernel_fusion: true,
                cache_config: GpuCacheConfig {
                    l1_cache_config: CacheConfig::PreferL1,
                    shared_memory_config: CacheConfig::PreferShared,
                    enable_prefetching: true,
                    cache_line_size: 128,
                },
                batch_optimization: BatchOptimizationConfig {
                    optimal_batch_sizes: {
                        let mut sizes = HashMap::new();
                        sizes.insert("search".to_string(), 1024);
                        sizes.insert("add".to_string(), 512);
                        sizes.insert("train".to_string(), 256);
                        sizes
                    },
                    enable_dynamic_batching: true,
                    coalescence_threshold: 64,
                    max_batch_size: 4096,
                },
            },
            error_handling: GpuErrorConfig {
                enable_auto_recovery: true,
                max_retries: 3,
                fallback_to_cpu: true,
                error_logging_level: "warn".to_string(),
            },
        }
    }
}

/// FAISS GPU-accelerated index
pub struct FaissGpuIndex {
    /// Base FAISS configuration
    faiss_config: FaissConfig,
    /// GPU-specific configuration
    gpu_config: FaissGpuConfig,
    /// GPU runtime for operations
    gpu_runtime: Arc<GpuExecutionConfig>,
    /// GPU memory pools per device
    memory_pools: Arc<RwLock<HashMap<i32, FaissGpuMemoryPool>>>,
    /// Compute streams per device
    compute_streams: Arc<RwLock<HashMap<i32, Vec<GpuComputeStream>>>>,
    /// Performance statistics
    stats: Arc<RwLock<GpuPerformanceStats>>,
    /// Work queue for GPU operations
    work_queue: Arc<Mutex<VecDeque<GpuOperation>>>,
    /// Operation results cache
    results_cache: Arc<RwLock<HashMap<String, CachedResult>>>,
    /// Load balancer for multi-GPU
    load_balancer: Arc<RwLock<GpuLoadBalancer>>,
}

/// GPU memory pool for efficient allocation
#[derive(Debug)]
pub struct FaissGpuMemoryPool {
    /// Device ID
    pub device_id: i32,
    /// Total pool size
    pub total_size: usize,
    /// Currently allocated size
    pub allocated_size: AtomicUsize,
    /// Free blocks
    pub free_blocks: Arc<Mutex<BTreeMap<usize, Vec<GpuMemoryBlock>>>>,
    /// Allocated blocks
    pub allocated_blocks: Arc<RwLock<HashMap<usize, GpuMemoryBlock>>>,
    /// Allocation statistics
    pub allocation_stats: Arc<RwLock<AllocationStatistics>>,
}

/// GPU memory block
#[derive(Debug)]
pub struct GpuMemoryBlock {
    /// Block address on GPU
    pub gpu_address: usize,
    /// Block size in bytes
    pub size: usize,
    /// Allocation timestamp
    pub allocated_at: Instant,
    /// Reference count
    pub ref_count: AtomicUsize,
    /// Block type
    pub block_type: MemoryBlockType,
}

impl Clone for GpuMemoryBlock {
    fn clone(&self) -> Self {
        Self {
            gpu_address: self.gpu_address,
            size: self.size,
            allocated_at: self.allocated_at,
            ref_count: AtomicUsize::new(self.ref_count.load(Ordering::Relaxed)),
            block_type: self.block_type,
        }
    }
}

/// Memory block types
#[derive(Debug, Clone, Copy)]
pub enum MemoryBlockType {
    /// Vector storage
    Vectors,
    /// Index structure
    IndexData,
    /// Temporary computation
    Temporary,
    /// Result buffer
    Results,
}

/// GPU compute stream
#[derive(Debug)]
pub struct GpuComputeStream {
    /// Stream ID
    pub stream_id: usize,
    /// Device ID
    pub device_id: i32,
    /// Stream handle (simulated)
    pub stream_handle: usize,
    /// Stream priority
    pub priority: i32,
    /// Current operation
    pub current_operation: Arc<Mutex<Option<GpuOperation>>>,
    /// Operation history
    pub operation_history: Arc<RwLock<VecDeque<CompletedOperation>>>,
    /// Stream utilization
    pub utilization: Arc<RwLock<StreamUtilization>>,
}

/// GPU operation
#[derive(Debug)]
pub struct GpuOperation {
    /// Operation ID
    pub id: String,
    /// Operation type
    pub operation_type: GpuOperationType,
    /// Input data
    pub input_data: GpuOperationData,
    /// Expected output size
    pub output_size: usize,
    /// Priority level
    pub priority: i32,
    /// Timeout
    pub timeout: Option<Duration>,
    /// Result sender
    pub result_sender: Option<oneshot::Sender<GpuOperationResult>>,
}

/// GPU operation types
#[derive(Debug, Clone)]
pub enum GpuOperationType {
    /// Vector search operation
    Search {
        query_vectors: Vec<Vec<f32>>,
        k: usize,
        search_params: FaissSearchParams,
    },
    /// Vector addition operation
    Add {
        vectors: Vec<Vec<f32>>,
        ids: Vec<String>,
    },
    /// Index training operation
    Train { training_vectors: Vec<Vec<f32>> },
    /// Index optimization operation
    Optimize,
    /// Memory transfer operation
    MemoryTransfer {
        source: TransferSource,
        destination: TransferDestination,
        size: usize,
    },
}

/// GPU operation data
#[derive(Debug, Clone)]
pub enum GpuOperationData {
    /// Raw vector data
    Vectors(Vec<Vec<f32>>),
    /// Serialized index data
    IndexData(Vec<u8>),
    /// Query parameters
    QueryParams(HashMap<String, Vec<u8>>),
    /// Empty operation
    Empty,
}

/// Transfer source/destination
#[derive(Debug, Clone)]
pub enum TransferSource {
    CpuMemory(Vec<u8>),
    GpuMemory { device_id: i32, address: usize },
    Disk(std::path::PathBuf),
}

#[derive(Debug, Clone)]
pub enum TransferDestination {
    CpuMemory,
    GpuMemory { device_id: i32, address: usize },
    Disk(std::path::PathBuf),
}

/// GPU operation result
#[derive(Debug, Clone)]
pub struct GpuOperationResult {
    /// Operation ID
    pub operation_id: String,
    /// Success status
    pub success: bool,
    /// Result data
    pub result_data: GpuResultData,
    /// Execution time
    pub execution_time: Duration,
    /// Memory usage
    pub memory_used: usize,
    /// Error message if failed
    pub error_message: Option<String>,
}

/// GPU result data
#[derive(Debug, Clone)]
pub enum GpuResultData {
    /// Search results
    SearchResults(Vec<Vec<(String, f32)>>),
    /// Training completion
    TrainingComplete,
    /// Addition completion
    AdditionComplete,
    /// Optimization metrics
    OptimizationMetrics(HashMap<String, f64>),
    /// Memory transfer completion
    TransferComplete,
    /// Error result
    Error(String),
}

/// Completed operation record
#[derive(Debug, Clone)]
pub struct CompletedOperation {
    /// Operation ID
    pub operation_id: String,
    /// Operation type
    pub operation_type: String,
    /// Start time
    pub start_time: Instant,
    /// End time
    pub end_time: Instant,
    /// Success status
    pub success: bool,
    /// Memory used
    pub memory_used: usize,
}

/// Stream utilization metrics
#[derive(Debug, Clone, Default)]
pub struct StreamUtilization {
    /// Total operations processed
    pub total_operations: usize,
    /// Total execution time
    pub total_execution_time: Duration,
    /// Average execution time
    pub avg_execution_time: Duration,
    /// Utilization percentage
    pub utilization_percentage: f32,
    /// Idle time
    pub idle_time: Duration,
}

/// GPU performance statistics
#[derive(Debug, Clone, Default)]
pub struct GpuPerformanceStats {
    /// Per-device statistics
    pub device_stats: HashMap<i32, DeviceStats>,
    /// Overall GPU utilization
    pub overall_utilization: f32,
    /// Memory efficiency
    pub memory_efficiency: f32,
    /// Throughput metrics
    pub throughput: ThroughputMetrics,
    /// Error statistics
    pub error_stats: ErrorStatistics,
    /// Performance trends
    pub performance_trends: PerformanceTrends,
}

/// Per-device performance statistics
#[derive(Debug, Clone, Default)]
pub struct DeviceStats {
    /// Device utilization percentage
    pub utilization: f32,
    /// Memory usage
    pub memory_usage: MemoryUsageStats,
    /// Compute performance
    pub compute_performance: ComputePerformanceStats,
    /// Power consumption (watts)
    pub power_consumption: f32,
    /// Temperature (Celsius)
    pub temperature: f32,
}

/// Memory usage statistics
#[derive(Debug, Clone, Default)]
pub struct MemoryUsageStats {
    /// Total memory
    pub total_memory: usize,
    /// Used memory
    pub used_memory: usize,
    /// Free memory
    pub free_memory: usize,
    /// Peak usage
    pub peak_usage: usize,
    /// Fragmentation percentage
    pub fragmentation: f32,
}

/// Compute performance statistics
#[derive(Debug, Clone, Default)]
pub struct ComputePerformanceStats {
    /// FLOPS (floating point operations per second)
    pub flops: f64,
    /// Memory bandwidth utilization
    pub memory_bandwidth_utilization: f32,
    /// Kernel efficiency
    pub kernel_efficiency: f32,
    /// Occupancy percentage
    pub occupancy: f32,
}

/// Throughput metrics
#[derive(Debug, Clone, Default)]
pub struct ThroughputMetrics {
    /// Vectors processed per second
    pub vectors_per_second: f64,
    /// Operations per second
    pub operations_per_second: f64,
    /// Data transfer rate (MB/s)
    pub transfer_rate_mbps: f64,
    /// Search queries per second
    pub search_qps: f64,
}

/// Error statistics
#[derive(Debug, Clone, Default)]
pub struct ErrorStatistics {
    /// Total errors
    pub total_errors: usize,
    /// Recoverable errors
    pub recoverable_errors: usize,
    /// Fatal errors
    pub fatal_errors: usize,
    /// Error rate (errors per operation)
    pub error_rate: f32,
    /// Recovery success rate
    pub recovery_rate: f32,
}

/// Performance trends
#[derive(Debug, Clone, Default)]
pub struct PerformanceTrends {
    /// Utilization trend over time
    pub utilization_trend: Vec<(Instant, f32)>,
    /// Throughput trend over time
    pub throughput_trend: Vec<(Instant, f64)>,
    /// Memory usage trend
    pub memory_trend: Vec<(Instant, usize)>,
    /// Error rate trend
    pub error_trend: Vec<(Instant, f32)>,
}

/// Allocation statistics
#[derive(Debug, Clone, Default)]
pub struct AllocationStatistics {
    /// Total allocations
    pub total_allocations: usize,
    /// Total deallocations
    pub total_deallocations: usize,
    /// Peak memory usage
    pub peak_usage: usize,
    /// Average allocation size
    pub avg_allocation_size: usize,
    /// Fragmentation events
    pub fragmentation_events: usize,
    /// Out of memory events
    pub oom_events: usize,
}

/// Cached result for performance optimization
#[derive(Debug)]
pub struct CachedResult {
    /// Result data
    pub data: GpuResultData,
    /// Cache timestamp
    pub timestamp: Instant,
    /// Hit count
    pub hit_count: AtomicUsize,
    /// Result size
    pub size: usize,
}

impl Clone for CachedResult {
    fn clone(&self) -> Self {
        Self {
            data: self.data.clone(),
            timestamp: self.timestamp,
            hit_count: AtomicUsize::new(self.hit_count.load(Ordering::Acquire)),
            size: self.size,
        }
    }
}

/// GPU load balancer for multi-GPU operations
#[derive(Debug)]
pub struct GpuLoadBalancer {
    /// Device utilization tracking
    pub device_utilization: HashMap<i32, f32>,
    /// Current workload distribution
    pub workload_distribution: HashMap<i32, usize>,
    /// Load balancing strategy
    pub strategy: LoadBalancingStrategy,
    /// Performance history for decisions
    pub performance_history: HashMap<i32, VecDeque<PerformanceSnapshot>>,
}

/// Load balancing strategies
#[derive(Debug, Clone)]
pub enum LoadBalancingStrategy {
    /// Round-robin distribution
    RoundRobin,
    /// Load-based distribution
    LoadBased,
    /// Performance-based distribution
    PerformanceBased,
    /// Memory-aware distribution
    MemoryAware,
    /// Hybrid strategy
    Hybrid,
}

/// Performance snapshot for load balancing decisions
#[derive(Debug, Clone)]
pub struct PerformanceSnapshot {
    /// Timestamp
    pub timestamp: Instant,
    /// Utilization percentage
    pub utilization: f32,
    /// Memory usage percentage
    pub memory_usage: f32,
    /// Operations per second
    pub ops_per_second: f64,
    /// Average latency
    pub avg_latency: Duration,
}

impl FaissGpuIndex {
    /// Create a new GPU-accelerated FAISS index
    pub async fn new(faiss_config: FaissConfig, gpu_config: FaissGpuConfig) -> Result<Self> {
        let span = span!(Level::INFO, "faiss_gpu_index_new");
        let _enter = span.enter();

        // Convert FaissGpuConfig to GpuConfig for the accelerator
        let _base_gpu_config = crate::gpu::GpuConfig {
            device_id: gpu_config.device_ids.first().copied().unwrap_or(0),
            enable_mixed_precision: true,
            enable_tensor_cores: true,
            batch_size: 1024,
            memory_pool_size: gpu_config.memory_per_device,
            stream_count: gpu_config.stream_config.streams_per_device,
            enable_peer_access: gpu_config.enable_multi_gpu,
            enable_unified_memory: matches!(gpu_config.memory_strategy, GpuMemoryStrategy::Unified),
            enable_async_execution: true,
            enable_multi_gpu: gpu_config.enable_multi_gpu,
            preferred_gpu_ids: gpu_config.device_ids.clone(),
            dynamic_batch_sizing: true,
            enable_memory_compression: false,
            kernel_cache_size: 1024 * 1024,
            optimization_level: crate::gpu::OptimizationLevel::Performance,
            precision_mode: crate::gpu::PrecisionMode::Mixed,
        };

        // Initialize GPU execution configuration
        let gpu_runtime = Arc::new(GpuExecutionConfig::default());

        // Initialize memory pools for each device
        let mut memory_pools = HashMap::new();
        for &device_id in &gpu_config.device_ids {
            let pool = FaissGpuMemoryPool::new(device_id, gpu_config.memory_per_device)?;
            memory_pools.insert(device_id, pool);
        }

        // Initialize compute streams
        let mut compute_streams = HashMap::new();
        for &device_id in &gpu_config.device_ids {
            let streams = Self::create_compute_streams(device_id, &gpu_config.stream_config)?;
            compute_streams.insert(device_id, streams);
        }

        // Initialize load balancer
        let load_balancer =
            GpuLoadBalancer::new(&gpu_config.device_ids, LoadBalancingStrategy::Hybrid);

        let device_count = gpu_config.device_ids.len();
        let index = Self {
            faiss_config,
            gpu_config,
            gpu_runtime,
            memory_pools: Arc::new(RwLock::new(memory_pools)),
            compute_streams: Arc::new(RwLock::new(compute_streams)),
            stats: Arc::new(RwLock::new(GpuPerformanceStats::default())),
            work_queue: Arc::new(Mutex::new(VecDeque::new())),
            results_cache: Arc::new(RwLock::new(HashMap::new())),
            load_balancer: Arc::new(RwLock::new(load_balancer)),
        };

        // Start background worker tasks
        index.start_background_workers().await?;

        info!(
            "Created GPU-accelerated FAISS index with {} devices",
            device_count
        );
        Ok(index)
    }

    /// Create compute streams for a device
    fn create_compute_streams(
        device_id: i32,
        stream_config: &GpuStreamConfig,
    ) -> Result<Vec<GpuComputeStream>> {
        let mut streams = Vec::new();

        for i in 0..stream_config.streams_per_device {
            let priority = stream_config
                .priority_levels
                .get(i % stream_config.priority_levels.len())
                .copied()
                .unwrap_or(0);

            let stream = GpuComputeStream {
                stream_id: i,
                device_id,
                stream_handle: device_id as usize * 1000 + i, // Simulated handle
                priority,
                current_operation: Arc::new(Mutex::new(None)),
                operation_history: Arc::new(RwLock::new(VecDeque::new())),
                utilization: Arc::new(RwLock::new(StreamUtilization::default())),
            };

            streams.push(stream);
        }

        Ok(streams)
    }

    /// Start background worker tasks
    async fn start_background_workers(&self) -> Result<()> {
        let span = span!(Level::DEBUG, "start_background_workers");
        let _enter = span.enter();

        // Start operation processor
        self.start_operation_processor().await?;

        // Start performance monitor
        self.start_performance_monitor().await?;

        // Start memory manager
        self.start_memory_manager().await?;

        // Start load balancer
        if self.gpu_config.enable_multi_gpu {
            self.start_load_balancer().await?;
        }

        debug!("Started background worker tasks");
        Ok(())
    }

    /// Start operation processor task
    async fn start_operation_processor(&self) -> Result<()> {
        let work_queue = Arc::clone(&self.work_queue);
        let compute_streams = Arc::clone(&self.compute_streams);
        let stats = Arc::clone(&self.stats);
        let gpu_config = self.gpu_config.clone();

        tokio::spawn(async move {
            loop {
                // Process pending operations
                if let Some(operation) = {
                    let mut queue = work_queue.lock().expect("lock poisoned");
                    queue.pop_front()
                } {
                    if let Err(e) = Self::process_gpu_operation(
                        operation,
                        &compute_streams,
                        &stats,
                        &gpu_config,
                    )
                    .await
                    {
                        error!("Failed to process GPU operation: {}", e);
                    }
                }

                // Sleep briefly to avoid busy waiting
                tokio::time::sleep(tokio::time::Duration::from_millis(1)).await;
            }
        });

        Ok(())
    }

    /// Process a single GPU operation
    async fn process_gpu_operation(
        mut operation: GpuOperation,
        compute_streams: &Arc<RwLock<HashMap<i32, Vec<GpuComputeStream>>>>,
        stats: &Arc<RwLock<GpuPerformanceStats>>,
        gpu_config: &FaissGpuConfig,
    ) -> Result<()> {
        let start_time = Instant::now();

        // Select optimal device and stream
        let (device_id, stream_id) =
            Self::select_optimal_stream(compute_streams, &operation).await?;

        // Extract result_sender before executing operation
        let result_sender = operation.result_sender.take();

        // Execute operation
        let result =
            Self::execute_operation_on_device(operation, device_id, stream_id, gpu_config).await?;

        // Send result back if callback provided
        if let Some(sender) = result_sender {
            let _ = sender.send(result.clone());
        }

        // Update statistics
        Self::update_operation_stats(stats, &result, start_time.elapsed()).await?;

        Ok(())
    }

    /// Select optimal device and stream for operation
    async fn select_optimal_stream(
        compute_streams: &Arc<RwLock<HashMap<i32, Vec<GpuComputeStream>>>>,
        _operation: &GpuOperation,
    ) -> Result<(i32, usize)> {
        let streams = compute_streams.read().expect("lock poisoned");

        // Simple strategy: find device with lowest utilization
        let mut best_device = 0;
        let mut best_stream = 0;
        let mut lowest_utilization = f32::MAX;

        for (&device_id, device_streams) in streams.iter() {
            for (stream_id, stream) in device_streams.iter().enumerate() {
                let utilization = stream
                    .utilization
                    .read()
                    .expect("lock poisoned")
                    .utilization_percentage;
                if utilization < lowest_utilization {
                    lowest_utilization = utilization;
                    best_device = device_id;
                    best_stream = stream_id;
                }
            }
        }

        Ok((best_device, best_stream))
    }

    /// Execute operation on specific device
    async fn execute_operation_on_device(
        operation: GpuOperation,
        _device_id: i32,
        _stream_id: usize,
        _gpu_config: &FaissGpuConfig,
    ) -> Result<GpuOperationResult> {
        let start_time = Instant::now();

        // Simulate GPU operation execution
        let result_data = match &operation.operation_type {
            GpuOperationType::Search {
                query_vectors, k, ..
            } => {
                // Simulate GPU-accelerated search
                tokio::time::sleep(tokio::time::Duration::from_millis(10)).await;

                let mut results = Vec::new();
                for _query in query_vectors {
                    let mut query_results = Vec::new();
                    for i in 0..*k {
                        query_results.push((format!("gpu_result_{i}"), 0.95 - (i as f32 * 0.05)));
                    }
                    results.push(query_results);
                }

                GpuResultData::SearchResults(results)
            }
            GpuOperationType::Add { vectors: _, .. } => {
                // Simulate GPU-accelerated vector addition
                tokio::time::sleep(tokio::time::Duration::from_millis(5)).await;
                GpuResultData::AdditionComplete
            }
            GpuOperationType::Train { .. } => {
                // Simulate GPU-accelerated training
                tokio::time::sleep(tokio::time::Duration::from_millis(100)).await;
                GpuResultData::TrainingComplete
            }
            GpuOperationType::Optimize => {
                // Simulate GPU optimization
                tokio::time::sleep(tokio::time::Duration::from_millis(20)).await;
                let mut metrics = HashMap::new();
                metrics.insert("optimization_improvement".to_string(), 15.0);
                metrics.insert("memory_efficiency".to_string(), 92.0);
                GpuResultData::OptimizationMetrics(metrics)
            }
            GpuOperationType::MemoryTransfer { size, .. } => {
                // Simulate memory transfer
                let transfer_time = *size as f64 / (10.0 * 1024.0 * 1024.0 * 1024.0); // 10 GB/s
                tokio::time::sleep(tokio::time::Duration::from_secs_f64(transfer_time)).await;
                GpuResultData::TransferComplete
            }
        };

        Ok(GpuOperationResult {
            operation_id: operation.id,
            success: true,
            result_data,
            execution_time: start_time.elapsed(),
            memory_used: 1024 * 1024, // Simulated 1MB
            error_message: None,
        })
    }

    /// Update operation statistics
    async fn update_operation_stats(
        stats: &Arc<RwLock<GpuPerformanceStats>>,
        result: &GpuOperationResult,
        execution_time: Duration,
    ) -> Result<()> {
        let mut stats = stats.write().expect("lock poisoned");

        // Update throughput metrics
        stats.throughput.operations_per_second += 1.0 / execution_time.as_secs_f64();

        // Update error statistics if needed
        if !result.success {
            stats.error_stats.total_errors += 1;
        }

        Ok(())
    }

    /// Start performance monitoring task
    async fn start_performance_monitor(&self) -> Result<()> {
        let stats = Arc::clone(&self.stats);
        let device_ids = self.gpu_config.device_ids.clone();

        tokio::spawn(async move {
            let mut interval = tokio::time::interval(tokio::time::Duration::from_secs(1));

            loop {
                interval.tick().await;

                // Collect performance metrics from all devices
                if let Err(e) = Self::collect_performance_metrics(&stats, &device_ids).await {
                    warn!("Failed to collect performance metrics: {}", e);
                }
            }
        });

        Ok(())
    }

    /// Collect performance metrics from devices
    async fn collect_performance_metrics(
        stats: &Arc<RwLock<GpuPerformanceStats>>,
        device_ids: &[i32],
    ) -> Result<()> {
        let mut stats = stats.write().expect("lock poisoned");

        for &device_id in device_ids {
            // Simulate GPU metrics collection
            let device_stats = DeviceStats {
                utilization: 75.0 + (device_id as f32 * 5.0) % 25.0, // Simulated
                memory_usage: MemoryUsageStats {
                    total_memory: 8 * 1024 * 1024 * 1024, // 8GB
                    used_memory: 6 * 1024 * 1024 * 1024,  // 6GB
                    free_memory: 2 * 1024 * 1024 * 1024,  // 2GB
                    peak_usage: 7 * 1024 * 1024 * 1024,   // 7GB
                    fragmentation: 5.0,
                },
                compute_performance: ComputePerformanceStats {
                    flops: 15.5e12, // 15.5 TFLOPS
                    memory_bandwidth_utilization: 80.0,
                    kernel_efficiency: 85.0,
                    occupancy: 75.0,
                },
                power_consumption: 250.0, // Watts
                temperature: 70.0,        // Celsius
            };

            stats.device_stats.insert(device_id, device_stats);
        }

        // Calculate overall utilization
        stats.overall_utilization = stats
            .device_stats
            .values()
            .map(|s| s.utilization)
            .sum::<f32>()
            / stats.device_stats.len() as f32;

        Ok(())
    }

    /// Start memory management task
    async fn start_memory_manager(&self) -> Result<()> {
        let memory_pools = Arc::clone(&self.memory_pools);
        let gpu_config = self.gpu_config.clone();

        tokio::spawn(async move {
            let mut interval = tokio::time::interval(tokio::time::Duration::from_secs(5));

            loop {
                interval.tick().await;

                // Perform memory cleanup and optimization
                if let Err(e) = Self::manage_gpu_memory(&memory_pools, &gpu_config).await {
                    warn!("Failed to manage GPU memory: {}", e);
                }
            }
        });

        Ok(())
    }

    /// Manage GPU memory pools
    async fn manage_gpu_memory(
        memory_pools: &Arc<RwLock<HashMap<i32, FaissGpuMemoryPool>>>,
        _gpu_config: &FaissGpuConfig,
    ) -> Result<()> {
        let pools = memory_pools.read().expect("lock poisoned");

        for (device_id, pool) in pools.iter() {
            // Check for memory fragmentation
            let fragmentation = pool.calculate_fragmentation();
            if fragmentation > 20.0 {
                debug!(
                    "High fragmentation detected on device {}: {:.1}%",
                    device_id, fragmentation
                );
                // Trigger defragmentation if needed
            }

            // Check for memory leaks
            let allocated_blocks = pool.allocated_blocks.read().expect("lock poisoned");
            let now = Instant::now();
            for (_, block) in allocated_blocks.iter() {
                if now.duration_since(block.allocated_at) > Duration::from_secs(3600) {
                    warn!(
                        "Potential memory leak detected on device {}: block allocated {} ago",
                        device_id,
                        humantime::format_duration(now.duration_since(block.allocated_at))
                    );
                }
            }
        }

        Ok(())
    }

    /// Start load balancer task
    async fn start_load_balancer(&self) -> Result<()> {
        let load_balancer = Arc::clone(&self.load_balancer);
        let stats = Arc::clone(&self.stats);

        tokio::spawn(async move {
            let mut interval = tokio::time::interval(tokio::time::Duration::from_secs(2));

            loop {
                interval.tick().await;

                // Update load balancing decisions
                if let Err(e) = Self::update_load_balancing(&load_balancer, &stats).await {
                    warn!("Failed to update load balancing: {}", e);
                }
            }
        });

        Ok(())
    }

    /// Update load balancing strategy
    async fn update_load_balancing(
        load_balancer: &Arc<RwLock<GpuLoadBalancer>>,
        stats: &Arc<RwLock<GpuPerformanceStats>>,
    ) -> Result<()> {
        let stats = stats.read().expect("lock poisoned");
        let mut balancer = load_balancer.write().expect("lock poisoned");

        // Update device utilization from stats
        for (&device_id, device_stats) in &stats.device_stats {
            balancer
                .device_utilization
                .insert(device_id, device_stats.utilization);

            // Add performance snapshot
            let snapshot = PerformanceSnapshot {
                timestamp: Instant::now(),
                utilization: device_stats.utilization,
                memory_usage: device_stats.memory_usage.used_memory as f32
                    / device_stats.memory_usage.total_memory as f32
                    * 100.0,
                ops_per_second: 1000.0, // Simulated
                avg_latency: Duration::from_micros(250),
            };

            balancer
                .performance_history
                .entry(device_id)
                .or_default()
                .push_back(snapshot);

            // Keep only recent history
            if balancer.performance_history[&device_id].len() > 100 {
                balancer
                    .performance_history
                    .get_mut(&device_id)
                    .expect("device_id should exist in performance_history")
                    .pop_front();
            }
        }

        Ok(())
    }

    /// Perform GPU-accelerated search
    pub async fn search_gpu(
        &self,
        query_vectors: Vec<Vec<f32>>,
        k: usize,
        search_params: FaissSearchParams,
    ) -> Result<Vec<Vec<(String, f32)>>> {
        let span = span!(Level::DEBUG, "search_gpu");
        let _enter = span.enter();

        // Create GPU operation
        let (result_sender, result_receiver) = oneshot::channel();
        let operation = GpuOperation {
            id: uuid::Uuid::new_v4().to_string(),
            operation_type: GpuOperationType::Search {
                query_vectors: query_vectors.clone(),
                k,
                search_params,
            },
            input_data: GpuOperationData::Vectors(query_vectors),
            output_size: k * std::mem::size_of::<(String, f32)>(),
            priority: 1,
            timeout: Some(Duration::from_secs(30)),
            result_sender: Some(result_sender),
        };

        // Queue operation
        {
            let mut queue = self.work_queue.lock().expect("lock poisoned");
            queue.push_back(operation);
        }

        // Wait for result
        let result = result_receiver
            .await
            .map_err(|_| AnyhowError::msg("GPU operation timeout"))?;

        if !result.success {
            return Err(AnyhowError::msg(
                result
                    .error_message
                    .unwrap_or_else(|| "GPU operation failed".to_string()),
            ));
        }

        match result.result_data {
            GpuResultData::SearchResults(results) => Ok(results),
            _ => Err(AnyhowError::msg("Unexpected result type")),
        }
    }

    /// Add vectors with GPU acceleration
    pub async fn add_vectors_gpu(&self, vectors: Vec<Vec<f32>>, ids: Vec<String>) -> Result<()> {
        let span = span!(Level::DEBUG, "add_vectors_gpu");
        let _enter = span.enter();

        let (result_sender, result_receiver) = oneshot::channel();
        let operation = GpuOperation {
            id: uuid::Uuid::new_v4().to_string(),
            operation_type: GpuOperationType::Add {
                vectors: vectors.clone(),
                ids,
            },
            input_data: GpuOperationData::Vectors(vectors),
            output_size: 0,
            priority: 2,
            timeout: Some(Duration::from_secs(60)),
            result_sender: Some(result_sender),
        };

        {
            let mut queue = self.work_queue.lock().expect("lock poisoned");
            queue.push_back(operation);
        }

        let result = result_receiver
            .await
            .map_err(|_| AnyhowError::msg("GPU operation timeout"))?;

        if !result.success {
            return Err(AnyhowError::msg(
                result
                    .error_message
                    .unwrap_or_else(|| "GPU operation failed".to_string()),
            ));
        }

        Ok(())
    }

    /// Get GPU performance statistics
    pub fn get_gpu_stats(&self) -> Result<GpuPerformanceStats> {
        let stats = self.stats.read().expect("lock poisoned");
        Ok(stats.clone())
    }

    /// Optimize GPU performance
    pub async fn optimize_gpu_performance(&self) -> Result<HashMap<String, f64>> {
        let span = span!(Level::INFO, "optimize_gpu_performance");
        let _enter = span.enter();

        let (result_sender, result_receiver) = oneshot::channel();
        let operation = GpuOperation {
            id: uuid::Uuid::new_v4().to_string(),
            operation_type: GpuOperationType::Optimize,
            input_data: GpuOperationData::Empty,
            output_size: 0,
            priority: 0, // High priority
            timeout: Some(Duration::from_secs(120)),
            result_sender: Some(result_sender),
        };

        {
            let mut queue = self.work_queue.lock().expect("lock poisoned");
            queue.push_back(operation);
        }

        let result = result_receiver
            .await
            .map_err(|_| AnyhowError::msg("GPU optimization timeout"))?;

        if !result.success {
            return Err(AnyhowError::msg("GPU optimization failed"));
        }

        match result.result_data {
            GpuResultData::OptimizationMetrics(metrics) => Ok(metrics),
            _ => Err(AnyhowError::msg("Unexpected result type")),
        }
    }
}

impl FaissGpuMemoryPool {
    /// Create a new GPU memory pool
    pub fn new(device_id: i32, total_size: usize) -> Result<Self> {
        Ok(Self {
            device_id,
            total_size,
            allocated_size: AtomicUsize::new(0),
            free_blocks: Arc::new(Mutex::new(BTreeMap::new())),
            allocated_blocks: Arc::new(RwLock::new(HashMap::new())),
            allocation_stats: Arc::new(RwLock::new(AllocationStatistics::default())),
        })
    }

    /// Allocate memory block
    pub fn allocate(&self, size: usize, block_type: MemoryBlockType) -> Result<GpuMemoryBlock> {
        let aligned_size = (size + 255) & !255; // 256-byte alignment

        if self.allocated_size.load(Ordering::Relaxed) + aligned_size > self.total_size {
            return Err(AnyhowError::msg("Out of GPU memory"));
        }

        let block = GpuMemoryBlock {
            gpu_address: self.allocated_size.load(Ordering::Relaxed), // Simulated address
            size: aligned_size,
            allocated_at: Instant::now(),
            ref_count: AtomicUsize::new(1),
            block_type,
        };

        self.allocated_size
            .fetch_add(aligned_size, Ordering::Relaxed);

        // Update statistics
        {
            let mut stats = self.allocation_stats.write().expect("lock poisoned");
            stats.total_allocations += 1;
            let current_usage = self.allocated_size.load(Ordering::Relaxed);
            if current_usage > stats.peak_usage {
                stats.peak_usage = current_usage;
            }
        }

        Ok(block)
    }

    /// Deallocate memory block
    pub fn deallocate(&self, block: &GpuMemoryBlock) -> Result<()> {
        self.allocated_size.fetch_sub(block.size, Ordering::Relaxed);

        {
            let mut stats = self.allocation_stats.write().expect("lock poisoned");
            stats.total_deallocations += 1;
        }

        Ok(())
    }

    /// Calculate memory fragmentation percentage
    pub fn calculate_fragmentation(&self) -> f32 {
        // Simplified fragmentation calculation
        let allocated = self.allocated_size.load(Ordering::Relaxed);
        let free_blocks = self.free_blocks.lock().expect("lock poisoned");
        let num_free_blocks = free_blocks.len();

        if allocated == 0 {
            return 0.0;
        }

        (num_free_blocks as f32 / (allocated / 1024) as f32) * 100.0
    }
}

impl GpuLoadBalancer {
    /// Create a new GPU load balancer
    pub fn new(device_ids: &[i32], strategy: LoadBalancingStrategy) -> Self {
        let mut device_utilization = HashMap::new();
        let mut workload_distribution = HashMap::new();
        let mut performance_history = HashMap::new();

        for &device_id in device_ids {
            device_utilization.insert(device_id, 0.0);
            workload_distribution.insert(device_id, 0);
            performance_history.insert(device_id, VecDeque::new());
        }

        Self {
            device_utilization,
            workload_distribution,
            strategy,
            performance_history,
        }
    }

    /// Select optimal device for operation
    pub fn select_device(&self, operation: &GpuOperation) -> i32 {
        match self.strategy {
            LoadBalancingStrategy::RoundRobin => self.select_round_robin(),
            LoadBalancingStrategy::LoadBased => self.select_load_based(),
            LoadBalancingStrategy::PerformanceBased => self.select_performance_based(),
            LoadBalancingStrategy::MemoryAware => self.select_memory_aware(),
            LoadBalancingStrategy::Hybrid => self.select_hybrid(operation),
        }
    }

    fn select_round_robin(&self) -> i32 {
        // Simple round-robin selection
        let total_workload: usize = self.workload_distribution.values().sum();
        let device_count = self.device_utilization.len();
        let target_device_index = total_workload % device_count;

        *self
            .device_utilization
            .keys()
            .nth(target_device_index)
            .unwrap_or(&0)
    }

    fn select_load_based(&self) -> i32 {
        // Select device with lowest utilization
        self.device_utilization
            .iter()
            .min_by(|a, b| a.1.partial_cmp(b.1).unwrap_or(std::cmp::Ordering::Equal))
            .map(|(&device_id, _)| device_id)
            .unwrap_or(0)
    }

    fn select_performance_based(&self) -> i32 {
        // Select device with best recent performance
        let mut best_device = 0;
        let mut best_score = f64::MIN;

        for (&device_id, history) in &self.performance_history {
            if let Some(recent_snapshot) = history.back() {
                let score = recent_snapshot.ops_per_second
                    / (recent_snapshot.avg_latency.as_secs_f64() + 1e-6);
                if score > best_score {
                    best_score = score;
                    best_device = device_id;
                }
            }
        }

        best_device
    }

    fn select_memory_aware(&self) -> i32 {
        // Select device with most available memory (simplified)
        self.device_utilization
            .iter()
            .min_by(|a, b| a.1.partial_cmp(b.1).unwrap_or(std::cmp::Ordering::Equal))
            .map(|(&device_id, _)| device_id)
            .unwrap_or(0)
    }

    fn select_hybrid(&self, operation: &GpuOperation) -> i32 {
        // Combine multiple factors for selection
        match &operation.operation_type {
            GpuOperationType::Search { .. } => self.select_performance_based(),
            GpuOperationType::Add { .. } => self.select_memory_aware(),
            GpuOperationType::Train { .. } => self.select_load_based(),
            _ => self.select_round_robin(),
        }
    }
}

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

    #[tokio::test]
    async fn test_faiss_gpu_index_creation() {
        let faiss_config = FaissConfig::default();
        let gpu_config = FaissGpuConfig::default();

        let result = FaissGpuIndex::new(faiss_config, gpu_config).await;
        assert!(result.is_ok());
    }

    #[test]
    fn test_gpu_memory_pool() -> Result<()> {
        let pool = FaissGpuMemoryPool::new(0, 1024 * 1024)?; // 1MB pool

        let block = pool.allocate(1024, MemoryBlockType::Vectors)?;
        assert_eq!(block.size, 1024);

        pool.deallocate(&block)?;
        assert_eq!(pool.allocated_size.load(Ordering::Relaxed), 0);
        Ok(())
    }

    #[test]
    fn test_gpu_load_balancer() {
        let device_ids = vec![0, 1, 2];
        let balancer = GpuLoadBalancer::new(&device_ids, LoadBalancingStrategy::RoundRobin);

        assert_eq!(balancer.device_utilization.len(), 3);

        let operation = GpuOperation {
            id: "test".to_string(),
            operation_type: GpuOperationType::Optimize,
            input_data: GpuOperationData::Empty,
            output_size: 0,
            priority: 0,
            timeout: None,
            result_sender: None,
        };

        let selected_device = balancer.select_device(&operation);
        assert!(device_ids.contains(&selected_device));
    }
}