scirs2_linalg/gpu/

acceleration.rs

//! Advanced MODE: Advanced GPU Acceleration Framework
//!
//! This module provides a comprehensive GPU acceleration framework that automatically
//! selects the optimal GPU backend and kernel for any given linear algebra operation.
//! It includes performance profiling, automatic tuning, and adaptive algorithm selection.

use super::{
    operations::{AdvancedGpuOperations, GpuKernelManager, GpuOperationDispatcher},
    GpuBackendManager, GpuContext, GpuDeviceType, GpuLinalgOps,
};
use crate::error::{LinalgError, LinalgResult};
use scirs2_core::ndarray::{Array1, Array2, ArrayView1, ArrayView2};
use scirs2_core::numeric::{Float, NumAssign, Zero};
use std::collections::HashMap;
use std::fmt::Debug;
use std::sync::{Arc, Mutex};

/// High-level GPU acceleration coordinator
pub struct GpuAccelerationFramework<T>
where
    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
{
    /// GPU backend manager
    backend_manager: Arc<Mutex<GpuBackendManager>>,
    /// Operation dispatcher
    dispatcher: GpuOperationDispatcher<T>,
    /// Advanced operations handler
    advanced_ops: AdvancedGpuOperations<T>,
    /// Kernel manager
    kernel_manager: Arc<Mutex<GpuKernelManager>>,
    /// Active GPU contexts
    contexts: HashMap<String, Arc<dyn GpuContext>>,
    /// Performance profiler
    profiler: GpuPerformanceProfiler,
    /// Configuration
    config: AccelerationConfig,
}

/// Configuration for GPU acceleration
#[derive(Debug, Clone)]
pub struct AccelerationConfig {
    /// Minimum problem size for GPU acceleration
    pub min_gpusize: usize,
    /// Maximum memory usage per operation (in bytes)
    pub max_memory_per_op: usize,
    /// Enable automatic kernel selection
    pub auto_kernel_selection: bool,
    /// Enable performance profiling
    pub enable_profiling: bool,
    /// Enable adaptive batching
    pub adaptive_batching: bool,
    /// Preferred device types (in order of preference)
    pub preferred_devices: Vec<GpuDeviceType>,
    /// Enable mixed precision when available
    pub mixed_precision: bool,
    /// Enable tensor core usage when available
    pub tensor_cores: bool,
}

impl Default for AccelerationConfig {
    fn default() -> Self {
        #[cfg(target_pointer_width = "32")]
        let max_memory_per_op = 512 * 1024 * 1024; // 512MB for 32-bit
        #[cfg(target_pointer_width = "64")]
        let max_memory_per_op = 2usize * 1024 * 1024 * 1024; // 2GB for 64-bit

        Self {
            min_gpusize: 50_000,
            max_memory_per_op,
            auto_kernel_selection: true,
            enable_profiling: true,
            adaptive_batching: true,
            preferred_devices: vec![
                GpuDeviceType::Cuda,
                GpuDeviceType::OpenCl,
                GpuDeviceType::Metal,
                GpuDeviceType::Vulkan,
            ],
            mixed_precision: true,
            tensor_cores: true,
        }
    }
}

/// Performance profiler for GPU operations
#[derive(Debug, Default)]
pub struct GpuPerformanceProfiler {
    /// Performance measurements per operation
    measurements: HashMap<String, Vec<PerformanceMeasurement>>,
    /// Total operations performed
    total_operations: usize,
    /// Total GPU time
    total_gpu_time: f64,
    /// Total CPU time
    total_cpu_time: f64,
}

/// Individual performance measurement
#[derive(Debug, Clone)]
pub struct PerformanceMeasurement {
    /// Operation name
    pub operation: String,
    /// Problem size
    pub problemsize: usize,
    /// Execution time in seconds
    pub execution_time: f64,
    /// Memory usage in bytes
    pub memory_usage: usize,
    /// Device used
    pub device_type: GpuDeviceType,
    /// GFLOPS achieved
    pub gflops: f64,
    /// Memory bandwidth utilization
    pub memory_bandwidth_util: f64,
}

impl<T> GpuAccelerationFramework<T>
where
    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
{
    /// Create a new GPU acceleration framework
    pub fn new() -> LinalgResult<Self> {
        Self::with_config(AccelerationConfig::default())
    }

    /// Create framework with custom configuration
    pub fn with_config(config: AccelerationConfig) -> LinalgResult<Self> {
        let backend_manager = Arc::new(Mutex::new(super::initialize_gpu_manager()?));
        let dispatcher = GpuOperationDispatcher::with_threshold(config.min_gpusize);
        let advanced_ops = AdvancedGpuOperations::new();
        let kernel_manager = Arc::new(Mutex::new(GpuKernelManager::new()));

        Ok(Self {
            backend_manager,
            dispatcher,
            advanced_ops,
            kernel_manager,
            contexts: HashMap::new(),
            profiler: GpuPerformanceProfiler::default(),
            config,
        })
    }

    /// Initialize GPU contexts for all available devices
    pub fn initialize_contexts(&mut self) -> LinalgResult<()> {
        let manager = self.backend_manager.lock().expect("Operation failed");
        let devices = manager.list_all_devices()?;

        for (backend_name, device_list) in devices {
            if let Some(backend) = manager.get_backend(&backend_name) {
                for (device_id, device_info) in device_list.iter().enumerate() {
                    // Check if this device type is in our preferences
                    if self
                        .config
                        .preferred_devices
                        .contains(&device_info.device_type)
                    {
                        if let Ok(context) = backend.create_context(device_id) {
                            let context_key = format!("{}_{}", backend_name, device_id);
                            self.contexts.insert(context_key, Arc::from(context));
                        }
                    }
                }
            }
        }

        Ok(())
    }

    /// Automatically accelerated matrix-vector multiplication
    pub fn accelerated_matvec(
        &mut self,
        a: &ArrayView2<T>,
        x: &ArrayView1<T>,
    ) -> LinalgResult<Array1<T>> {
        let operation_name = "matvec";
        let problemsize = a.len() + x.len();

        // Select optimal execution strategy
        let strategy = self.select_execution_strategy(operation_name, problemsize)?;

        let start_time = std::time::Instant::now();

        let result = match strategy {
            ExecutionStrategy::Cpu => self.dispatcher.cpu_matvec(a, x),
            ExecutionStrategy::Gpu { ref context, .. } => {
                self.dispatcher.gpu_matvec(context.as_ref(), a, x)
            }
            ExecutionStrategy::MultiGpu {
                ref primary_context,
                ..
            } => {
                // For now, use primary GPU
                self.dispatcher.gpu_matvec(primary_context.as_ref(), a, x)
            }
        };

        let execution_time = start_time.elapsed().as_secs_f64();

        // Record performance if enabled
        if self.config.enable_profiling {
            self.record_performance(operation_name, problemsize, execution_time, &strategy);
        }

        result
    }

    /// Automatically accelerated matrix-matrix multiplication
    pub fn accelerated_matmul(
        &mut self,
        a: &ArrayView2<T>,
        b: &ArrayView2<T>,
    ) -> LinalgResult<Array2<T>> {
        let operation_name = "matmul";
        let problemsize = a.len() + b.len();

        let strategy = self.select_execution_strategy(operation_name, problemsize)?;
        let start_time = std::time::Instant::now();

        let result = match strategy {
            ExecutionStrategy::Cpu => self.dispatcher.cpu_matmul(a, b),
            ExecutionStrategy::Gpu { ref context, .. } => {
                self.dispatcher.gpu_matmul(context.as_ref(), a, b)
            }
            ExecutionStrategy::MultiGpu {
                ref primary_context,
                ..
            } => {
                // For large matrices, could implement multi-GPU GEMM
                self.dispatcher.gpu_matmul(primary_context.as_ref(), a, b)
            }
        };

        let execution_time = start_time.elapsed().as_secs_f64();

        if self.config.enable_profiling {
            self.record_performance(operation_name, problemsize, execution_time, &strategy);
        }

        result
    }

    /// Accelerated batch operations
    pub fn accelerated_batch_matmul(
        &mut self,
        matrices_a: &[ArrayView2<T>],
        matrices_b: &[ArrayView2<T>],
    ) -> LinalgResult<Vec<Array2<T>>> {
        if self.config.adaptive_batching && matrices_a.len() > 1 {
            // Use advanced batched operations
            self.advanced_ops
                .batched_matmul_optimized(matrices_a, matrices_b)
        } else {
            // Process individually
            matrices_a
                .iter()
                .zip(matrices_b.iter())
                .map(|(a, b)| self.accelerated_matmul(a, b))
                .collect()
        }
    }

    /// Select optimal execution strategy
    fn select_execution_strategy(
        &self,
        operation: &str,
        problemsize: usize,
    ) -> LinalgResult<ExecutionStrategy> {
        // Check if problem is large enough for GPU
        if problemsize < self.config.min_gpusize {
            return Ok(ExecutionStrategy::Cpu);
        }

        // Find best available GPU context
        let best_context = self.select_best_context(operation, problemsize)?;

        match best_context {
            Some(context) => {
                // Check if multi-GPU would be beneficial
                if problemsize > 1_000_000 && self.contexts.len() > 1 {
                    Ok(ExecutionStrategy::MultiGpu {
                        primary_context: context.clone(),
                        secondary_contexts: self.get_secondary_contexts(&context),
                    })
                } else {
                    Ok(ExecutionStrategy::Gpu {
                        context,
                        kernel_variant: self.select_kernel_variant(operation, problemsize),
                    })
                }
            }
            None => Ok(ExecutionStrategy::Cpu),
        }
    }

    /// Select the best GPU context for an operation
    fn select_best_context(
        &self,
        operation: &str,
        problemsize: usize,
    ) -> LinalgResult<Option<Arc<dyn GpuContext>>> {
        if self.contexts.is_empty() {
            return Ok(None);
        }

        let mut best_context = None;
        let mut best_score = 0.0f64;

        for (_context_name, context) in &self.contexts {
            let score = self.calculate_context_score(context.as_ref(), operation, problemsize);

            if score > best_score {
                best_score = score;
                best_context = Some(context.clone());
            }
        }

        Ok(best_context)
    }

    /// Calculate a score for a GPU context based on operation requirements
    fn calculate_context_score(
        &self,
        context: &dyn GpuContext,
        operation: &str,
        _problemsize: usize,
    ) -> f64 {
        let device_info = context.device_info();
        let mut score = 0.0;

        // Base score from compute units and memory
        score += device_info.compute_units as f64 * 0.1;
        score += (device_info.total_memory as f64 / 1_000_000_000.0) * 0.2; // GB of memory

        // Bonus for specific capabilities
        if operation.contains("mixed") && device_info.supports_mixed_precision {
            score += 1.0;
        }

        if device_info.supports_tensor_cores && self.config.tensor_cores {
            score += 2.0;
        }

        // Memory bandwidth factor
        score += device_info.memory_bandwidth / 100.0;

        // Historical performance bonus
        if let Some(measurements) = self.profiler.measurements.get(operation) {
            let avg_gflops: f64 = measurements
                .iter()
                .filter(|m| m.device_type == device_info.device_type)
                .map(|m| m.gflops)
                .sum::<f64>()
                / measurements.len() as f64;
            score += avg_gflops / 100.0;
        }

        score
    }

    /// Select optimal kernel variant for an operation
    fn select_kernel_variant(&self, operation: &str, problemsize: usize) -> KernelVariant {
        if !self.config.auto_kernel_selection {
            return KernelVariant::Basic;
        }

        match operation {
            "matmul" => {
                if problemsize > 100_000 {
                    KernelVariant::Optimized
                } else {
                    KernelVariant::Basic
                }
            }
            "matvec" => {
                if problemsize > 50_000 {
                    KernelVariant::Vectorized
                } else {
                    KernelVariant::Basic
                }
            }
            _ => KernelVariant::Basic,
        }
    }

    /// Get secondary contexts for multi-GPU operations
    fn get_secondary_contexts(&self, primary: &Arc<dyn GpuContext>) -> Vec<Arc<dyn GpuContext>> {
        self.contexts
            .values()
            .filter(|ctx| !Arc::ptr_eq(ctx, primary))
            .cloned()
            .collect()
    }

    /// Record performance measurement
    fn record_performance(
        &mut self,
        operation: &str,
        problemsize: usize,
        execution_time: f64,
        strategy: &ExecutionStrategy,
    ) {
        let device_type = match strategy {
            ExecutionStrategy::Cpu => return, // Don't record CPU performance here
            ExecutionStrategy::Gpu { context, .. } => context.device_info().device_type,
            ExecutionStrategy::MultiGpu {
                primary_context, ..
            } => primary_context.device_info().device_type,
        };

        // Estimate GFLOPS (rough approximation)
        let operations = match operation {
            "matmul" => problemsize as f64 * 2.0, // Rough estimate
            "matvec" => problemsize as f64,
            _ => problemsize as f64,
        };
        let gflops = operations / (execution_time * 1e9);

        let measurement = PerformanceMeasurement {
            operation: operation.to_string(),
            problemsize,
            execution_time,
            memory_usage: problemsize * std::mem::size_of::<T>(),
            device_type,
            gflops,
            memory_bandwidth_util: 0.8, // Placeholder
        };

        self.profiler
            .measurements
            .entry(operation.to_string())
            .or_default()
            .push(measurement);

        self.profiler.total_operations += 1;
        self.profiler.total_gpu_time += execution_time;
    }

    /// Get performance statistics
    pub fn get_performance_stats(&self) -> &GpuPerformanceProfiler {
        &self.profiler
    }

    /// Get available GPU contexts
    pub fn available_contexts(&self) -> Vec<String> {
        self.contexts.keys().cloned().collect()
    }

    /// Warm up GPU contexts (useful for benchmarking)
    pub fn warmup(&mut self) -> LinalgResult<()> {
        // Perform small operations on each GPU to initialize kernels
        let test_a = Array2::ones((32, 32));
        let test_b = Array2::ones((32, 32));

        for context in self.contexts.values() {
            let _ = self
                .dispatcher
                .gpu_matmul(context.as_ref(), &test_a.view(), &test_b.view());
        }

        Ok(())
    }

    /// Auto-tune performance parameters
    pub fn auto_tune(&mut self) -> LinalgResult<()> {
        // Implement auto-tuning by running benchmarks with different configurations
        let sizes = vec![64, 128, 256, 512, 1024, 2048];

        for &size in &sizes {
            let test_a = Array2::ones((size, size));
            let test_b = Array2::ones((size, size));

            // Try different strategies and record performance
            let _ = self.accelerated_matmul(&test_a.view(), &test_b.view())?;
        }

        // Analysis and parameter adjustment would go here
        Ok(())
    }
}

/// Execution strategy for an operation
enum ExecutionStrategy {
    /// Execute on CPU
    Cpu,
    /// Execute on single GPU
    Gpu {
        context: Arc<dyn GpuContext>,
        kernel_variant: KernelVariant,
    },
    /// Execute across multiple GPUs
    MultiGpu {
        primary_context: Arc<dyn GpuContext>,
        secondary_contexts: Vec<Arc<dyn GpuContext>>,
    },
}

impl std::fmt::Debug for ExecutionStrategy {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        match self {
            ExecutionStrategy::Cpu => f.debug_tuple("Cpu").finish(),
            ExecutionStrategy::Gpu { kernel_variant, .. } => f
                .debug_struct("Gpu")
                .field("kernel_variant", kernel_variant)
                .field("context", &"<GpuContext>")
                .finish(),
            ExecutionStrategy::MultiGpu { .. } => f
                .debug_struct("MultiGpu")
                .field("primary_context", &"<GpuContext>")
                .field("secondary_contexts", &"<Vec<GpuContext>>")
                .finish(),
        }
    }
}

/// GPU kernel variant selection
#[derive(Debug, Clone, Copy)]
enum KernelVariant {
    Basic,
    Optimized,
    Vectorized,
    TensorCore,
    Mixed,
}

impl<T> Default for GpuAccelerationFramework<T>
where
    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
{
    fn default() -> Self {
        Self::new().expect("Failed to initialize GPU acceleration framework")
    }
}

/// Convenience functions for common operations
impl<T> GpuAccelerationFramework<T>
where
    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
{
    /// Quick matrix multiplication with automatic acceleration
    pub fn quick_matmul(a: &ArrayView2<T>, b: &ArrayView2<T>) -> LinalgResult<Array2<T>> {
        let mut framework = Self::new()?;
        framework.initialize_contexts()?;
        framework.accelerated_matmul(a, b)
    }

    /// Quick matrix-vector multiplication with automatic acceleration
    pub fn quick_matvec(a: &ArrayView2<T>, x: &ArrayView1<T>) -> LinalgResult<Array1<T>> {
        let mut framework = Self::new()?;
        framework.initialize_contexts()?;
        framework.accelerated_matvec(a, x)
    }
}

/// Global GPU acceleration instance for convenience
static GLOBAL_GPU_FRAMEWORK: std::sync::OnceLock<
    Arc<Mutex<Option<GpuAccelerationFramework<f64>>>>,
> = std::sync::OnceLock::new();

/// Initialize global GPU acceleration (call once at startup)
#[allow(dead_code)]
pub fn initialize_global_gpu_acceleration() -> LinalgResult<()> {
    let mut framework = GpuAccelerationFramework::new()?;
    framework.initialize_contexts()?;
    framework.warmup()?;

    let arc_mutex = Arc::new(Mutex::new(Some(framework)));
    GLOBAL_GPU_FRAMEWORK.set(arc_mutex).map_err(|_| {
        LinalgError::ComputationError("Global GPU framework already initialized".to_string())
    })?;

    Ok(())
}

/// Advanced MODE: Advanced GPU Memory Management and Streaming
///
/// This extension provides sophisticated memory management and streaming capabilities
/// for handling very large matrices that don't fit in GPU memory.
pub struct AdvancedGpuMemoryManager<T>
where
    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
{
    /// Memory pools for different data types and sizes
    memory_pools: HashMap<String, GpuMemoryPool<T>>,
    /// Stream manager for asynchronous operations
    stream_manager: GpuStreamManager,
    /// Out-of-core operation handler
    out_of_core_handler: OutOfCoreHandler<T>,
    /// Memory usage statistics
    memory_stats: MemoryStatistics,
}

/// GPU memory pool for efficient allocation and reuse
pub struct GpuMemoryPool<T> {
    free_buffers: HashMap<usize, Vec<Box<dyn super::GpuBuffer<T>>>>,
    allocated_buffers: Vec<Box<dyn super::GpuBuffer<T>>>,
    total_allocated: usize,
    peak_usage: usize,
    allocation_count: usize,
    deallocation_count: usize,
}

impl<T> std::fmt::Debug for GpuMemoryPool<T> {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        f.debug_struct("GpuMemoryPool")
            .field("free_buffers_count", &self.free_buffers.len())
            .field("allocated_buffers_count", &self.allocated_buffers.len())
            .field("total_allocated", &self.total_allocated)
            .field("peak_usage", &self.peak_usage)
            .field("allocation_count", &self.allocation_count)
            .field("deallocation_count", &self.deallocation_count)
            .finish()
    }
}

/// GPU stream manager for asynchronous operations
#[derive(Debug)]
pub struct GpuStreamManager {
    active_streams: HashMap<String, StreamInfo>,
    stream_queue: Vec<StreamOperation>,
    max_concurrent_streams: usize,
}

/// Information about an active GPU stream
#[derive(Debug, Clone)]
pub struct StreamInfo {
    stream_id: String,
    device_context: String,
    operation_type: String,
    start_time: std::time::Instant,
    memory_usage: usize,
}

/// Stream operation for queue management
#[derive(Debug, Clone)]
pub struct StreamOperation {
    operation_id: String,
    priority: StreamPriority,
    dependencies: Vec<String>,
    estimated_time: f64,
    memory_requirement: usize,
}

/// Priority levels for stream operations
#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord)]
pub enum StreamPriority {
    Low = 0,
    Normal = 1,
    High = 2,
    Critical = 3,
}

/// Out-of-core operation handler for very large matrices
pub struct OutOfCoreHandler<T> {
    tile_manager: TileManager<T>,
    prefetch_cache: PrefetchCache<T>,
    compression_engine: CompressionEngine<T>,
}

/// Tile manager for matrix decomposition and processing
#[derive(Debug)]
pub struct TileManager<T> {
    tilesize: (usize, usize),
    overlapsize: usize,
    active_tiles: HashMap<String, MatrixTile<T>>,
    tile_schedule: Vec<TileOperation>,
}

/// Matrix tile for out-of-core processing
#[derive(Debug, Clone)]
pub struct MatrixTile<T> {
    tile_id: String,
    position: (usize, usize),
    size: (usize, usize),
    data: Option<Array2<T>>,
    last_accessed: std::time::Instant,
    is_dirty: bool,
}

/// Tile operation for scheduling
#[derive(Debug, Clone)]
pub struct TileOperation {
    operation_type: TileOperationType,
    source_tiles: Vec<String>,
    destination_tile: String,
    priority: StreamPriority,
}

/// Types of tile operations
#[derive(Debug, Clone, Copy)]
pub enum TileOperationType {
    Load,
    Store,
    Compute,
    Prefetch,
    Evict,
}

/// Prefetch cache for predictive data loading
pub struct PrefetchCache<T> {
    cache_entries: HashMap<String, CacheEntry<T>>,
    prediction_model: PredictionModel,
    max_cachesize: usize,
    current_cachesize: usize,
}

/// Cache entry for prefetch system
#[derive(Debug)]
pub struct CacheEntry<T> {
    data: Array2<T>,
    access_count: usize,
    last_access: std::time::Instant,
    prediction_score: f64,
}

/// Prediction model for cache prefetching
#[derive(Debug)]
pub struct PredictionModel {
    access_pattern_history: Vec<AccessPattern>,
    pattern_weights: HashMap<AccessPatternType, f64>,
    prediction_accuracy: f64,
}

/// Access pattern for prediction
#[derive(Debug, Clone)]
pub struct AccessPattern {
    pattern_type: AccessPatternType,
    sequence: Vec<String>,
    frequency: usize,
    last_seen: std::time::Instant,
}

/// Types of access patterns
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub enum AccessPatternType {
    Sequential,
    Strided,
    Random,
    Blocked,
    Hierarchical,
}

/// Compression engine for memory optimization
pub struct CompressionEngine<T> {
    compression_algorithms: HashMap<String, Box<dyn CompressionAlgorithm<T>>>,
    compression_stats: CompressionStatistics,
    adaptive_compression: bool,
}

/// Trait for compression algorithms
pub trait CompressionAlgorithm<T>: Send + Sync {
    fn compress(&self, data: &Array2<T>) -> LinalgResult<CompressedData>;
    fn decompress(&self, data: &CompressedData) -> LinalgResult<Array2<T>>;
    fn compression_ratio(&self) -> f64;
    fn compression_speed(&self) -> f64; // Operations per second
}

/// Compressed data structure
#[derive(Debug, Clone)]
pub struct CompressedData {
    algorithm: String,
    compressed_bytes: Vec<u8>,
    originalshape: (usize, usize),
    compression_ratio: f64,
    compression_time: f64,
}

/// Compression statistics
#[derive(Debug, Default)]
pub struct CompressionStatistics {
    total_compressions: usize,
    total_decompressions: usize,
    total_bytes_saved: usize,
    avg_compression_ratio: f64,
    avg_compression_time: f64,
    avg_decompression_time: f64,
}

/// Memory usage statistics
#[derive(Debug, Default)]
pub struct MemoryStatistics {
    peak_gpu_memory_usage: usize,
    current_gpu_memory_usage: usize,
    total_allocations: usize,
    total_deallocations: usize,
    allocation_failures: usize,
    memory_fragmentation: f64,
    cache_hit_rate: f64,
    cache_miss_rate: f64,
}

impl<T> AdvancedGpuMemoryManager<T>
where
    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
{
    /// Create a new advanced GPU memory manager
    pub fn new() -> Self {
        Self {
            memory_pools: HashMap::new(),
            stream_manager: GpuStreamManager::new(),
            out_of_core_handler: OutOfCoreHandler::new(),
            memory_stats: MemoryStatistics::default(),
        }
    }

    /// Initialize memory pools for different GPU contexts
    pub fn initialize_pools(
        &mut self,
        contexts: &HashMap<String, Arc<dyn super::GpuContext>>,
    ) -> LinalgResult<()> {
        for (context_name, context) in contexts {
            let device_info = context.device_info();
            let pool = GpuMemoryPool::new(device_info.total_memory / 4); // Use 25% of total memory for pool
            self.memory_pools.insert(context_name.clone(), pool);
        }
        Ok(())
    }

    /// Allocate GPU memory with intelligent pooling
    pub fn allocate_buffer(
        &mut self,
        context_name: &str,
        size: usize,
    ) -> LinalgResult<Box<dyn super::GpuBuffer<T>>> {
        if let Some(pool) = self.memory_pools.get_mut(context_name) {
            // Try to reuse existing buffer
            if let Some(buffer) = pool.try_reuse_buffer(size) {
                self.memory_stats.cache_hit_rate += 1.0;
                return Ok(buffer);
            }

            // Allocate new buffer
            let buffer = pool.allocate_new_buffer(size)?;
            self.memory_stats.total_allocations += 1;
            self.memory_stats.current_gpu_memory_usage += size;
            self.memory_stats.peak_gpu_memory_usage = self
                .memory_stats
                .peak_gpu_memory_usage
                .max(self.memory_stats.current_gpu_memory_usage);

            Ok(buffer)
        } else {
            Err(LinalgError::InvalidInput(format!(
                "Unknown context: {}",
                context_name
            )))
        }
    }

    /// Process very large matrix multiplication using out-of-core techniques
    pub fn out_of_core_matmul(
        &mut self,
        context: &dyn super::GpuContext,
        ashape: (usize, usize),
        bshape: (usize, usize),
        load_a: impl Fn(usize, usize, usize, usize) -> LinalgResult<Array2<T>>,
        load_b: impl Fn(usize, usize, usize, usize) -> LinalgResult<Array2<T>>,
        store_c: impl Fn(usize, usize, &Array2<T>) -> LinalgResult<()>,
    ) -> LinalgResult<()> {
        let (m, k) = ashape;
        let (k2, n) = bshape;

        if k != k2 {
            return Err(LinalgError::ShapeError(
                "Matrix dimensions must match".to_string(),
            ));
        }

        // Determine optimal tile sizes based on available GPU memory
        let available_memory = context.available_memory()?;
        let tilesize = self.calculate_optimal_tilesize(available_memory, (m, n, k));

        // Process matrix multiplication in tiles
        for i in (0..m).step_by(tilesize.0) {
            for j in (0..n).step_by(tilesize.1) {
                let mut c_tile =
                    Array2::zeros(((i + tilesize.0).min(m) - i, (j + tilesize.1).min(n) - j));

                // Accumulate partial results across K dimension
                for l in (0..k).step_by(tilesize.2) {
                    let a_tile = load_a(i, l, tilesize.0, tilesize.2)?;
                    let b_tile = load_b(l, j, tilesize.2, tilesize.1)?;

                    // Perform tile multiplication on GPU
                    let partial_result = self.gpu_tile_matmul(context, &a_tile, &b_tile)?;

                    // Accumulate result
                    c_tile = c_tile + partial_result;
                }

                // Store result tile
                store_c(i, j, &c_tile)?;
            }
        }

        Ok(())
    }

    /// Asynchronous matrix operations with streaming
    pub fn async_matmul_streamed(
        &mut self,
        context: &dyn super::GpuContext,
        a: &ArrayView2<T>,
        b: &ArrayView2<T>,
    ) -> LinalgResult<StreamHandle<Array2<T>>> {
        let stream_id = format!("matmul_{}", self.stream_manager.generate_stream_id());

        // Create stream operation
        let operation = StreamOperation {
            operation_id: stream_id.clone(),
            priority: StreamPriority::Normal,
            dependencies: vec![],
            estimated_time: self.estimate_operation_time("matmul", a.len() + b.len()),
            memory_requirement: (a.len() + b.len()) * std::mem::size_of::<T>(),
        };

        // Queue operation
        self.stream_manager.queue_operation(operation);

        // Create stream handle
        Ok(StreamHandle::new(stream_id))
    }

    /// Predictive prefetching based on access patterns
    pub fn enable_predictive_prefetching(&mut self, enable: bool) {
        self.out_of_core_handler
            .prefetch_cache
            .prediction_model
            .update_enabled(enable);
    }

    /// Get comprehensive memory statistics
    pub fn get_memory_statistics(&self) -> &MemoryStatistics {
        &self.memory_stats
    }

    /// Optimize memory layout for better performance
    pub fn optimize_memory_layout(&mut self, context: &dyn super::GpuContext) -> LinalgResult<()> {
        // Implement memory defragmentation and layout optimization
        for (_, pool) in self.memory_pools.iter_mut() {
            pool.defragment()?;
        }

        // Update fragmentation statistics
        self.memory_stats.memory_fragmentation = self.calculate_fragmentation();

        Ok(())
    }

    // Private helper methods

    fn calculate_optimal_tilesize(
        &self,
        available_memory: usize,
        dimensions: (usize, usize, usize),
    ) -> (usize, usize, usize) {
        let (m, n, k) = dimensions;
        let elementsize = std::mem::size_of::<T>();

        // Reserve _memory for 3 tiles (A, B, C) plus overhead
        let usable_memory = available_memory / 4; // Use 25% of available _memory

        // Calculate tile size that fits in _memory
        let max_tile_elements = usable_memory / (3 * elementsize);
        let tile_dim = (max_tile_elements as f64).powf(1.0 / 3.0) as usize;

        (tile_dim.min(m), tile_dim.min(n), tile_dim.min(k))
    }

    fn gpu_tile_matmul(
        &self,
        context: &dyn super::GpuContext,
        a: &Array2<T>,
        b: &Array2<T>,
    ) -> LinalgResult<Array2<T>> {
        // Simplified tile multiplication - would use optimized GPU kernels
        let (m, k) = a.dim();
        let (_, n) = b.dim();
        let mut result = Array2::zeros((m, n));

        for i in 0..m {
            for j in 0..n {
                let mut sum = T::zero();
                for l in 0..k {
                    sum += a[[i, l]] * b[[l, j]];
                }
                result[[i, j]] = sum;
            }
        }

        Ok(result)
    }

    fn estimate_operation_time(&self, operation: &str, problemsize: usize) -> f64 {
        // Estimate operation time based on historical data
        match operation {
            "matmul" => problemsize as f64 * 1e-9, // Rough estimate
            "matvec" => problemsize as f64 * 5e-10,
            _ => problemsize as f64 * 1e-9,
        }
    }

    fn calculate_fragmentation(&self) -> f64 {
        // Calculate memory fragmentation across all pools
        let mut total_fragmentation = 0.0;
        let mut pool_count = 0;

        for pool in self.memory_pools.values() {
            total_fragmentation += pool.calculate_fragmentation();
            pool_count += 1;
        }

        if pool_count > 0 {
            total_fragmentation / pool_count as f64
        } else {
            0.0
        }
    }
}

/// Handle for asynchronous stream operations
pub struct StreamHandle<T> {
    stream_id: String,
    _phantom: std::marker::PhantomData<T>,
}

impl<T> StreamHandle<T> {
    fn new(stream_id: String) -> Self {
        Self {
            stream_id,
            _phantom: std::marker::PhantomData,
        }
    }

    /// Check if the operation is complete
    pub fn is_ready(&self) -> bool {
        // Would check actual stream status
        true // Placeholder
    }

    /// Get the result (blocking if not ready)
    pub fn get_result(self) -> LinalgResult<T> {
        // Would wait for stream completion and return result
        Err(LinalgError::ComputationError("Not implemented".to_string()))
    }
}

impl<T: Clone + Send + Sync + std::fmt::Debug + 'static> GpuMemoryPool<T> {
    fn new(_maxsize: usize) -> Self {
        Self {
            free_buffers: HashMap::new(),
            allocated_buffers: Vec::new(),
            total_allocated: 0,
            peak_usage: 0,
            allocation_count: 0,
            deallocation_count: 0,
        }
    }

    fn try_reuse_buffer(&mut self, size: usize) -> Option<Box<dyn super::GpuBuffer<T>>> {
        // Find the smallest buffer that's large enough
        let mut bestsize = None;
        for &buffersize in self.free_buffers.keys() {
            if buffersize >= size {
                match bestsize {
                    Some(current_best) if buffersize < current_best => {
                        bestsize = Some(buffersize);
                    }
                    None => {
                        bestsize = Some(buffersize);
                    }
                    _ => {}
                }
            }
        }

        if let Some(buffersize) = bestsize {
            if let Some(mut buffers) = self.free_buffers.remove(&buffersize) {
                if let Some(buffer) = buffers.pop() {
                    if !buffers.is_empty() {
                        self.free_buffers.insert(buffersize, buffers);
                    }
                    return Some(buffer);
                }
            }
        }

        None
    }

    fn allocate_new_buffer(&mut self, size: usize) -> LinalgResult<Box<dyn super::GpuBuffer<T>>> {
        // No physical GPU is available through this pool, so we hand out a real
        // CPU-backed buffer that actually stores the data. This keeps results
        // correct (host round-trip) instead of fabricating an opaque device buffer.
        self.allocation_count += 1;
        self.total_allocated += size;
        self.peak_usage = self.peak_usage.max(self.total_allocated);

        Ok(Box::new(CpuFallbackBuffer::new(size)))
    }

    fn defragment(&mut self) -> LinalgResult<()> {
        // Implement memory defragmentation
        // This would reorganize memory to reduce fragmentation
        Ok(())
    }

    fn calculate_fragmentation(&self) -> f64 {
        // Calculate fragmentation metric (0.0 = no fragmentation, 1.0 = maximum fragmentation)
        if self.total_allocated == 0 {
            return 0.0;
        }

        // Simplified fragmentation calculation
        let free_chunks = self.free_buffers.len();
        if free_chunks <= 1 {
            0.0
        } else {
            (free_chunks as f64 - 1.0) / free_chunks as f64
        }
    }
}

impl GpuStreamManager {
    fn new() -> Self {
        Self {
            active_streams: HashMap::new(),
            stream_queue: Vec::new(),
            max_concurrent_streams: 4, // Default
        }
    }

    fn generate_stream_id(&self) -> String {
        format!("stream_{}", self.active_streams.len())
    }

    fn queue_operation(&mut self, operation: StreamOperation) {
        self.stream_queue.push(operation);
        self.stream_queue
            .sort_by_key(|op| std::cmp::Reverse(op.priority));
    }
}

impl<T> OutOfCoreHandler<T> {
    fn new() -> Self {
        Self {
            tile_manager: TileManager::new(),
            prefetch_cache: PrefetchCache::new(),
            compression_engine: CompressionEngine::new(),
        }
    }
}

impl<T> TileManager<T> {
    fn new() -> Self {
        Self {
            tilesize: (256, 256), // Default tile size
            overlapsize: 0,
            active_tiles: HashMap::new(),
            tile_schedule: Vec::new(),
        }
    }
}

impl<T> PrefetchCache<T> {
    fn new() -> Self {
        #[cfg(target_pointer_width = "32")]
        let max_cachesize = 256 * 1024 * 1024; // 256MB default for 32-bit
        #[cfg(target_pointer_width = "64")]
        let max_cachesize = 1024 * 1024 * 1024; // 1GB default for 64-bit

        Self {
            cache_entries: HashMap::new(),
            prediction_model: PredictionModel::new(),
            max_cachesize,
            current_cachesize: 0,
        }
    }
}

impl PredictionModel {
    fn new() -> Self {
        Self {
            access_pattern_history: Vec::new(),
            pattern_weights: HashMap::new(),
            prediction_accuracy: 0.0,
        }
    }

    fn update_enabled(&mut self, enable: bool) {
        // Update prediction model state
    }
}

impl<T> CompressionEngine<T> {
    fn new() -> Self {
        Self {
            compression_algorithms: HashMap::new(),
            compression_stats: CompressionStatistics::default(),
            adaptive_compression: true,
        }
    }
}

/// Real CPU-backed buffer used as a fallback when no GPU device is present.
///
/// Unlike a mock, this buffer genuinely stores the data on the host, so
/// `copy_from_host` / `copy_to_host` round-trip correctly and computations
/// performed through it return real results rather than fabricated values.
#[derive(Debug)]
pub struct CpuFallbackBuffer<T> {
    data: Vec<T>,
}

impl<T: Clone> CpuFallbackBuffer<T> {
    /// Create an empty buffer with capacity reserved for `size` elements.
    pub fn new(size: usize) -> Self {
        Self {
            data: Vec::with_capacity(size),
        }
    }
}

impl<T> super::GpuBuffer<T> for CpuFallbackBuffer<T>
where
    T: Clone + Send + Sync + std::fmt::Debug,
{
    fn len(&self) -> usize {
        self.data.len()
    }

    fn copy_from_host(&mut self, data: &[T]) -> LinalgResult<()> {
        self.data.clear();
        self.data.extend_from_slice(data);
        Ok(())
    }

    fn copy_to_host(&self, data: &mut [T]) -> LinalgResult<()> {
        if data.len() != self.data.len() {
            return Err(LinalgError::ShapeError(format!(
                "Buffer size mismatch: host slice holds {} elements but device buffer holds {}",
                data.len(),
                self.data.len()
            )));
        }
        data.clone_from_slice(&self.data);
        Ok(())
    }

    fn device_ptr(&self) -> *mut std::ffi::c_void {
        self.data.as_ptr() as *mut std::ffi::c_void
    }
}

/// Get reference to global GPU acceleration framework
#[allow(dead_code)]
pub fn get_global_gpu_framework(
) -> Option<std::sync::MutexGuard<'static, Option<GpuAccelerationFramework<f64>>>> {
    GLOBAL_GPU_FRAMEWORK.get()?.try_lock().ok()
}

#[cfg(test)]
mod tests {
    use super::*;
    use scirs2_core::ndarray::Array2;

    #[test]
    fn test_gpu_framework_creation() {
        let framework = GpuAccelerationFramework::<f64>::new();
        assert!(framework.is_ok());
    }

    #[test]
    fn test_execution_strategy_selection() {
        let framework = GpuAccelerationFramework::<f64>::new().expect("Operation failed");

        // Small problem should prefer CPU
        let strategy = framework.select_execution_strategy("matmul", 1000);
        assert!(strategy.is_ok());

        // Large problem should consider GPU if available
        let strategy = framework.select_execution_strategy("matmul", 1_000_000);
        assert!(strategy.is_ok());
    }

    #[test]
    fn test_quick_operations() {
        let a = Array2::<f64>::ones((4, 4));
        let b = Array2::<f64>::ones((4, 4));

        // These should not panic even without GPU
        let _result = GpuAccelerationFramework::quick_matmul(&a.view(), &b.view());
        // Result depends on GPU availability

        let x = Array1::<f64>::ones(4);
        let _result = GpuAccelerationFramework::quick_matvec(&a.view(), &x.view());
        // Result depends on GPU availability
    }
}