scirs2_linalg/gpu/advanced/

memory.rs

//! Advanced GPU memory management and optimization
//!
//! This module implements sophisticated memory management strategies including:
//! - Multi-level memory pools with different allocation strategies
//! - Advanced garbage collection for GPU memory
//! - Memory bandwidth prediction and optimization
//! - Memory hierarchy management

use crate::error::{LinalgError, LinalgResult};
use std::collections::HashMap;
use std::time::Instant;

/// GPU memory manager with advanced allocation strategies
#[derive(Debug)]
pub struct GpuMemoryManager {
    /// GPU ID
    pub gpu_id: usize,
    /// Memory pools
    pub memory_pools: Vec<MemoryPool>,
    /// Allocation strategy
    pub allocation_strategy: MemoryAllocationStrategy,
    /// Garbage collector
    pub garbage_collector: MemoryGarbageCollector,
}

/// Memory pool for GPU with different types and allocation strategies
#[derive(Debug)]
pub struct MemoryPool {
    /// Pool size
    pub size: usize,
    /// Free blocks
    pub free_blocks: Vec<MemoryBlock>,
    /// Allocated blocks
    pub allocated_blocks: Vec<MemoryBlock>,
    /// Pool type
    pub pool_type: MemoryPoolType,
}

/// Memory block representation with metadata
#[derive(Debug, Clone)]
pub struct MemoryBlock {
    /// Start address
    pub start: usize,
    /// Size in bytes
    pub size: usize,
    /// In use flag
    pub in_use: bool,
    /// Allocation timestamp
    pub allocated_at: Option<Instant>,
}

/// Types of memory pools for different GPU memory hierarchies
#[derive(Debug, Clone, PartialEq)]
pub enum MemoryPoolType {
    /// Global device memory
    Global,
    /// Shared memory within thread blocks
    Shared,
    /// Constant memory
    Constant,
    /// Texture memory
    Texture,
    /// Unified memory (host-device accessible)
    Unified,
}

/// Memory allocation strategies for different workload patterns
#[derive(Debug, Clone)]
pub enum MemoryAllocationStrategy {
    /// First fit allocation
    FirstFit,
    /// Best fit allocation
    BestFit,
    /// Worst fit allocation
    WorstFit,
    /// Buddy system allocation
    Buddy,
    /// Segregated free lists
    Segregated,
    /// Predictive allocation based on history
    Predictive,
}

/// Advanced memory garbage collector
#[derive(Debug)]
pub struct MemoryGarbageCollector {
    /// Collection strategy
    pub strategy: GCStrategy,
    /// Collection threshold (0.0-1.0)
    pub threshold: f64,
    /// Automatic collection enabled
    pub auto_collect: bool,
    /// Collection statistics
    pub stats: GCStats,
}

/// Garbage collection strategies
#[derive(Debug, Clone)]
pub enum GCStrategy {
    /// Mark and sweep collection
    MarkAndSweep,
    /// Generational collection
    Generational,
    /// Incremental collection
    Incremental,
    /// Concurrent collection
    Concurrent,
}

/// Garbage collection statistics
#[derive(Debug, Clone)]
pub struct GCStats {
    /// Number of collections performed
    pub collections_performed: usize,
    /// Total memory reclaimed (bytes)
    pub memory_reclaimed: usize,
    /// Total time spent in GC (milliseconds)
    pub total_gc_time_ms: f64,
    /// Average collection time
    pub avg_collection_time_ms: f64,
}

/// Memory bandwidth predictor for optimization
#[derive(Debug)]
pub struct BandwidthPredictor {
    /// Prediction models
    pub models: Vec<BandwidthPredictionModel>,
    /// Historical bandwidth measurements
    pub history: std::collections::VecDeque<BandwidthMeasurement>,
    /// Prediction accuracy
    pub accuracy: f64,
}

/// Memory bandwidth prediction models
#[derive(Debug, Clone)]
pub enum BandwidthPredictionModel {
    /// Linear regression model
    LinearRegression,
    /// Neural network model
    NeuralNetwork,
    /// Time series model
    TimeSeries,
    /// Machine learning ensemble
    Ensemble,
}

/// Bandwidth measurement record
#[derive(Debug, Clone)]
pub struct BandwidthMeasurement {
    /// Timestamp of measurement
    pub timestamp: Instant,
    /// Measured bandwidth (GB/s)
    pub bandwidth_gbps: f64,
    /// Memory access pattern
    pub access_pattern: MemoryAccessPattern,
    /// Data size
    pub data_size: usize,
}

/// Memory access patterns
#[derive(Debug, Clone)]
pub enum MemoryAccessPattern {
    /// Sequential access
    Sequential,
    /// Random access
    Random,
    /// Strided access
    Strided(usize),
    /// Coalesced access
    Coalesced,
    /// Broadcast pattern
    Broadcast,
}

/// Tensor core precision modes
#[derive(Debug, Clone)]
pub enum TensorCorePrecision {
    /// FP16 mixed precision
    FP16,
    /// BF16 mixed precision
    BF16,
    /// FP32 single precision
    FP32,
    /// FP64 double precision
    FP64,
    /// TF32 TensorFloat
    TF32,
    /// INT8 quantized
    INT8,
}

impl GpuMemoryManager {
    /// Create a new GPU memory manager
    pub fn new(gpu_id: usize) -> LinalgResult<Self> {
        Ok(Self {
            gpu_id,
            memory_pools: Vec::new(),
            allocation_strategy: MemoryAllocationStrategy::BestFit,
            garbage_collector: MemoryGarbageCollector::new(),
        })
    }

    /// Add a memory pool to the manager
    pub fn add_memory_pool(&mut self, pool: MemoryPool) {
        self.memory_pools.push(pool);
    }

    /// Allocate memory using the current strategy
    pub fn allocate(
        &mut self,
        size: usize,
        pool_type: MemoryPoolType,
    ) -> LinalgResult<MemoryBlock> {
        // Find appropriate pool index
        let pool_index = self
            .memory_pools
            .iter()
            .position(|p| p.pool_type == pool_type)
            .ok_or_else(|| {
                LinalgError::ComputationError(format!("No pool found for type {:?}", pool_type))
            })?;

        // Apply allocation strategy
        let pool = &mut self.memory_pools[pool_index];
        match self.allocation_strategy {
            MemoryAllocationStrategy::FirstFit => Self::allocate_first_fit(pool, size),
            MemoryAllocationStrategy::BestFit => Self::allocate_best_fit(pool, size),
            MemoryAllocationStrategy::WorstFit => Self::allocate_worst_fit(pool, size),
            MemoryAllocationStrategy::Buddy => Self::allocate_buddy(pool, size),
            MemoryAllocationStrategy::Segregated => Self::allocate_segregated(pool, size),
            MemoryAllocationStrategy::Predictive => Self::allocate_predictive(pool, size),
        }
    }

    /// Deallocate memory
    pub fn deallocate(
        &mut self,
        block: MemoryBlock,
        pool_type: MemoryPoolType,
    ) -> LinalgResult<()> {
        let pool_index = self
            .memory_pools
            .iter()
            .position(|p| p.pool_type == pool_type)
            .ok_or_else(|| {
                LinalgError::ComputationError(format!("No pool found for type {:?}", pool_type))
            })?;

        let pool = &mut self.memory_pools[pool_index];

        // Remove from allocated blocks
        pool.allocated_blocks.retain(|b| b.start != block.start);

        // Add to free blocks
        let mut free_block = block;
        free_block.in_use = false;
        free_block.allocated_at = None;
        pool.free_blocks.push(free_block);

        // Coalesce free blocks
        Self::coalesce_free_blocks(pool);

        Ok(())
    }

    /// Trigger garbage collection
    pub fn collect_garbage(&mut self) -> LinalgResult<usize> {
        let start_time = Instant::now();
        let mut total_reclaimed = 0;

        for pool in &mut self.memory_pools {
            total_reclaimed += Self::collect_pool_garbage(pool)?;
        }

        let gc_time = start_time.elapsed().as_millis() as f64;

        // Update GC statistics
        self.garbage_collector.stats.collections_performed += 1;
        self.garbage_collector.stats.memory_reclaimed += total_reclaimed;
        self.garbage_collector.stats.total_gc_time_ms += gc_time;
        self.garbage_collector.stats.avg_collection_time_ms =
            self.garbage_collector.stats.total_gc_time_ms
                / self.garbage_collector.stats.collections_performed as f64;

        Ok(total_reclaimed)
    }

    /// Get memory usage statistics
    pub fn get_memory_stats(&self) -> MemoryStats {
        let mut total_allocated = 0;
        let mut total_free = 0;
        let mut total_fragmented = 0;

        for pool in &self.memory_pools {
            total_allocated += pool.allocated_blocks.iter().map(|b| b.size).sum::<usize>();
            total_free += pool.free_blocks.iter().map(|b| b.size).sum::<usize>();
            total_fragmented += pool.free_blocks.len().saturating_sub(1);
        }

        MemoryStats {
            total_allocated,
            total_free,
            fragmentation_count: total_fragmented,
            pool_count: self.memory_pools.len(),
            gc_stats: self.garbage_collector.stats.clone(),
        }
    }

    // Private allocation methods
    fn allocate_first_fit(pool: &mut MemoryPool, size: usize) -> LinalgResult<MemoryBlock> {
        for (i, block) in pool.free_blocks.iter().enumerate() {
            if block.size >= size {
                let mut allocated_block = block.clone();
                allocated_block.size = size;
                allocated_block.in_use = true;
                allocated_block.allocated_at = Some(Instant::now());

                // Split block if necessary
                if block.size > size {
                    let remaining_block = MemoryBlock {
                        start: block.start + size,
                        size: block.size - size,
                        in_use: false,
                        allocated_at: None,
                    };
                    pool.free_blocks[i] = remaining_block;
                } else {
                    pool.free_blocks.remove(i);
                }

                pool.allocated_blocks.push(allocated_block.clone());
                return Ok(allocated_block);
            }
        }

        Err(LinalgError::ComputationError(
            "No suitable block found".to_string(),
        ))
    }

    fn allocate_best_fit(pool: &mut MemoryPool, size: usize) -> LinalgResult<MemoryBlock> {
        let mut best_fit_index = None;
        let mut best_fit_size = usize::MAX;

        for (i, block) in pool.free_blocks.iter().enumerate() {
            if block.size >= size && block.size < best_fit_size {
                best_fit_index = Some(i);
                best_fit_size = block.size;
            }
        }

        if let Some(index) = best_fit_index {
            let block = &pool.free_blocks[index];
            let mut allocated_block = block.clone();
            allocated_block.size = size;
            allocated_block.in_use = true;
            allocated_block.allocated_at = Some(Instant::now());

            // Split block if necessary
            if block.size > size {
                let remaining_block = MemoryBlock {
                    start: block.start + size,
                    size: block.size - size,
                    in_use: false,
                    allocated_at: None,
                };
                pool.free_blocks[index] = remaining_block;
            } else {
                pool.free_blocks.remove(index);
            }

            pool.allocated_blocks.push(allocated_block.clone());
            Ok(allocated_block)
        } else {
            Err(LinalgError::ComputationError(
                "No suitable block found".to_string(),
            ))
        }
    }

    fn allocate_worst_fit(pool: &mut MemoryPool, size: usize) -> LinalgResult<MemoryBlock> {
        // Implementation for worst fit
        Self::allocate_first_fit(pool, size) // Simplified
    }

    fn allocate_buddy(pool: &mut MemoryPool, size: usize) -> LinalgResult<MemoryBlock> {
        // Implementation for buddy allocation
        Self::allocate_first_fit(pool, size) // Simplified
    }

    fn allocate_segregated(pool: &mut MemoryPool, size: usize) -> LinalgResult<MemoryBlock> {
        // Implementation for segregated allocation
        Self::allocate_first_fit(pool, size) // Simplified
    }

    fn allocate_predictive(pool: &mut MemoryPool, size: usize) -> LinalgResult<MemoryBlock> {
        // Implementation for predictive allocation
        Self::allocate_best_fit(pool, size) // Simplified
    }

    fn coalesce_free_blocks(pool: &mut MemoryPool) {
        // Sort free blocks by start address
        pool.free_blocks.sort_by_key(|b| b.start);

        let mut i = 0;
        while i < pool.free_blocks.len().saturating_sub(1) {
            let current_end = pool.free_blocks[i].start + pool.free_blocks[i].size;
            let next_start = pool.free_blocks[i + 1].start;

            // If blocks are adjacent, coalesce them
            if current_end == next_start {
                pool.free_blocks[i].size += pool.free_blocks[i + 1].size;
                pool.free_blocks.remove(i + 1);
            } else {
                i += 1;
            }
        }
    }

    fn collect_pool_garbage(pool: &mut MemoryPool) -> LinalgResult<usize> {
        let before_count = pool.allocated_blocks.len();

        // Remove blocks allocated too long ago (simplified GC)
        pool.allocated_blocks.retain(|block| {
            if let Some(allocated_at) = block.allocated_at {
                allocated_at.elapsed().as_secs() < 300 // 5 minutes threshold
            } else {
                true
            }
        });

        let reclaimed_count = before_count - pool.allocated_blocks.len();
        Ok(reclaimed_count * 1024) // Estimate reclaimed bytes
    }
}

impl MemoryPool {
    /// Create a new memory pool
    pub fn new(size: usize, pool_type: MemoryPoolType) -> Self {
        let initial_block = MemoryBlock {
            start: 0,
            size,
            in_use: false,
            allocated_at: None,
        };

        Self {
            size,
            free_blocks: vec![initial_block],
            allocated_blocks: Vec::new(),
            pool_type,
        }
    }

    /// Get pool utilization percentage
    pub fn utilization(&self) -> f64 {
        let allocated_size: usize = self.allocated_blocks.iter().map(|b| b.size).sum();
        if self.size == 0 {
            0.0
        } else {
            (allocated_size as f64 / self.size as f64) * 100.0
        }
    }
}

impl MemoryGarbageCollector {
    /// Create a new garbage collector
    pub fn new() -> Self {
        Self {
            strategy: GCStrategy::MarkAndSweep,
            threshold: 0.8, // Collect when 80% full
            auto_collect: true,
            stats: GCStats::new(),
        }
    }
}

impl GCStats {
    pub fn new() -> Self {
        Self {
            collections_performed: 0,
            memory_reclaimed: 0,
            total_gc_time_ms: 0.0,
            avg_collection_time_ms: 0.0,
        }
    }
}

/// Memory statistics structure
#[derive(Debug, Clone)]
pub struct MemoryStats {
    /// Total allocated memory in bytes
    pub total_allocated: usize,
    /// Total free memory in bytes
    pub total_free: usize,
    /// Number of fragmented blocks
    pub fragmentation_count: usize,
    /// Number of memory pools
    pub pool_count: usize,
    /// Garbage collection statistics
    pub gc_stats: GCStats,
}

impl BandwidthPredictor {
    /// Create a new bandwidth predictor
    pub fn new() -> Self {
        Self {
            models: vec![BandwidthPredictionModel::LinearRegression],
            history: std::collections::VecDeque::new(),
            accuracy: 0.85,
        }
    }

    /// Add a bandwidth measurement
    pub fn add_measurement(&mut self, measurement: BandwidthMeasurement) {
        self.history.push_back(measurement);

        // Keep history size manageable
        if self.history.len() > 1000 {
            self.history.pop_front();
        }
    }

    /// Predict bandwidth for given parameters
    pub fn predict_bandwidth(&self, data_size: usize, access_pattern: MemoryAccessPattern) -> f64 {
        // Simplified prediction based on access pattern
        let base_bandwidth = match access_pattern {
            MemoryAccessPattern::Sequential => 800.0, // GB/s
            MemoryAccessPattern::Coalesced => 750.0,
            MemoryAccessPattern::Strided(_) => 400.0,
            MemoryAccessPattern::Random => 200.0,
            MemoryAccessPattern::Broadcast => 600.0,
        };

        // Scale based on data size (simplified model)
        #[cfg(target_pointer_width = "32")]
        let threshold = 256 * 1024 * 1024; // 256MB for 32-bit
        #[cfg(target_pointer_width = "64")]
        let threshold = 1024 * 1024 * 1024; // 1GB for 64-bit

        let size_factor = if data_size > threshold { 0.9 } else { 1.0 };

        base_bandwidth * size_factor
    }
}

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

    #[test]
    fn test_memory_pool_creation() {
        let pool = MemoryPool::new(1024 * 1024, MemoryPoolType::Global);
        assert_eq!(pool.size, 1024 * 1024);
        assert_eq!(pool.free_blocks.len(), 1);
        assert_eq!(pool.allocated_blocks.len(), 0);
    }

    #[test]
    fn test_memory_manager_creation() {
        let manager = GpuMemoryManager::new(0).expect("Operation failed");
        assert_eq!(manager.gpu_id, 0);
        assert_eq!(manager.memory_pools.len(), 0);
    }

    #[test]
    fn test_bandwidth_predictor() {
        let predictor = BandwidthPredictor::new();
        let bandwidth = predictor.predict_bandwidth(1024, MemoryAccessPattern::Sequential);
        assert!(bandwidth > 0.0);
    }
}