scirs2-metrics 0.3.0

//! GPU context management for different backends
//!
//! This module provides context management for CUDA, OpenCL, Metal, and Vulkan
//! including device properties, memory pools, and runtime state.

#![allow(clippy::too_many_arguments)]
#![allow(dead_code)]

use super::runtime::{CudaRuntime, OpenClRuntime};
use crate::error::Result;
use std::collections::HashMap;
use std::sync::{Arc, Mutex};
use std::time::Instant;

/// CUDA context management
#[derive(Debug)]
pub struct CudaContext {
    /// Device ID
    pub _device_id: i32,
    /// Context handle (would be actual CUDA context in real implementation)
    pub context_handle: usize,
    /// Stream handles for asynchronous operations
    pub streams: Vec<usize>,
    /// Memory pool for efficient allocation
    pub memory_pool: Arc<Mutex<CudaMemoryPool>>,
    /// Device properties
    pub device_props: CudaDeviceProperties,
    /// CUDA runtime interface
    pub runtime: Arc<Mutex<CudaRuntime>>,
}

/// CUDA device properties
#[derive(Debug, Clone)]
pub struct CudaDeviceProperties {
    pub name: String,
    pub major: i32,
    pub minor: i32,
    pub total_global_mem: usize,
    pub shared_mem_per_block: usize,
    pub max_threads_per_block: i32,
    pub max_threads_dim: [i32; 3],
    pub max_grid_size: [i32; 3],
    pub warp_size: i32,
    pub memory_pitch: usize,
    pub max_threads_per_multiprocessor: i32,
    pub multiprocessor_count: i32,
    pub clock_rate: i32,
    pub memory_clock_rate: i32,
    pub memory_bus_width: i32,
    pub l2_cache_size: i32,
    pub texture_alignment: usize,
    pub concurrent_kernels: bool,
    pub compute_mode: i32,
    pub unified_addressing: bool,
}

/// CUDA memory pool for efficient allocation
#[derive(Debug)]
pub struct CudaMemoryPool {
    /// Available memory blocks
    pub free_blocks: HashMap<usize, Vec<CudaMemoryBlock>>,
    /// Allocated memory blocks
    pub allocated_blocks: HashMap<usize, CudaMemoryBlock>,
    /// Total allocated memory
    pub total_allocated: usize,
    /// Memory allocation limit
    pub memory_limit: usize,
}

/// CUDA memory block
#[derive(Debug, Clone)]
pub struct CudaMemoryBlock {
    /// Device pointer (would be actual CUDA device pointer)
    pub ptr: usize,
    /// Size in bytes
    pub size: usize,
    /// Allocation timestamp
    pub allocated_at: Instant,
}

/// OpenCL context management
#[derive(Debug)]
pub struct OpenClContext {
    /// Platform ID
    pub platform_id: usize,
    /// Device ID
    pub _device_id: usize,
    /// Context handle
    pub context_handle: usize,
    /// Command queue
    pub command_queue: usize,
    /// Compiled programs cache
    pub program_cache: Arc<Mutex<HashMap<String, usize>>>,
    /// Device info
    pub device_info: OpenClDeviceInfo,
    /// OpenCL runtime interface
    pub runtime: Arc<Mutex<OpenClRuntime>>,
}

/// OpenCL device information
#[derive(Debug, Clone)]
pub struct OpenClDeviceInfo {
    pub name: String,
    pub vendor: String,
    pub version: String,
    pub profile: String,
    pub global_mem_size: usize,
    pub local_mem_size: usize,
    pub max_work_group_size: usize,
    pub max_work_item_dimensions: u32,
    pub max_work_item_sizes: Vec<usize>,
    pub max_compute_units: u32,
    pub max_clock_frequency: u32,
    pub address_bits: u32,
    pub image_support: bool,
    pub preferred_vector_width_float: u32,
    pub preferred_vector_width_double: u32,
}

impl CudaMemoryPool {
    /// Create new CUDA memory pool
    pub fn new(memory_limit: usize) -> Self {
        Self {
            free_blocks: HashMap::new(),
            allocated_blocks: HashMap::new(),
            total_allocated: 0,
            memory_limit,
        }
    }

    /// Allocate memory block
    pub fn allocate(&mut self, size: usize) -> Option<CudaMemoryBlock> {
        if self.total_allocated + size > self.memory_limit {
            return None;
        }

        // Check for available free block
        if let Some(blocks) = self.free_blocks.get_mut(&size) {
            if let Some(block) = blocks.pop() {
                self.allocated_blocks.insert(block.ptr, block.clone());
                return Some(block);
            }
        }

        // Create new block
        let ptr = self.total_allocated + 0x10000000; // Mock address
        let block = CudaMemoryBlock {
            ptr,
            size,
            allocated_at: Instant::now(),
        };

        self.total_allocated += size;
        self.allocated_blocks.insert(ptr, block.clone());
        Some(block)
    }

    /// Free memory block
    pub fn free(&mut self, ptr: usize) -> bool {
        if let Some(block) = self.allocated_blocks.remove(&ptr) {
            self.total_allocated -= block.size;
            self.free_blocks
                .entry(block.size)
                .or_insert_with(Vec::new)
                .push(block);
            true
        } else {
            false
        }
    }

    /// Get memory usage statistics
    pub fn get_stats(&self) -> CudaMemoryStats {
        CudaMemoryStats {
            total_allocated: self.total_allocated,
            free_blocks: self.free_blocks.values().map(|v| v.len()).sum(),
            allocated_blocks: self.allocated_blocks.len(),
            memory_limit: self.memory_limit,
        }
    }
}

/// CUDA memory pool statistics
#[derive(Debug, Clone)]
pub struct CudaMemoryStats {
    pub total_allocated: usize,
    pub free_blocks: usize,
    pub allocated_blocks: usize,
    pub memory_limit: usize,
}