scirs2_metrics/optimization/gpu_kernels/
contexts.rs

1//! GPU context management for different backends
2//!
3//! This module provides context management for CUDA, OpenCL, Metal, and Vulkan
4//! including device properties, memory pools, and runtime state.
5
6#![allow(clippy::too_many_arguments)]
7#![allow(dead_code)]
8
9use super::runtime::{CudaRuntime, OpenClRuntime};
10use crate::error::Result;
11use std::collections::HashMap;
12use std::sync::{Arc, Mutex};
13use std::time::Instant;
14
15/// CUDA context management
16#[derive(Debug)]
17pub struct CudaContext {
18    /// Device ID
19    pub _device_id: i32,
20    /// Context handle (would be actual CUDA context in real implementation)
21    pub context_handle: usize,
22    /// Stream handles for asynchronous operations
23    pub streams: Vec<usize>,
24    /// Memory pool for efficient allocation
25    pub memory_pool: Arc<Mutex<CudaMemoryPool>>,
26    /// Device properties
27    pub device_props: CudaDeviceProperties,
28    /// CUDA runtime interface
29    pub runtime: Arc<Mutex<CudaRuntime>>,
30}
31
32/// CUDA device properties
33#[derive(Debug, Clone)]
34pub struct CudaDeviceProperties {
35    pub name: String,
36    pub major: i32,
37    pub minor: i32,
38    pub total_global_mem: usize,
39    pub shared_mem_per_block: usize,
40    pub max_threads_per_block: i32,
41    pub max_threads_dim: [i32; 3],
42    pub max_grid_size: [i32; 3],
43    pub warp_size: i32,
44    pub memory_pitch: usize,
45    pub max_threads_per_multiprocessor: i32,
46    pub multiprocessor_count: i32,
47    pub clock_rate: i32,
48    pub memory_clock_rate: i32,
49    pub memory_bus_width: i32,
50    pub l2_cache_size: i32,
51    pub texture_alignment: usize,
52    pub concurrent_kernels: bool,
53    pub compute_mode: i32,
54    pub unified_addressing: bool,
55}
56
57/// CUDA memory pool for efficient allocation
58#[derive(Debug)]
59pub struct CudaMemoryPool {
60    /// Available memory blocks
61    pub free_blocks: HashMap<usize, Vec<CudaMemoryBlock>>,
62    /// Allocated memory blocks
63    pub allocated_blocks: HashMap<usize, CudaMemoryBlock>,
64    /// Total allocated memory
65    pub total_allocated: usize,
66    /// Memory allocation limit
67    pub memory_limit: usize,
68}
69
70/// CUDA memory block
71#[derive(Debug, Clone)]
72pub struct CudaMemoryBlock {
73    /// Device pointer (would be actual CUDA device pointer)
74    pub ptr: usize,
75    /// Size in bytes
76    pub size: usize,
77    /// Allocation timestamp
78    pub allocated_at: Instant,
79}
80
81/// OpenCL context management
82#[derive(Debug)]
83pub struct OpenClContext {
84    /// Platform ID
85    pub platform_id: usize,
86    /// Device ID
87    pub _device_id: usize,
88    /// Context handle
89    pub context_handle: usize,
90    /// Command queue
91    pub command_queue: usize,
92    /// Compiled programs cache
93    pub program_cache: Arc<Mutex<HashMap<String, usize>>>,
94    /// Device info
95    pub device_info: OpenClDeviceInfo,
96    /// OpenCL runtime interface
97    pub runtime: Arc<Mutex<OpenClRuntime>>,
98}
99
100/// OpenCL device information
101#[derive(Debug, Clone)]
102pub struct OpenClDeviceInfo {
103    pub name: String,
104    pub vendor: String,
105    pub version: String,
106    pub profile: String,
107    pub global_mem_size: usize,
108    pub local_mem_size: usize,
109    pub max_work_group_size: usize,
110    pub max_work_item_dimensions: u32,
111    pub max_work_item_sizes: Vec<usize>,
112    pub max_compute_units: u32,
113    pub max_clock_frequency: u32,
114    pub address_bits: u32,
115    pub image_support: bool,
116    pub preferred_vector_width_float: u32,
117    pub preferred_vector_width_double: u32,
118}
119
120impl CudaMemoryPool {
121    /// Create new CUDA memory pool
122    pub fn new(memory_limit: usize) -> Self {
123        Self {
124            free_blocks: HashMap::new(),
125            allocated_blocks: HashMap::new(),
126            total_allocated: 0,
127            memory_limit,
128        }
129    }
130
131    /// Allocate memory block
132    pub fn allocate(&mut self, size: usize) -> Option<CudaMemoryBlock> {
133        if self.total_allocated + size > self.memory_limit {
134            return None;
135        }
136
137        // Check for available free block
138        if let Some(blocks) = self.free_blocks.get_mut(&size) {
139            if let Some(block) = blocks.pop() {
140                self.allocated_blocks.insert(block.ptr, block.clone());
141                return Some(block);
142            }
143        }
144
145        // Create new block
146        let ptr = self.total_allocated + 0x10000000; // Mock address
147        let block = CudaMemoryBlock {
148            ptr,
149            size,
150            allocated_at: Instant::now(),
151        };
152
153        self.total_allocated += size;
154        self.allocated_blocks.insert(ptr, block.clone());
155        Some(block)
156    }
157
158    /// Free memory block
159    pub fn free(&mut self, ptr: usize) -> bool {
160        if let Some(block) = self.allocated_blocks.remove(&ptr) {
161            self.total_allocated -= block.size;
162            self.free_blocks
163                .entry(block.size)
164                .or_insert_with(Vec::new)
165                .push(block);
166            true
167        } else {
168            false
169        }
170    }
171
172    /// Get memory usage statistics
173    pub fn get_stats(&self) -> CudaMemoryStats {
174        CudaMemoryStats {
175            total_allocated: self.total_allocated,
176            free_blocks: self.free_blocks.values().map(|v| v.len()).sum(),
177            allocated_blocks: self.allocated_blocks.len(),
178            memory_limit: self.memory_limit,
179        }
180    }
181}
182
183/// CUDA memory pool statistics
184#[derive(Debug, Clone)]
185pub struct CudaMemoryStats {
186    pub total_allocated: usize,
187    pub free_blocks: usize,
188    pub allocated_blocks: usize,
189    pub memory_limit: usize,
190}
scirs2_metrics/optimization/gpu_kernels/contexts.rs

scirs2_metrics/optimization/gpu_kernels/
contexts.rs
