scirs2_linalg/gpu/advanced/
mod.rs

1//! Advanced GPU acceleration features and optimizations
2//!
3//! This module implements cutting-edge GPU acceleration techniques including:
4//! - Dynamic kernel fusion for complex operation chains
5//! - Multi-GPU tensor core optimization
6//! - Predictive memory bandwidth optimization
7//! - Asynchronous operation pipelining with dependency resolution
8//! - Advanced scheduling algorithms for optimal resource utilization
9//!
10//! ## Module Organization
11//!
12//! - **kernels**: Kernel fusion engine and operation graph management
13//! - **memory**: Advanced memory management and bandwidth prediction
14//! - **optimization**: Multi-GPU coordination and optimization strategies
15//! - **scheduling**: Tensor core scheduling and performance monitoring
16//!
17//! ## Usage
18//!
19//! ```rust,no_run
20//! # fn main() -> Result<(), Box<dyn std::error::Error>> {
21//! use scirs2_linalg::gpu::advanced::*;
22//!
23//! // Create kernel fusion engine
24//! let fusion_engine = AdvancedGpuKernelFusion::<f32>::new()?;
25//!
26//! // Set up multi-GPU coordinator
27//! let mut coordinator = AdvancedMultiGpuCoordinator::new()?;
28//!
29//! // Create tensor core scheduler
30//! let mut scheduler = AdvancedGpuTensorCoreScheduler::<f32>::new()?;
31//! # Ok(())
32//! # }
33//! ```
34
35// Declare submodules
36pub mod kernels;
37pub mod memory;
38pub mod optimization;
39pub mod scheduling;
40
41// Re-export main types for convenience
42pub use kernels::{
43    AdvancedGpuKernelFusion, DependencyEdge, DependencyType, ElementType, FusionCandidate,
44    FusionOptimizationParams, FusionRuleSet, FusionStrategy, GpuOperationType, KernelFusionEngine,
45    KernelSpecification, MemoryLayout, MemoryRequirements, OperationDependencyGraph, OperationNode,
46    PerformanceModel, TensorShape,
47};
48
49pub use memory::{
50    BandwidthMeasurement, BandwidthPredictionModel, BandwidthPredictor, GCStats, GCStrategy,
51    GpuMemoryManager, MemoryAccessPattern, MemoryAllocationStrategy, MemoryBlock,
52    MemoryGarbageCollector, MemoryPool, MemoryPoolType, MemoryStats, TensorCorePrecision,
53};
54
55pub use optimization::{
56    AdvancedMultiGpuCoordinator, BandwidthAllocationPolicy, BandwidthAllocator,
57    CommOptimizationAlgorithm, CommunicationPattern, DynamicLoadBalancer, GpuConnection, GpuInfo,
58    GpuTopologyMap, GpuWorkPartition, IntelligentPartitioner, InterGpuCommOptimizer,
59    InterGpuConnectionType, LoadBalancingAlgorithm, LoadMonitor, MigrationCostModel,
60    MigrationPolicy, MigrationStrategy, MigrationTrigger, PartitioningCostModel,
61    PartitioningPerformanceRecord, PartitioningStrategy, WorkloadCharacteristics,
62};
63
64pub use scheduling::{
65    AdvancedGpuTensorCoreScheduler, OperationAnalysis, SchedulingStats, TensorCoreOpType,
66    TensorCoreOperation, TensorCorePerformanceMonitor, TensorCoreSchedulingAlgorithm,
67    TensorCoreUnit,
68};
69
70use crate::error::{LinalgError, LinalgResult};
71
72/// Unified advanced GPU acceleration framework
73pub struct AdvancedGpuAccelerationFramework<T>
74where
75    T: scirs2_core::numeric::Float
76        + scirs2_core::numeric::NumAssign
77        + scirs2_core::numeric::Zero
78        + Send
79        + Sync
80        + std::fmt::Debug
81        + 'static,
82{
83    /// Kernel fusion engine
84    pub fusion_engine: AdvancedGpuKernelFusion<T>,
85    /// Multi-GPU coordinator
86    pub multi_gpu_coordinator: AdvancedMultiGpuCoordinator,
87    /// Tensor core scheduler
88    pub tensor_scheduler: AdvancedGpuTensorCoreScheduler<T>,
89    /// Memory manager
90    pub memory_manager: GpuMemoryManager,
91    /// Bandwidth predictor
92    pub bandwidth_predictor: BandwidthPredictor,
93}
94
95impl<T> AdvancedGpuAccelerationFramework<T>
96where
97    T: scirs2_core::numeric::Float
98        + scirs2_core::numeric::NumAssign
99        + scirs2_core::numeric::Zero
100        + Send
101        + Sync
102        + std::fmt::Debug
103        + 'static,
104{
105    /// Create a new advanced GPU acceleration framework
106    pub fn new(gpu_id: usize) -> LinalgResult<Self> {
107        Ok(Self {
108            fusion_engine: AdvancedGpuKernelFusion::new()?,
109            multi_gpu_coordinator: AdvancedMultiGpuCoordinator::new()?,
110            tensor_scheduler: AdvancedGpuTensorCoreScheduler::new()?,
111            memory_manager: GpuMemoryManager::new(gpu_id)?,
112            bandwidth_predictor: BandwidthPredictor::new(),
113        })
114    }
115
116    /// Perform comprehensive GPU optimization
117    pub fn optimize_execution(&mut self) -> LinalgResult<()> {
118        // 1. Analyze fusion opportunities
119        let fusion_candidates = self.fusion_engine.analyze_fusion_opportunities()?;
120
121        // 2. Optimize multi-GPU communication
122        self.multi_gpu_coordinator.optimize_communication()?;
123
124        // 3. Balance load across GPUs
125        self.multi_gpu_coordinator.balance_load()?;
126
127        // 4. Collect garbage memory
128        self.memory_manager.collect_garbage()?;
129
130        Ok(())
131    }
132
133    /// Get comprehensive performance statistics
134    pub fn get_performance_stats(&self) -> AdvancedPerformanceStats {
135        AdvancedPerformanceStats {
136            scheduling_stats: self.tensor_scheduler.get_performance_stats(),
137            memory_stats: self.memory_manager.get_memory_stats(),
138            bandwidth_prediction_accuracy: self.bandwidth_predictor.accuracy,
139            total_fusion_candidates: self
140                .fusion_engine
141                .operation_graph
142                .read()
143                .expect("Operation failed")
144                .fusion_candidates
145                .len(),
146        }
147    }
148}
149
150/// Comprehensive performance statistics
151#[derive(Debug, Clone)]
152pub struct AdvancedPerformanceStats {
153    /// Tensor core scheduling statistics
154    pub scheduling_stats: SchedulingStats,
155    /// Memory management statistics
156    pub memory_stats: MemoryStats,
157    /// Bandwidth prediction accuracy
158    pub bandwidth_prediction_accuracy: f64,
159    /// Total number of fusion candidates identified
160    pub total_fusion_candidates: usize,
161}
162
163/// Initialize the global advanced GPU acceleration framework
164pub fn initialize_advanced_gpu_acceleration<T>(
165    gpu_id: usize,
166) -> LinalgResult<AdvancedGpuAccelerationFramework<T>>
167where
168    T: scirs2_core::numeric::Float
169        + scirs2_core::numeric::NumAssign
170        + scirs2_core::numeric::Zero
171        + Send
172        + Sync
173        + std::fmt::Debug
174        + 'static,
175{
176    AdvancedGpuAccelerationFramework::new(gpu_id)
177}
178
179/// Get optimization recommendations based on current state
180pub fn get_optimization_recommendations(
181    stats: &AdvancedPerformanceStats,
182) -> Vec<OptimizationRecommendation> {
183    let mut recommendations = Vec::new();
184
185    // Memory recommendations
186    if stats.memory_stats.fragmentation_count > 100 {
187        recommendations.push(OptimizationRecommendation {
188            category: RecommendationCategory::Memory,
189            description: "High memory fragmentation detected. Consider running garbage collection."
190                .to_string(),
191            priority: RecommendationPriority::High,
192            estimated_benefit: 0.3,
193        });
194    }
195
196    // Scheduling recommendations
197    if stats.scheduling_stats.tensor_core_utilization < 0.5 {
198        recommendations.push(OptimizationRecommendation {
199            category: RecommendationCategory::Scheduling,
200            description: "Low tensor core utilization. Consider batching smaller operations."
201                .to_string(),
202            priority: RecommendationPriority::Medium,
203            estimated_benefit: 0.4,
204        });
205    }
206
207    // Bandwidth prediction recommendations
208    if stats.bandwidth_prediction_accuracy < 0.7 {
209        recommendations.push(OptimizationRecommendation {
210            category: RecommendationCategory::Prediction,
211            description: "Low bandwidth prediction accuracy. Consider updating prediction models."
212                .to_string(),
213            priority: RecommendationPriority::Low,
214            estimated_benefit: 0.2,
215        });
216    }
217
218    // Fusion recommendations
219    if stats.total_fusion_candidates > 50 {
220        recommendations.push(OptimizationRecommendation {
221            category: RecommendationCategory::Fusion,
222            description:
223                "Many fusion opportunities available. Enable aggressive fusion optimization."
224                    .to_string(),
225            priority: RecommendationPriority::High,
226            estimated_benefit: 0.5,
227        });
228    }
229
230    recommendations
231}
232
233/// Optimization recommendation
234#[derive(Debug, Clone)]
235pub struct OptimizationRecommendation {
236    /// Recommendation category
237    pub category: RecommendationCategory,
238    /// Description of the recommendation
239    pub description: String,
240    /// Priority level
241    pub priority: RecommendationPriority,
242    /// Estimated performance benefit (0.0-1.0)
243    pub estimated_benefit: f64,
244}
245
246/// Categories of optimization recommendations
247#[derive(Debug, Clone, PartialEq)]
248pub enum RecommendationCategory {
249    /// Memory management optimization
250    Memory,
251    /// Scheduling optimization
252    Scheduling,
253    /// Prediction model optimization
254    Prediction,
255    /// Kernel fusion optimization
256    Fusion,
257    /// Multi-GPU coordination
258    MultiGpu,
259}
260
261/// Priority levels for recommendations
262#[derive(Debug, Clone, PartialEq, PartialOrd)]
263pub enum RecommendationPriority {
264    /// Low priority recommendation
265    Low,
266    /// Medium priority recommendation
267    Medium,
268    /// High priority recommendation
269    High,
270    /// Critical priority recommendation
271    Critical,
272}
273
274#[cfg(test)]
275mod tests {
276    use super::*;
277
278    #[test]
279    fn test_advanced_framework_creation() {
280        let framework = AdvancedGpuAccelerationFramework::<f32>::new(0).expect("Operation failed");
281        assert_eq!(framework.memory_manager.gpu_id, 0);
282    }
283
284    #[test]
285    fn test_optimization_recommendations() {
286        let stats = AdvancedPerformanceStats {
287            scheduling_stats: SchedulingStats {
288                average_throughput: 100.0,
289                average_latency: 0.01,
290                total_operations_scheduled: 1000,
291                tensor_core_utilization: 0.3, // Low utilization
292            },
293            memory_stats: MemoryStats {
294                total_allocated: 1024 * 1024,
295                total_free: 512 * 1024,
296                fragmentation_count: 150, // High fragmentation
297                pool_count: 4,
298                gc_stats: GCStats::new(),
299            },
300            bandwidth_prediction_accuracy: 0.85,
301            total_fusion_candidates: 25,
302        };
303
304        let recommendations = get_optimization_recommendations(&stats);
305        assert!(!recommendations.is_empty());
306
307        // Should have memory and scheduling recommendations
308        assert!(recommendations
309            .iter()
310            .any(|r| r.category == RecommendationCategory::Memory));
311        assert!(recommendations
312            .iter()
313            .any(|r| r.category == RecommendationCategory::Scheduling));
314    }
315
316    #[test]
317    fn test_initialize_advanced_gpu_acceleration() {
318        let framework = initialize_advanced_gpu_acceleration::<f32>(0).expect("Operation failed");
319        assert_eq!(framework.memory_manager.gpu_id, 0);
320    }
321}
scirs2_linalg/gpu/advanced/mod.rs

scirs2_linalg/gpu/advanced/
mod.rs
