scirs2_linalg/gpu/advanced/
kernels.rs

1//! Advanced GPU kernel fusion and kernel management
2//!
3//! This module implements cutting-edge GPU acceleration techniques including:
4//! - Dynamic kernel fusion for complex operation chains
5//! - Kernel optimization and compilation
6//! - Performance modeling and prediction
7
8use crate::error::{LinalgError, LinalgResult};
9use crate::gpu::operations::kernels::GpuKernelManager;
10use crate::gpu::{GpuBackend, GpuContext, GpuDeviceType};
11use scirs2_core::ndarray::{Array1, Array2, ArrayView1, ArrayView2};
12use scirs2_core::numeric::{Float, NumAssign, Zero};
13use std::collections::{HashMap, VecDeque};
14use std::fmt::Debug;
15use std::sync::{Arc, Mutex, RwLock};
16
17/// Advanced-advanced GPU kernel fusion engine
18pub struct AdvancedGpuKernelFusion<T>
19where
20    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
21{
22    /// Operation dependency graph
23    pub operation_graph: Arc<RwLock<OperationDependencyGraph<T>>>,
24    /// Kernel fusion optimizer
25    pub fusion_optimizer: Arc<Mutex<KernelFusionEngine>>,
26}
27
28/// Operation dependency graph for kernel fusion
29#[derive(Debug)]
30pub struct OperationDependencyGraph<T> {
31    /// Graph nodes representing operations
32    pub nodes: Vec<OperationNode<T>>,
33    /// Dependency edges between operations
34    pub edges: Vec<DependencyEdge>,
35    /// Fusion opportunities
36    pub fusion_candidates: Vec<FusionCandidate>,
37}
38
39/// Individual operation node in the dependency graph
40#[derive(Debug)]
41pub struct OperationNode<T> {
42    /// Unique operation ID
43    pub id: usize,
44    /// Operation type
45    pub op_type: GpuOperationType,
46    /// Input tensor shapes
47    pub input_shapes: Vec<TensorShape>,
48    /// Output tensor shape
49    pub output_shape: TensorShape,
50    /// Memory requirements
51    pub memory_requirements: MemoryRequirements,
52    /// Execution cost estimate
53    pub cost_estimate: f64,
54    /// Kernel specifications
55    pub kernel_spec: KernelSpecification<T>,
56}
57
58/// GPU operation types supported for fusion
59#[derive(Debug, Clone, PartialEq, Eq, Hash)]
60pub enum GpuOperationType {
61    MatrixMultiplication,
62    MatrixAddition,
63    MatrixSubtraction,
64    ElementwiseMultiplication,
65    ElementwiseAddition,
66    ElementwiseDivision,
67    MatrixTranspose,
68    VectorNorm,
69    MatrixNorm,
70    Reduction,
71    BroadcastOperation,
72    ConvolutionalOperation,
73    Convolution,
74    ActivationFunction,
75    BatchNormalization,
76    Transpose,
77    Normalization,
78    Custom(String),
79}
80
81/// Tensor shape representation
82#[derive(Debug, Clone, PartialEq)]
83pub struct TensorShape {
84    pub dimensions: Vec<usize>,
85    pub element_type: ElementType,
86    pub memory_layout: MemoryLayout,
87}
88
89/// Element types supported
90#[derive(Debug, Clone, PartialEq)]
91pub enum ElementType {
92    F32,
93    F64,
94    F16,
95    BF16,
96    Int32,
97    Int16,
98    Int8,
99    UInt8,
100}
101
102/// Memory layout types
103#[derive(Debug, Clone, PartialEq)]
104pub enum MemoryLayout {
105    RowMajor,
106    ColumnMajor,
107    Blocked(usize, usize),
108    Custom(String),
109}
110
111/// Memory requirements for an operation
112#[derive(Debug, Clone)]
113pub struct MemoryRequirements {
114    /// Input memory requirement in bytes
115    pub input_memory: usize,
116    /// Output memory requirement in bytes
117    pub output_memory: usize,
118    /// Temporary memory requirement in bytes
119    pub temp_memory: usize,
120    /// Memory bandwidth requirement in GB/s
121    pub bandwidth_requirement: f64,
122}
123
124/// Kernel specification for GPU operations
125#[derive(Debug)]
126pub struct KernelSpecification<T> {
127    /// Kernel name
128    pub name: String,
129    /// Thread block dimensions
130    pub block_dims: (u32, u32, u32),
131    /// Grid dimensions
132    pub grid_dims: (u32, u32, u32),
133    /// Shared memory requirement
134    pub shared_memory: usize,
135    /// Register requirement per thread
136    pub registers_per_thread: u32,
137    /// Kernel parameters
138    pub parameters: Vec<KernelParameter<T>>,
139}
140
141/// Kernel parameters
142#[derive(Debug)]
143pub enum KernelParameter<T> {
144    Scalar(T),
145    Vector(Vec<T>),
146    Matrix(Array2<T>),
147    Pointer(*mut T),
148}
149
150/// Dependency edge between operations
151#[derive(Debug, Clone)]
152pub struct DependencyEdge {
153    /// Source operation ID
154    pub source: usize,
155    /// Target operation ID
156    pub target: usize,
157    /// Data dependency type
158    pub dependency_type: DependencyType,
159    /// Data size flowing through the edge
160    pub data_size: usize,
161}
162
163/// Types of dependencies between operations
164#[derive(Debug, Clone, PartialEq)]
165pub enum DependencyType {
166    /// True data dependency (RAW - Read After Write)
167    TrueDependency,
168    /// Anti-dependency (WAR - Write After Read)
169    AntiDependency,
170    /// Output dependency (WAW - Write After Write)
171    OutputDependency,
172    /// Control dependency
173    ControlDependency,
174}
175
176/// Fusion candidate representing operations that can be fused
177#[derive(Debug, Clone)]
178pub struct FusionCandidate {
179    /// Operations to fuse
180    pub operations: Vec<usize>,
181    /// Expected performance benefit
182    pub benefit_score: f64,
183    /// Memory savings estimate
184    pub memory_savings: usize,
185    /// Fusion complexity
186    pub complexity: f64,
187}
188
189/// Kernel fusion engine
190#[derive(Debug)]
191pub struct KernelFusionEngine {
192    /// Fusion strategies
193    fusion_strategies: Vec<FusionStrategy>,
194    /// Fusion rules
195    fusion_rules: FusionRuleSet,
196    /// Performance models
197    performance_models: HashMap<String, PerformanceModel>,
198    /// Optimization parameters
199    optimization_params: FusionOptimizationParams,
200}
201
202/// Kernel fusion strategies
203#[derive(Debug, Clone)]
204pub enum FusionStrategy {
205    /// Fuse elementwise operations
206    ElementwiseFusion,
207    /// Fuse matrix operations
208    MatrixOperationFusion,
209    /// Fuse reduction operations
210    ReductionFusion,
211    /// Fuse memory-bound operations
212    MemoryBoundFusion,
213    /// Fuse compute-bound operations
214    ComputeBoundFusion,
215    /// Custom fusion strategy
216    Custom(String),
217}
218
219/// Fusion rule set
220#[derive(Debug)]
221pub struct FusionRuleSet {
222    /// Compatibility rules between operation types
223    compatibility_rules: HashMap<(GpuOperationType, GpuOperationType), bool>,
224    /// Memory constraint rules
225    memory_rules: Vec<MemoryConstraintRule>,
226    /// Performance constraint rules
227    performance_rules: Vec<PerformanceConstraintRule>,
228}
229
230/// Memory constraint rule for fusion
231#[derive(Debug)]
232pub struct MemoryConstraintRule {
233    /// Maximum memory usage for fused operation
234    pub max_memory: usize,
235    /// Maximum number of operations to fuse
236    pub max_operations: usize,
237    /// Memory hierarchy considerations
238    pub memory_hierarchy: MemoryHierarchyConstraint,
239}
240
241/// Memory hierarchy constraints
242#[derive(Debug)]
243pub struct MemoryHierarchyConstraint {
244    /// L1 cache limit
245    pub l1_cache_limit: usize,
246    /// L2 cache limit
247    pub l2_cache_limit: usize,
248    /// Shared memory limit
249    pub shared_memory_limit: usize,
250    /// Global memory bandwidth
251    pub global_memory_bandwidth: f64,
252}
253
254/// Performance constraint rule
255#[derive(Debug)]
256pub struct PerformanceConstraintRule {
257    /// Minimum performance improvement required
258    pub min_improvement: f64,
259    /// Maximum fusion complexity allowed
260    pub max_complexity: f64,
261    /// Thread divergence threshold
262    pub divergence_threshold: f64,
263}
264
265/// Performance model for operations
266#[derive(Debug)]
267pub struct PerformanceModel {
268    /// Execution time predictor
269    pub execution_time_fn: fn(&TensorShape, &TensorShape) -> f64,
270    /// Memory bandwidth utilization
271    pub bandwidth_utilization: f64,
272    /// Compute utilization
273    pub compute_utilization: f64,
274    /// Accuracy of the model
275    pub model_accuracy: f64,
276}
277
278/// Fusion optimization parameters
279#[derive(Debug)]
280pub struct FusionOptimizationParams {
281    /// Weight for performance improvement
282    pub performance_weight: f64,
283    /// Weight for memory savings
284    pub memory_weight: f64,
285    /// Weight for complexity penalty
286    pub complexity_weight: f64,
287    /// Maximum fusion depth
288    pub max_fusion_depth: usize,
289    /// Enable aggressive optimization
290    pub aggressive_optimization: bool,
291}
292
293impl<T> AdvancedGpuKernelFusion<T>
294where
295    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
296{
297    pub fn new() -> LinalgResult<Self> {
298        Ok(Self {
299            operation_graph: Arc::new(RwLock::new(OperationDependencyGraph::new())),
300            fusion_optimizer: Arc::new(Mutex::new(KernelFusionEngine::new()?)),
301        })
302    }
303
304    /// Add operation to the fusion graph
305    pub fn add_operation(&self, operation: OperationNode<T>) -> LinalgResult<usize> {
306        let mut graph = self.operation_graph.write().expect("Operation failed");
307        let id = operation.id;
308        graph.nodes.push(operation);
309        Ok(id)
310    }
311
312    /// Add dependency between operations
313    pub fn add_dependency(&self, edge: DependencyEdge) -> LinalgResult<()> {
314        let mut graph = self.operation_graph.write().expect("Operation failed");
315        graph.edges.push(edge);
316        Ok(())
317    }
318
319    /// Analyze fusion opportunities
320    pub fn analyze_fusion_opportunities(&self) -> LinalgResult<Vec<FusionCandidate>> {
321        let graph = self.operation_graph.read().expect("Operation failed");
322        let optimizer = self.fusion_optimizer.lock().expect("Operation failed");
323
324        let mut candidates = Vec::new();
325
326        // Find connected components that can be fused
327        for (i, node1) in graph.nodes.iter().enumerate() {
328            for (j, node2) in graph.nodes.iter().enumerate().skip(i + 1) {
329                if optimizer.can_fuse_operations(node1, node2) {
330                    let benefit = optimizer.estimate_fusion_benefit(node1, node2);
331                    let memory_savings = optimizer.estimate_memory_savings(node1, node2);
332
333                    candidates.push(FusionCandidate {
334                        operations: vec![node1.id, node2.id],
335                        benefit_score: benefit,
336                        memory_savings,
337                        complexity: 1.0, // Simple binary fusion
338                    });
339                }
340            }
341        }
342
343        Ok(candidates)
344    }
345}
346
347impl<T> OperationDependencyGraph<T> {
348    pub fn new() -> Self {
349        Self {
350            nodes: Vec::new(),
351            edges: Vec::new(),
352            fusion_candidates: Vec::new(),
353        }
354    }
355}
356
357impl KernelFusionEngine {
358    pub fn new() -> LinalgResult<Self> {
359        Ok(Self {
360            fusion_strategies: vec![
361                FusionStrategy::ElementwiseFusion,
362                FusionStrategy::MatrixOperationFusion,
363                FusionStrategy::ReductionFusion,
364            ],
365            fusion_rules: FusionRuleSet::default(),
366            performance_models: HashMap::new(),
367            optimization_params: FusionOptimizationParams::default(),
368        })
369    }
370
371    fn can_fuse_operations<T>(&self, op1: &OperationNode<T>, op2: &OperationNode<T>) -> bool {
372        // Check if operations are compatible for fusion
373        match (&op1.op_type, &op2.op_type) {
374            (
375                GpuOperationType::ElementwiseAddition,
376                GpuOperationType::ElementwiseMultiplication,
377            ) => true,
378            (GpuOperationType::MatrixMultiplication, GpuOperationType::MatrixAddition) => true,
379            (GpuOperationType::MatrixTranspose, GpuOperationType::MatrixMultiplication) => true,
380            _ => false,
381        }
382    }
383
384    fn estimate_fusion_benefit<T>(&self, op1: &OperationNode<T>, op2: &OperationNode<T>) -> f64 {
385        // Simplified performance benefit estimation
386        let memory_transfer_saved =
387            op1.output_shape.dimensions.iter().product::<usize>() as f64 * 4.0;
388        memory_transfer_saved / 1e9 // Benefit in GB/s saved
389    }
390
391    fn estimate_memory_savings<T>(&self, op1: &OperationNode<T>, op2: &OperationNode<T>) -> usize {
392        // Memory saved by not storing intermediate result
393        op1.output_shape.dimensions.iter().product::<usize>() * 4
394    }
395}
396
397// Default implementations
398impl Default for FusionRuleSet {
399    fn default() -> Self {
400        Self {
401            compatibility_rules: HashMap::new(),
402            memory_rules: Vec::new(),
403            performance_rules: Vec::new(),
404        }
405    }
406}
407
408impl Default for FusionOptimizationParams {
409    fn default() -> Self {
410        Self {
411            performance_weight: 0.5,
412            memory_weight: 0.3,
413            complexity_weight: 0.2,
414            max_fusion_depth: 5,
415            aggressive_optimization: false,
416        }
417    }
418}
419
420#[cfg(test)]
421mod tests {
422    use super::*;
423
424    #[test]
425    fn test_kernel_fusion_engine_creation() {
426        let engine = KernelFusionEngine::new().expect("Operation failed");
427        assert_eq!(engine.fusion_strategies.len(), 3);
428    }
429
430    #[test]
431    fn test_operation_dependency_graph() {
432        let graph = OperationDependencyGraph::<f32>::new();
433        assert!(graph.nodes.is_empty());
434        assert!(graph.edges.is_empty());
435    }
436
437    #[test]
438    fn test_advanced_gpu_kernel_fusion_creation() {
439        let fusion = AdvancedGpuKernelFusion::<f32>::new().expect("Operation failed");
440        assert!(fusion
441            .operation_graph
442            .read()
443            .expect("Operation failed")
444            .nodes
445            .is_empty());
446    }
447}
scirs2_linalg/gpu/advanced/kernels.rs

scirs2_linalg/gpu/advanced/
kernels.rs
