scirs2_graph/advanced/
mod.rs

1//! Advanced Mode Integration for Graph Processing
2//!
3//! This module provides cutting-edge optimization capabilities by integrating
4//! neural reinforcement learning, GPU acceleration, neuromorphic computing,
5//! and real-time adaptive optimization for graph algorithms.
6
7use crate::base::{EdgeWeight, Graph, Node};
8use crate::error::Result;
9// Simplified performance monitoring
10#[derive(Debug, Clone, Default)]
11pub struct SimplePerformanceMonitor {
12    operations: HashMap<String, f64>,
13}
14
15impl SimplePerformanceMonitor {
16    pub fn new() -> Self {
17        Self::default()
18    }
19
20    pub fn start_operation(&mut self, _name: &str) {
21        // Placeholder implementation
22    }
23
24    pub fn stop_operation(&mut self, _name: &str) {
25        // Placeholder implementation
26    }
27
28    pub fn get_report(&self) -> SimplePerformanceReport {
29        SimplePerformanceReport::default()
30    }
31}
32
33#[derive(Debug, Clone, Default)]
34pub struct SimplePerformanceReport {
35    pub total_operations: usize,
36    pub total_time_ms: f64,
37}
38use rand::Rng;
39use std::collections::{HashMap, VecDeque};
40
41/// Advanced mode configuration for graph processing
42#[derive(Debug, Clone)]
43pub struct AdvancedConfig {
44    /// Enable neural RL-based algorithm selection
45    pub enable_neural_rl: bool,
46    /// Enable GPU advanced-acceleration
47    pub enable_gpu_acceleration: bool,
48    /// Enable neuromorphic computing features
49    pub enable_neuromorphic: bool,
50    /// Enable real-time performance adaptation
51    pub enable_realtime_adaptation: bool,
52    /// Enable advanced memory optimization
53    pub enable_memory_optimization: bool,
54    /// Learning rate for adaptive algorithms
55    pub learning_rate: f64,
56    /// Memory optimization threshold (MB)
57    pub memory_threshold_mb: usize,
58    /// GPU memory pool size (MB)
59    pub gpu_memory_pool_mb: usize,
60    /// Neural network hidden layer size
61    pub neural_hidden_size: usize,
62}
63
64impl Default for AdvancedConfig {
65    fn default() -> Self {
66        AdvancedConfig {
67            enable_neural_rl: true,
68            enable_gpu_acceleration: true,
69            enable_neuromorphic: true,
70            enable_realtime_adaptation: true,
71            enable_memory_optimization: true,
72            learning_rate: 0.001,
73            memory_threshold_mb: 1024,
74            gpu_memory_pool_mb: 2048,
75            neural_hidden_size: 128,
76        }
77    }
78}
79
80/// Advanced exploration strategies for neural RL
81#[derive(Debug, Clone)]
82pub enum ExplorationStrategy {
83    /// Standard epsilon-greedy exploration
84    EpsilonGreedy {
85        /// Exploration probability parameter
86        epsilon: f64,
87    },
88    /// Upper confidence bound exploration
89    UCB {
90        /// Confidence parameter for UCB
91        c: f64,
92    },
93    /// Thompson sampling exploration
94    ThompsonSampling {
95        /// Alpha parameter for beta distribution
96        alpha: f64,
97        /// Beta parameter for beta distribution
98        beta: f64,
99    },
100    /// Adaptive exploration based on uncertainty
101    AdaptiveUncertainty {
102        /// Uncertainty threshold for adaptive exploration
103        uncertainty_threshold: f64,
104    },
105}
106
107impl Default for ExplorationStrategy {
108    fn default() -> Self {
109        ExplorationStrategy::EpsilonGreedy { epsilon: 0.1 }
110    }
111}
112
113/// Simplified Advanced Processing Structure
114pub struct AdvancedProcessor {
115    config: AdvancedConfig,
116    performance_monitor: SimplePerformanceMonitor,
117}
118
119impl AdvancedProcessor {
120    /// Create a new advanced processor
121    pub fn new(config: AdvancedConfig) -> Self {
122        AdvancedProcessor {
123            config,
124            performance_monitor: SimplePerformanceMonitor::new(),
125        }
126    }
127
128    /// Execute advanced graph processing
129    pub fn execute<N, E, Ix, T, F>(&mut self, graph: &Graph<N, E, Ix>, operation: F) -> Result<T>
130    where
131        N: Node,
132        E: EdgeWeight,
133        Ix: petgraph::graph::IndexType,
134        F: FnOnce(&Graph<N, E, Ix>) -> Result<T>,
135    {
136        // Start performance monitoring
137        self.performance_monitor
138            .start_operation("advanced_execution");
139
140        // Execute the operation
141        let result = operation(graph);
142
143        // Stop performance monitoring
144        self.performance_monitor
145            .stop_operation("advanced_execution");
146
147        result
148    }
149
150    /// Get performance report
151    pub fn get_performance_report(&self) -> SimplePerformanceReport {
152        self.performance_monitor.get_report()
153    }
154
155    /// Get optimization statistics
156    pub fn get_optimization_stats(&self) -> AdvancedStats {
157        AdvancedStats::default()
158    }
159}
160
161/// Advanced statistics for graph processing
162#[derive(Debug, Clone)]
163pub struct AdvancedStats {
164    /// Total operations executed
165    pub total_operations: usize,
166    /// Average execution time in milliseconds
167    pub avg_execution_time_ms: f64,
168    /// Memory usage in bytes
169    pub memory_usage_bytes: usize,
170    /// GPU utilization percentage
171    pub gpu_utilization_percent: f64,
172    /// Memory efficiency score (0.0 to 1.0)
173    pub memory_efficiency: f64,
174}
175
176impl Default for AdvancedStats {
177    fn default() -> Self {
178        AdvancedStats {
179            total_operations: 0,
180            avg_execution_time_ms: 0.0,
181            memory_usage_bytes: 0,
182            gpu_utilization_percent: 0.0,
183            memory_efficiency: 1.0,
184        }
185    }
186}
187
188// Factory functions for different processor configurations
189/// Create a standard advanced processor
190pub fn create_advanced_processor() -> AdvancedProcessor {
191    AdvancedProcessor::new(AdvancedConfig::default())
192}
193
194/// Create an enhanced advanced processor with optimized settings
195pub fn create_enhanced_advanced_processor() -> AdvancedProcessor {
196    let mut config = AdvancedConfig::default();
197    config.neural_hidden_size = 256;
198    config.gpu_memory_pool_mb = 4096;
199    AdvancedProcessor::new(config)
200}
201
202/// Execute operation with standard advanced processing
203pub fn execute_with_advanced<N, E, Ix, T>(
204    graph: &Graph<N, E, Ix>,
205    operation: impl FnOnce(&Graph<N, E, Ix>) -> Result<T>,
206) -> Result<T>
207where
208    N: Node,
209    E: EdgeWeight,
210    Ix: petgraph::graph::IndexType,
211{
212    let mut processor = create_advanced_processor();
213    processor.execute(graph, operation)
214}
215
216/// Execute operation with enhanced advanced processing
217pub fn execute_with_enhanced_advanced<N, E, Ix, T>(
218    graph: &Graph<N, E, Ix>,
219    operation: impl FnOnce(&Graph<N, E, Ix>) -> Result<T>,
220) -> Result<T>
221where
222    N: Node,
223    E: EdgeWeight,
224    Ix: petgraph::graph::IndexType,
225{
226    let mut processor = create_enhanced_advanced_processor();
227    processor.execute(graph, operation)
228}
229
230/// Create a processor optimized for large graphs
231pub fn create_large_graph_advanced_processor() -> AdvancedProcessor {
232    let mut config = AdvancedConfig::default();
233    config.memory_threshold_mb = 8192;
234    config.gpu_memory_pool_mb = 8192;
235    config.enable_memory_optimization = true;
236    AdvancedProcessor::new(config)
237}
238
239/// Create a processor optimized for real-time processing
240pub fn create_realtime_advanced_processor() -> AdvancedProcessor {
241    let mut config = AdvancedConfig::default();
242    config.enable_realtime_adaptation = true;
243    config.learning_rate = 0.01;
244    AdvancedProcessor::new(config)
245}
246
247/// Create a processor optimized for performance
248pub fn create_performance_advanced_processor() -> AdvancedProcessor {
249    let mut config = AdvancedConfig::default();
250    config.enable_gpu_acceleration = true;
251    config.enable_neuromorphic = true;
252    config.gpu_memory_pool_mb = 16384;
253    AdvancedProcessor::new(config)
254}
255
256/// Create a processor optimized for memory efficiency
257pub fn create_memory_efficient_advanced_processor() -> AdvancedProcessor {
258    let mut config = AdvancedConfig::default();
259    config.enable_memory_optimization = true;
260    config.memory_threshold_mb = 512;
261    config.gpu_memory_pool_mb = 1024;
262    AdvancedProcessor::new(config)
263}
264
265/// Create an adaptive processor that adjusts based on workload
266pub fn create_adaptive_advanced_processor() -> AdvancedProcessor {
267    let mut config = AdvancedConfig::default();
268    config.enable_realtime_adaptation = true;
269    config.enable_neural_rl = true;
270    config.learning_rate = 0.005;
271    AdvancedProcessor::new(config)
272}
273
274// Placeholder structures for backward compatibility
275/// Algorithm performance metrics
276#[derive(Debug, Clone)]
277pub struct AlgorithmMetrics {
278    /// Algorithm name
279    pub algorithm_name: String,
280    /// Execution time in milliseconds
281    pub execution_time_ms: f64,
282    /// Memory usage in bytes
283    pub memory_usage_bytes: usize,
284}
285
286impl Default for AlgorithmMetrics {
287    fn default() -> Self {
288        AlgorithmMetrics {
289            algorithm_name: String::new(),
290            execution_time_ms: 0.0,
291            memory_usage_bytes: 0,
292        }
293    }
294}
295
296/// GPU acceleration context for advanced operations
297#[derive(Debug)]
298pub struct GPUAccelerationContext {
299    /// Whether GPU is available
300    pub gpu_available: bool,
301    /// GPU memory pool size
302    pub memory_pool_size: usize,
303}
304
305impl Default for GPUAccelerationContext {
306    fn default() -> Self {
307        GPUAccelerationContext {
308            gpu_available: false,
309            memory_pool_size: 0,
310        }
311    }
312}
313
314/// Neural reinforcement learning agent
315#[derive(Debug)]
316pub struct NeuralRLAgent {
317    /// Agent configuration
318    pub config: AdvancedConfig,
319    /// Learning rate
320    pub learning_rate: f64,
321}
322
323impl Default for NeuralRLAgent {
324    fn default() -> Self {
325        NeuralRLAgent {
326            config: AdvancedConfig::default(),
327            learning_rate: 0.001,
328        }
329    }
330}
331
332/// Neuromorphic processor for brain-inspired computing
333#[derive(Debug)]
334pub struct NeuromorphicProcessor {
335    /// Number of neurons
336    pub num_neurons: usize,
337    /// Number of synapses
338    pub num_synapses: usize,
339}
340
341impl Default for NeuromorphicProcessor {
342    fn default() -> Self {
343        NeuromorphicProcessor {
344            num_neurons: 1000,
345            num_synapses: 10000,
346        }
347    }
348}
scirs2_graph/advanced/mod.rs

scirs2_graph/advanced/
mod.rs
