cuda_rust_wasm/neural_integration/
performance_monitor.rs

1//! Performance Monitoring for Neural Integration
2//!
3//! This module provides real-time performance monitoring, bottleneck detection,
4//! and automatic optimization suggestions for neural operations.
5
6use super::{
7    NeuralIntegrationError, NeuralResult, OperationHandle, OperationStats, 
8    PerformanceDegradation, PerformanceMonitorTrait, PerformanceStats,
9};
10use std::collections::{HashMap, VecDeque};
11use std::sync::{Arc, Mutex, RwLock};
12use std::time::{Duration, Instant};
13
14/// Real-time performance monitor with adaptive optimization
15pub struct RealTimeMonitor {
16    operations: Arc<RwLock<HashMap<OperationHandle, OngoingOperation>>>,
17    history: Arc<Mutex<PerformanceHistory>>,
18    baselines: Arc<RwLock<HashMap<String, PerformanceBaseline>>>,
19    next_handle: Arc<Mutex<u64>>,
20    config: MonitorConfig,
21}
22
23/// Configuration for performance monitoring
24#[derive(Debug, Clone)]
25pub struct MonitorConfig {
26    pub history_size: usize,
27    pub baseline_window: usize,
28    pub degradation_threshold: f64, // 1.5 = 50% slower than baseline
29    pub enable_auto_optimization: bool,
30    pub sample_rate: f64, // 0.0 to 1.0
31}
32
33/// Ongoing operation tracking
34#[derive(Debug)]
35struct OngoingOperation {
36    name: String,
37    start_time: Instant,
38    gpu_start: Option<Instant>,
39    memory_start: usize,
40    expected_duration: Option<Duration>,
41}
42
43/// Performance history tracking
44struct PerformanceHistory {
45    operations: VecDeque<CompletedOperation>,
46    aggregated_stats: HashMap<String, AggregatedStats>,
47    total_operations: u64,
48}
49
50/// Completed operation record
51#[derive(Debug, Clone)]
52struct CompletedOperation {
53    name: String,
54    execution_time: Duration,
55    gpu_time: Duration,
56    memory_transfer_time: Duration,
57    throughput: f64,
58    timestamp: Instant,
59    memory_usage: usize,
60    success: bool,
61}
62
63/// Aggregated statistics for an operation type
64#[derive(Debug, Clone)]
65struct AggregatedStats {
66    count: u64,
67    total_time: Duration,
68    min_time: Duration,
69    max_time: Duration,
70    avg_time: Duration,
71    std_dev: f64,
72    throughput_sum: f64,
73    memory_usage_sum: usize,
74    failure_count: u64,
75}
76
77/// Performance baseline for comparison
78#[derive(Debug, Clone)]
79struct PerformanceBaseline {
80    operation_name: String,
81    expected_time: Duration,
82    expected_throughput: f64,
83    confidence: f64,
84    sample_count: u64,
85    last_updated: Instant,
86}
87
88/// No-op monitor for when monitoring is disabled
89pub struct NoOpMonitor;
90
91impl Default for NoOpMonitor {
92    fn default() -> Self {
93        Self::new()
94    }
95}
96
97impl NoOpMonitor {
98    pub fn new() -> Self {
99        NoOpMonitor
100    }
101}
102
103impl Default for MonitorConfig {
104    fn default() -> Self {
105        Self {
106            history_size: 10000,
107            baseline_window: 100,
108            degradation_threshold: 1.5,
109            enable_auto_optimization: true,
110            sample_rate: 1.0,
111        }
112    }
113}
114
115impl RealTimeMonitor {
116    /// Create a new real-time performance monitor
117    pub fn new() -> NeuralResult<Self> {
118        Self::with_config(MonitorConfig::default())
119    }
120    
121    /// Create a monitor with custom configuration
122    pub fn with_config(config: MonitorConfig) -> NeuralResult<Self> {
123        Ok(Self {
124            operations: Arc::new(RwLock::new(HashMap::new())),
125            history: Arc::new(Mutex::new(PerformanceHistory::new(config.history_size))),
126            baselines: Arc::new(RwLock::new(HashMap::new())),
127            next_handle: Arc::new(Mutex::new(1)),
128            config,
129        })
130    }
131    
132    /// Update baseline for an operation
133    fn update_baseline(&self, operation: &CompletedOperation) {
134        if !operation.success {
135            return;
136        }
137        
138        let mut baselines = self.baselines.write().unwrap();
139        let baseline = baselines.entry(operation.name.clone())
140            .or_insert_with(|| PerformanceBaseline {
141                operation_name: operation.name.clone(),
142                expected_time: operation.execution_time,
143                expected_throughput: operation.throughput,
144                confidence: 0.5,
145                sample_count: 0,
146                last_updated: Instant::now(),
147            });
148        
149        // Update baseline using exponential moving average
150        let alpha = 0.1; // Learning rate
151        let new_time_ms = operation.execution_time.as_secs_f64() * 1000.0;
152        let old_time_ms = baseline.expected_time.as_secs_f64() * 1000.0;
153        let updated_time_ms = alpha * new_time_ms + (1.0 - alpha) * old_time_ms;
154        
155        baseline.expected_time = Duration::from_secs_f64(updated_time_ms / 1000.0);
156        baseline.expected_throughput = alpha * operation.throughput + (1.0 - alpha) * baseline.expected_throughput;
157        baseline.sample_count += 1;
158        baseline.last_updated = Instant::now();
159        
160        // Increase confidence as we get more samples
161        baseline.confidence = (baseline.sample_count as f64 / 100.0).min(1.0);
162    }
163    
164    /// Check for performance degradation
165    fn check_degradation(&self, operation: &CompletedOperation) -> Option<PerformanceDegradation> {
166        let baselines = self.baselines.read().unwrap();
167        
168        if let Some(baseline) = baselines.get(&operation.name) {
169            if baseline.confidence < 0.3 || baseline.sample_count < 10 {
170                return None; // Not enough data for reliable comparison
171            }
172            
173            let actual_time = operation.execution_time.as_secs_f64();
174            let expected_time = baseline.expected_time.as_secs_f64();
175            let degradation_factor = actual_time / expected_time;
176            
177            if degradation_factor > self.config.degradation_threshold {
178                return Some(PerformanceDegradation {
179                    operation: operation.name.clone(),
180                    expected_time,
181                    actual_time,
182                    degradation_factor,
183                    suggested_action: self.generate_optimization_suggestion(operation, baseline),
184                });
185            }
186        }
187        
188        None
189    }
190    
191    /// Generate optimization suggestions
192    fn generate_optimization_suggestion(
193        &self,
194        operation: &CompletedOperation,
195        baseline: &PerformanceBaseline,
196    ) -> String {
197        if operation.memory_transfer_time > operation.execution_time / 2 {
198            "Consider using memory pooling or batch operations to reduce transfer overhead".to_string()
199        } else if operation.gpu_time < operation.execution_time / 3 {
200            "GPU utilization is low, consider increasing batch size or workgroup size".to_string()
201        } else if operation.throughput < baseline.expected_throughput * 0.7 {
202            "Throughput is significantly below baseline, check for memory pressure or resource contention".to_string()
203        } else {
204            "Performance degradation detected, consider profiling individual kernels".to_string()
205        }
206    }
207    
208    /// Get performance trends for an operation
209    pub fn get_trends(&self, operation_name: &str, window_size: usize) -> Option<PerformanceTrend> {
210        let history = self.history.lock().unwrap();
211        let recent_ops: Vec<&CompletedOperation> = history.operations
212            .iter()
213            .rev()
214            .filter(|op| op.name == operation_name)
215            .take(window_size)
216            .collect();
217        
218        if recent_ops.len() < 5 {
219            return None;
220        }
221        
222        let times: Vec<f64> = recent_ops.iter()
223            .map(|op| op.execution_time.as_secs_f64())
224            .collect();
225        
226        let trend_slope = calculate_trend_slope(&times);
227        let volatility = calculate_volatility(&times);
228        
229        Some(PerformanceTrend {
230            operation_name: operation_name.to_string(),
231            trend_slope,
232            volatility,
233            sample_count: recent_ops.len(),
234            improving: trend_slope < -0.01, // Negative slope means improving (faster)
235        })
236    }
237    
238    /// Get bottleneck analysis
239    pub fn get_bottleneck_analysis(&self, operation_name: &str) -> Option<BottleneckAnalysis> {
240        let history = self.history.lock().unwrap();
241        if let Some(stats) = history.aggregated_stats.get(operation_name) {
242            let avg_execution = stats.avg_time.as_secs_f64();
243            let avg_memory_transfer = stats.total_time.as_secs_f64() / stats.count as f64;
244            
245            let memory_ratio = avg_memory_transfer / avg_execution;
246            let gpu_ratio = 1.0 - memory_ratio; // Simplified calculation
247            
248            let bottleneck_type = if memory_ratio > 0.5 {
249                BottleneckType::MemoryTransfer
250            } else if gpu_ratio < 0.3 {
251                BottleneckType::GpuUnderutilization
252            } else if stats.failure_count as f64 / stats.count as f64 > 0.1 {
253                BottleneckType::ErrorRate
254            } else {
255                BottleneckType::Computation
256            };
257            
258            Some(BottleneckAnalysis {
259                operation_name: operation_name.to_string(),
260                bottleneck_type,
261                memory_transfer_ratio: memory_ratio,
262                gpu_utilization_ratio: gpu_ratio,
263                error_rate: stats.failure_count as f64 / stats.count as f64,
264                recommendation: generate_bottleneck_recommendation(&bottleneck_type),
265            })
266        } else {
267            None
268        }
269    }
270}
271
272impl PerformanceMonitorTrait for RealTimeMonitor {
273    fn start_operation(&self, name: &str) -> OperationHandle {
274        // Sample operations based on config
275        if self.config.sample_rate < 1.0 && rand::random::<f64>() > self.config.sample_rate {
276            return OperationHandle(0); // Skip monitoring for this operation
277        }
278        
279        let mut next_handle = self.next_handle.lock().unwrap();
280        let handle = OperationHandle(*next_handle);
281        *next_handle += 1;
282        
283        let operation = OngoingOperation {
284            name: name.to_string(),
285            start_time: Instant::now(),
286            gpu_start: None,
287            memory_start: 0, // TODO: Get actual memory usage
288            expected_duration: self.get_expected_duration(name),
289        };
290        
291        let mut operations = self.operations.write().unwrap();
292        operations.insert(handle, operation);
293        
294        handle
295    }
296    
297    fn end_operation(&self, handle: OperationHandle) -> NeuralResult<OperationStats> {
298        if handle.0 == 0 {
299            // This operation was not monitored
300            return Ok(OperationStats {
301                name: "unmonitored".to_string(),
302                execution_time: 0.0,
303                gpu_time: 0.0,
304                memory_transfer_time: 0.0,
305                throughput: 0.0,
306            });
307        }
308        
309        let mut operations = self.operations.write().unwrap();
310        let ongoing = operations.remove(&handle).ok_or_else(|| {
311            NeuralIntegrationError::PerformanceError("Invalid operation handle".to_string())
312        })?;
313        
314        let end_time = Instant::now();
315        let execution_time = end_time.duration_since(ongoing.start_time);
316        
317        // TODO: Get actual GPU and memory transfer times
318        let gpu_time = execution_time * 7 / 10; // Assume 70% GPU time
319        let memory_transfer_time = execution_time * 2 / 10; // Assume 20% transfer time
320        
321        let throughput = 1.0 / execution_time.as_secs_f64(); // Operations per second
322        
323        let completed_op = CompletedOperation {
324            name: ongoing.name.clone(),
325            execution_time,
326            gpu_time,
327            memory_transfer_time,
328            throughput,
329            timestamp: end_time,
330            memory_usage: 0, // TODO: Get actual memory usage
331            success: true, // TODO: Determine success based on context
332        };
333        
334        // Update history and baselines
335        {
336            let mut history = self.history.lock().unwrap();
337            history.add_operation(completed_op.clone());
338        }
339        
340        self.update_baseline(&completed_op);
341        
342        Ok(OperationStats {
343            name: ongoing.name,
344            execution_time: execution_time.as_secs_f64(),
345            gpu_time: gpu_time.as_secs_f64(),
346            memory_transfer_time: memory_transfer_time.as_secs_f64(),
347            throughput,
348        })
349    }
350    
351    fn get_performance_summary(&self) -> PerformanceStats {
352        let history = self.history.lock().unwrap();
353        
354        if history.total_operations == 0 {
355            return PerformanceStats {
356                total_operations: 0,
357                average_execution_time: 0.0,
358                gpu_utilization: 0.0,
359                memory_bandwidth: 0.0,
360                throughput: 0.0,
361            };
362        }
363        
364        let total_time: Duration = history.operations.iter()
365            .map(|op| op.execution_time)
366            .sum();
367        
368        let total_gpu_time: Duration = history.operations.iter()
369            .map(|op| op.gpu_time)
370            .sum();
371        
372        let total_throughput: f64 = history.operations.iter()
373            .map(|op| op.throughput)
374            .sum();
375        
376        PerformanceStats {
377            total_operations: history.total_operations,
378            average_execution_time: total_time.as_secs_f64() / history.total_operations as f64,
379            gpu_utilization: (total_gpu_time.as_secs_f64() / total_time.as_secs_f64()) as f32,
380            memory_bandwidth: 0.0, // TODO: Calculate actual memory bandwidth
381            throughput: total_throughput / history.total_operations as f64,
382        }
383    }
384    
385    fn detect_degradation(&self) -> Option<PerformanceDegradation> {
386        let history = self.history.lock().unwrap();
387        
388        // Check the most recent operation for degradation
389        if let Some(recent_op) = history.operations.back() {
390            // Clone the operation to avoid borrow after drop
391            let recent_op_clone = recent_op.clone();
392            drop(history);
393            self.check_degradation(&recent_op_clone)
394        } else {
395            None
396        }
397    }
398}
399
400impl PerformanceHistory {
401    fn new(max_size: usize) -> Self {
402        Self {
403            operations: VecDeque::with_capacity(max_size),
404            aggregated_stats: HashMap::new(),
405            total_operations: 0,
406        }
407    }
408    
409    fn add_operation(&mut self, operation: CompletedOperation) {
410        // Add to history
411        if self.operations.len() >= self.operations.capacity() {
412            self.operations.pop_front();
413        }
414        self.operations.push_back(operation.clone());
415        self.total_operations += 1;
416        
417        // Update aggregated stats
418        let stats = self.aggregated_stats.entry(operation.name.clone())
419            .or_insert_with(|| AggregatedStats {
420                count: 0,
421                total_time: Duration::ZERO,
422                min_time: operation.execution_time,
423                max_time: operation.execution_time,
424                avg_time: Duration::ZERO,
425                std_dev: 0.0,
426                throughput_sum: 0.0,
427                memory_usage_sum: 0,
428                failure_count: 0,
429            });
430        
431        stats.count += 1;
432        stats.total_time += operation.execution_time;
433        stats.min_time = stats.min_time.min(operation.execution_time);
434        stats.max_time = stats.max_time.max(operation.execution_time);
435        stats.avg_time = stats.total_time / stats.count as u32;
436        stats.throughput_sum += operation.throughput;
437        stats.memory_usage_sum += operation.memory_usage;
438        
439        if !operation.success {
440            stats.failure_count += 1;
441        }
442        
443        // Update standard deviation (simplified calculation)
444        let times: Vec<f64> = self.operations.iter()
445            .filter(|op| op.name == operation.name)
446            .map(|op| op.execution_time.as_secs_f64())
447            .collect();
448        
449        if times.len() > 1 {
450            stats.std_dev = calculate_std_dev(&times);
451        }
452    }
453}
454
455impl RealTimeMonitor {
456    fn get_expected_duration(&self, name: &str) -> Option<Duration> {
457        let baselines = self.baselines.read().unwrap();
458        baselines.get(name).map(|b| b.expected_time)
459    }
460}
461
462impl PerformanceMonitorTrait for NoOpMonitor {
463    fn start_operation(&self, _name: &str) -> OperationHandle {
464        OperationHandle(0)
465    }
466    
467    fn end_operation(&self, _handle: OperationHandle) -> NeuralResult<OperationStats> {
468        Ok(OperationStats {
469            name: "noop".to_string(),
470            execution_time: 0.0,
471            gpu_time: 0.0,
472            memory_transfer_time: 0.0,
473            throughput: 0.0,
474        })
475    }
476    
477    fn get_performance_summary(&self) -> PerformanceStats {
478        PerformanceStats {
479            total_operations: 0,
480            average_execution_time: 0.0,
481            gpu_utilization: 0.0,
482            memory_bandwidth: 0.0,
483            throughput: 0.0,
484        }
485    }
486    
487    fn detect_degradation(&self) -> Option<PerformanceDegradation> {
488        None
489    }
490}
491
492/// Performance trend information
493#[derive(Debug, Clone)]
494pub struct PerformanceTrend {
495    pub operation_name: String,
496    pub trend_slope: f64,
497    pub volatility: f64,
498    pub sample_count: usize,
499    pub improving: bool,
500}
501
502/// Bottleneck analysis
503#[derive(Debug, Clone)]
504pub struct BottleneckAnalysis {
505    pub operation_name: String,
506    pub bottleneck_type: BottleneckType,
507    pub memory_transfer_ratio: f64,
508    pub gpu_utilization_ratio: f64,
509    pub error_rate: f64,
510    pub recommendation: String,
511}
512
513/// Types of performance bottlenecks
514#[derive(Debug, Clone, Copy)]
515pub enum BottleneckType {
516    MemoryTransfer,
517    Computation,
518    GpuUnderutilization,
519    ErrorRate,
520}
521
522/// Calculate trend slope using linear regression
523fn calculate_trend_slope(values: &[f64]) -> f64 {
524    if values.len() < 2 {
525        return 0.0;
526    }
527    
528    let n = values.len() as f64;
529    let x_sum: f64 = (0..values.len()).map(|i| i as f64).sum();
530    let y_sum: f64 = values.iter().sum();
531    let xy_sum: f64 = values.iter().enumerate()
532        .map(|(i, &y)| i as f64 * y)
533        .sum();
534    let x_sq_sum: f64 = (0..values.len()).map(|i| (i as f64).powi(2)).sum();
535    
536    (n * xy_sum - x_sum * y_sum) / (n * x_sq_sum - x_sum.powi(2))
537}
538
539/// Calculate volatility (standard deviation)
540fn calculate_volatility(values: &[f64]) -> f64 {
541    calculate_std_dev(values)
542}
543
544/// Calculate standard deviation
545fn calculate_std_dev(values: &[f64]) -> f64 {
546    if values.len() < 2 {
547        return 0.0;
548    }
549    
550    let mean = values.iter().sum::<f64>() / values.len() as f64;
551    let variance = values.iter()
552        .map(|&x| (x - mean).powi(2))
553        .sum::<f64>() / values.len() as f64;
554    
555    variance.sqrt()
556}
557
558/// Generate recommendation for bottleneck type
559fn generate_bottleneck_recommendation(bottleneck_type: &BottleneckType) -> String {
560    match bottleneck_type {
561        BottleneckType::MemoryTransfer => {
562            "Optimize memory transfers by using larger batch sizes, memory pooling, or reducing data precision".to_string()
563        }
564        BottleneckType::Computation => {
565            "Optimize computation by improving algorithm efficiency, using better GPU kernels, or increasing parallelism".to_string()
566        }
567        BottleneckType::GpuUnderutilization => {
568            "Increase GPU utilization by using larger workgroup sizes, higher occupancy, or more parallel work".to_string()
569        }
570        BottleneckType::ErrorRate => {
571            "Reduce error rate by improving input validation, handling edge cases, or fixing stability issues".to_string()
572        }
573    }
574}
575
576#[cfg(test)]
577mod tests {
578    use super::*;
579    
580    #[test]
581    fn test_performance_monitor_creation() {
582        let monitor = RealTimeMonitor::new().unwrap();
583        let stats = monitor.get_performance_summary();
584        assert_eq!(stats.total_operations, 0);
585    }
586    
587    #[test]
588    fn test_operation_tracking() {
589        let monitor = RealTimeMonitor::new().unwrap();
590        
591        let handle = monitor.start_operation("test_op");
592        std::thread::sleep(Duration::from_millis(10));
593        let stats = monitor.end_operation(handle).unwrap();
594        
595        assert_eq!(stats.name, "test_op");
596        assert!(stats.execution_time > 0.0);
597    }
598    
599    #[test]
600    fn test_trend_calculation() {
601        let values = vec![1.0, 1.1, 1.2, 1.15, 1.3];
602        let slope = calculate_trend_slope(&values);
603        assert!(slope > 0.0); // Generally increasing
604    }
605    
606    #[test]
607    fn test_std_dev_calculation() {
608        let values = vec![1.0, 2.0, 3.0, 4.0, 5.0];
609        let std_dev = calculate_std_dev(&values);
610        assert!((std_dev - 1.58).abs() < 0.1); // Approximately sqrt(2.5)
611    }
612    
613    #[test]
614    fn test_noop_monitor() {
615        let monitor = NoOpMonitor;
616        let handle = monitor.start_operation("test");
617        let stats = monitor.end_operation(handle).unwrap();
618        assert_eq!(stats.name, "noop");
619    }
620}