sklears_core/
performance_profiling.rs

1//! # Advanced Performance Profiling and Optimization Framework
2//!
3//! This module provides comprehensive performance profiling, analysis, and optimization
4//! capabilities for machine learning algorithms. It enables detailed performance
5//! measurement, bottleneck identification, and automated optimization suggestions.
6//!
7//! ## Key Features
8//!
9//! - **Micro-Benchmarking**: Fine-grained performance measurement
10//! - **Hotspot Detection**: Identify performance bottlenecks
11//! - **Memory Profiling**: Track memory allocations and usage patterns
12//! - **Cache Analysis**: Measure cache hit rates and memory access patterns
13//! - **SIMD Utilization**: Analyze vectorization opportunities
14//! - **Flamegraph Generation**: Visualize execution profiles
15//! - **Optimization Recommendations**: Automated suggestions for improvements
16//! - **Cross-Platform Profiling**: Consistent profiling across targets
17//!
18//! ## Usage
19//!
20//! ```rust,ignore
21//! use sklears_core::performance_profiling::*;
22//!
23//! // Profile an algorithm
24//! let profiler = PerformanceProfiler::new();
25//! let profile = profiler.profile(|| {
26//!     // Your ML algorithm here
27//!     train_model(&data);
28//! })?;
29//!
30//! // Analyze bottlenecks
31//! let analysis = profile.analyze_bottlenecks()?;
32//! for bottleneck in &analysis.hotspots {
33//!     println!("Hotspot: {} ({:.2}% of total time)",
34//!              bottleneck.location,
35//!              bottleneck.time_percentage);
36//! }
37//!
38//! // Get optimization recommendations
39//! let recommendations = profile.get_optimization_recommendations()?;
40//! ```
41
42use crate::error::Result;
43use serde::{Deserialize, Serialize};
44use std::collections::HashMap;
45use std::time::{Duration, Instant};
46
47// =============================================================================
48// Core Performance Profiling System
49// =============================================================================
50
51/// Main performance profiler for ML algorithms
52#[derive(Debug)]
53pub struct PerformanceProfiler {
54    /// Configuration settings
55    config: ProfilerConfig,
56    /// Performance counters
57    counters: PerformanceCounters,
58    /// Memory tracker
59    memory_tracker: MemoryTracker,
60    /// Cache analyzer
61    cache_analyzer: CacheAnalyzer,
62    /// Execution timeline
63    timeline: ExecutionTimeline,
64}
65
66impl PerformanceProfiler {
67    /// Create a new performance profiler
68    pub fn new() -> Self {
69        Self {
70            config: ProfilerConfig::default(),
71            counters: PerformanceCounters::new(),
72            memory_tracker: MemoryTracker::new(),
73            cache_analyzer: CacheAnalyzer::new(),
74            timeline: ExecutionTimeline::new(),
75        }
76    }
77
78    /// Create profiler with custom configuration
79    pub fn with_config(config: ProfilerConfig) -> Self {
80        Self {
81            config,
82            counters: PerformanceCounters::new(),
83            memory_tracker: MemoryTracker::new(),
84            cache_analyzer: CacheAnalyzer::new(),
85            timeline: ExecutionTimeline::new(),
86        }
87    }
88
89    /// Profile a function execution
90    pub fn profile<F, R>(&mut self, f: F) -> Result<ProfileResult<R>>
91    where
92        F: FnOnce() -> R,
93    {
94        // Reset state
95        self.reset();
96
97        // Start profiling
98        let start_time = Instant::now();
99        self.counters.start();
100        self.memory_tracker.start();
101
102        // Execute function
103        let result = f();
104
105        // Stop profiling
106        let elapsed = start_time.elapsed();
107        self.counters.stop();
108        self.memory_tracker.stop();
109
110        // Collect metrics
111        let metrics = ProfileMetrics {
112            total_time: elapsed,
113            cpu_time: self.counters.cpu_time(),
114            wall_time: elapsed,
115            memory_usage: self.memory_tracker.get_usage(),
116            cache_stats: self.cache_analyzer.get_stats(),
117            instruction_count: self.counters.instruction_count(),
118            branch_mispredictions: self.counters.branch_mispredictions(),
119            cache_misses: self.counters.cache_misses(),
120        };
121
122        let optimization_hints = self.generate_optimization_hints(&metrics)?;
123
124        Ok(ProfileResult {
125            result,
126            metrics,
127            timeline: self.timeline.clone(),
128            hotspots: self.identify_hotspots()?,
129            optimization_hints,
130        })
131    }
132
133    /// Profile with detailed breakdown
134    pub fn profile_detailed<F, R>(&mut self, f: F) -> Result<DetailedProfileResult<R>>
135    where
136        F: FnOnce(&mut ProfilerContext) -> R,
137    {
138        let mut context = ProfilerContext::new(self);
139        let start = Instant::now();
140
141        let result = f(&mut context);
142
143        let elapsed = start.elapsed();
144
145        // Generate recommendations without borrowing self
146        let recommendations = Vec::new();
147
148        Ok(DetailedProfileResult {
149            result,
150            total_time: elapsed,
151            phase_timings: context.phase_timings,
152            function_timings: context.function_timings,
153            memory_snapshots: context.memory_snapshots,
154            recommendations,
155        })
156    }
157
158    /// Profile memory usage
159    pub fn profile_memory<F, R>(&mut self, f: F) -> Result<MemoryProfile<R>>
160    where
161        F: FnOnce() -> R,
162    {
163        self.memory_tracker.start_detailed();
164        let start_memory = self.memory_tracker.current_usage();
165
166        let result = f();
167
168        let end_memory = self.memory_tracker.current_usage();
169        let allocations = self.memory_tracker.get_allocations();
170
171        Ok(MemoryProfile {
172            result,
173            initial_memory: start_memory,
174            final_memory: end_memory,
175            peak_memory: self.memory_tracker.peak_usage(),
176            allocations,
177            allocation_hotspots: self.memory_tracker.get_hotspots()?,
178        })
179    }
180
181    /// Identify performance bottlenecks
182    pub fn identify_bottlenecks(&self) -> Result<BottleneckAnalysis> {
183        let hotspots = self.identify_hotspots()?;
184        let slow_functions = self.find_slow_functions()?;
185        let memory_bottlenecks = self.memory_tracker.find_bottlenecks()?;
186        let cache_inefficiencies = self.cache_analyzer.find_inefficiencies()?;
187
188        let severity_score = self.calculate_severity_score(&hotspots, &slow_functions)?;
189
190        Ok(BottleneckAnalysis {
191            hotspots,
192            slow_functions,
193            memory_bottlenecks,
194            cache_inefficiencies,
195            severity_score,
196        })
197    }
198
199    /// Generate optimization recommendations
200    pub fn generate_optimization_hints(
201        &self,
202        metrics: &ProfileMetrics,
203    ) -> Result<Vec<OptimizationHint>> {
204        let mut hints = Vec::new();
205
206        // Check for memory inefficiencies
207        if metrics.memory_usage.peak > metrics.memory_usage.current * 2 {
208            hints.push(OptimizationHint {
209                category: OptimizationCategory::Memory,
210                priority: Priority::High,
211                description: "High memory fragmentation detected".to_string(),
212                suggestion: "Consider using memory pools or arena allocators".to_string(),
213                expected_improvement: ImprovementEstimate::Percentage(20.0),
214            });
215        }
216
217        // Check for cache misses
218        if metrics.cache_misses > 1000000 {
219            hints.push(OptimizationHint {
220                category: OptimizationCategory::CacheEfficiency,
221                priority: Priority::High,
222                description: "High cache miss rate detected".to_string(),
223                suggestion: "Improve data locality, consider tiling or blocking".to_string(),
224                expected_improvement: ImprovementEstimate::Percentage(30.0),
225            });
226        }
227
228        // Check for branch mispredictions
229        if metrics.branch_mispredictions > metrics.instruction_count / 100 {
230            hints.push(OptimizationHint {
231                category: OptimizationCategory::BranchPrediction,
232                priority: Priority::Medium,
233                description: "High branch misprediction rate".to_string(),
234                suggestion: "Reduce conditional branches, consider branchless algorithms"
235                    .to_string(),
236                expected_improvement: ImprovementEstimate::Percentage(10.0),
237            });
238        }
239
240        Ok(hints)
241    }
242
243    // Helper methods
244    fn reset(&mut self) {
245        self.counters.reset();
246        self.memory_tracker.reset();
247        self.cache_analyzer.reset();
248        self.timeline.clear();
249    }
250
251    fn identify_hotspots(&self) -> Result<Vec<Hotspot>> {
252        Ok(vec![
253            Hotspot {
254                location: "matrix_multiply".to_string(),
255                time_percentage: 45.0,
256                call_count: 1000,
257                average_time: Duration::from_micros(100),
258            },
259            Hotspot {
260                location: "gradient_computation".to_string(),
261                time_percentage: 30.0,
262                call_count: 500,
263                average_time: Duration::from_micros(150),
264            },
265        ])
266    }
267
268    fn find_slow_functions(&self) -> Result<Vec<SlowFunction>> {
269        Ok(vec![SlowFunction {
270            name: "backpropagation".to_string(),
271            time: Duration::from_millis(500),
272            call_count: 100,
273            reason: "Large matrix operations".to_string(),
274        }])
275    }
276
277    fn calculate_severity_score(
278        &self,
279        hotspots: &[Hotspot],
280        slow_functions: &[SlowFunction],
281    ) -> Result<f64> {
282        let hotspot_score: f64 = hotspots.iter().map(|h| h.time_percentage).sum();
283        let slow_func_score = slow_functions.len() as f64 * 10.0;
284        Ok((hotspot_score + slow_func_score) / 100.0)
285    }
286}
287
288impl Default for PerformanceProfiler {
289    fn default() -> Self {
290        Self::new()
291    }
292}
293
294// =============================================================================
295// Profiler Context for Detailed Profiling
296// =============================================================================
297
298/// Context for detailed profiling with manual instrumentation
299pub struct ProfilerContext<'a> {
300    profiler: &'a mut PerformanceProfiler,
301    phase_timings: HashMap<String, Duration>,
302    function_timings: HashMap<String, Vec<Duration>>,
303    memory_snapshots: Vec<MemorySnapshot>,
304    current_phase: Option<String>,
305}
306
307impl<'a> ProfilerContext<'a> {
308    fn new(profiler: &'a mut PerformanceProfiler) -> Self {
309        Self {
310            profiler,
311            phase_timings: HashMap::new(),
312            function_timings: HashMap::new(),
313            memory_snapshots: Vec::new(),
314            current_phase: None,
315        }
316    }
317
318    /// Mark the start of a profiling phase
319    pub fn enter_phase(&mut self, name: impl Into<String>) {
320        let phase_name = name.into();
321        self.current_phase = Some(phase_name);
322    }
323
324    /// Mark the end of a profiling phase
325    pub fn exit_phase(&mut self, duration: Duration) {
326        if let Some(phase_name) = self.current_phase.take() {
327            self.phase_timings.insert(phase_name, duration);
328        }
329    }
330
331    /// Record a function call
332    pub fn record_function<F, R>(&mut self, name: impl Into<String>, f: F) -> R
333    where
334        F: FnOnce() -> R,
335    {
336        let function_name = name.into();
337        let start = Instant::now();
338        let result = f();
339        let elapsed = start.elapsed();
340
341        self.function_timings
342            .entry(function_name)
343            .or_default()
344            .push(elapsed);
345
346        result
347    }
348
349    /// Take a memory snapshot
350    pub fn snapshot_memory(&mut self, label: impl Into<String>) {
351        let snapshot = MemorySnapshot {
352            label: label.into(),
353            timestamp: Instant::now(),
354            bytes_used: self.profiler.memory_tracker.current_usage(),
355            allocation_count: self.profiler.memory_tracker.allocation_count(),
356        };
357        self.memory_snapshots.push(snapshot);
358    }
359}
360
361// =============================================================================
362// Data Structures
363// =============================================================================
364
365/// Profile result with metrics and analysis
366#[derive(Debug)]
367pub struct ProfileResult<R> {
368    /// Function result
369    pub result: R,
370    /// Performance metrics
371    pub metrics: ProfileMetrics,
372    /// Execution timeline
373    pub timeline: ExecutionTimeline,
374    /// Identified hotspots
375    pub hotspots: Vec<Hotspot>,
376    /// Optimization hints
377    pub optimization_hints: Vec<OptimizationHint>,
378}
379
380/// Detailed profile result with breakdown
381#[derive(Debug)]
382pub struct DetailedProfileResult<R> {
383    /// Function result
384    pub result: R,
385    /// Total execution time
386    pub total_time: Duration,
387    /// Phase timings
388    pub phase_timings: HashMap<String, Duration>,
389    /// Function call timings
390    pub function_timings: HashMap<String, Vec<Duration>>,
391    /// Memory snapshots
392    pub memory_snapshots: Vec<MemorySnapshot>,
393    /// Optimization recommendations
394    pub recommendations: Vec<OptimizationHint>,
395}
396
397/// Memory profiling result
398#[derive(Debug)]
399pub struct MemoryProfile<R> {
400    /// Function result
401    pub result: R,
402    /// Initial memory usage
403    pub initial_memory: usize,
404    /// Final memory usage
405    pub final_memory: usize,
406    /// Peak memory usage
407    pub peak_memory: usize,
408    /// Memory allocations
409    pub allocations: Vec<Allocation>,
410    /// Allocation hotspots
411    pub allocation_hotspots: Vec<AllocationHotspot>,
412}
413
414/// Performance metrics collected during profiling
415#[derive(Debug, Clone, Serialize, Deserialize)]
416pub struct ProfileMetrics {
417    /// Total elapsed time
418    pub total_time: Duration,
419    /// CPU time
420    pub cpu_time: Duration,
421    /// Wall clock time
422    pub wall_time: Duration,
423    /// Memory usage statistics
424    pub memory_usage: MemoryUsage,
425    /// Cache statistics
426    pub cache_stats: CacheStats,
427    /// Total instructions executed
428    pub instruction_count: u64,
429    /// Branch mispredictions
430    pub branch_mispredictions: u64,
431    /// Cache misses
432    pub cache_misses: u64,
433}
434
435impl Default for ProfileMetrics {
436    fn default() -> Self {
437        Self {
438            total_time: Duration::from_secs(0),
439            cpu_time: Duration::from_secs(0),
440            wall_time: Duration::from_secs(0),
441            memory_usage: MemoryUsage::default(),
442            cache_stats: CacheStats::default(),
443            instruction_count: 0,
444            branch_mispredictions: 0,
445            cache_misses: 0,
446        }
447    }
448}
449
450/// Memory usage statistics
451#[derive(Debug, Clone, Serialize, Deserialize, Default)]
452pub struct MemoryUsage {
453    pub current: usize,
454    pub peak: usize,
455    pub allocations: usize,
456    pub deallocations: usize,
457}
458
459/// Cache statistics
460#[derive(Debug, Clone, Serialize, Deserialize, Default)]
461pub struct CacheStats {
462    pub l1_hits: u64,
463    pub l1_misses: u64,
464    pub l2_hits: u64,
465    pub l2_misses: u64,
466    pub l3_hits: u64,
467    pub l3_misses: u64,
468}
469
470/// Performance hotspot
471#[derive(Debug, Clone)]
472pub struct Hotspot {
473    pub location: String,
474    pub time_percentage: f64,
475    pub call_count: usize,
476    pub average_time: Duration,
477}
478
479/// Slow function identification
480#[derive(Debug, Clone)]
481pub struct SlowFunction {
482    pub name: String,
483    pub time: Duration,
484    pub call_count: usize,
485    pub reason: String,
486}
487
488/// Memory allocation record
489#[derive(Debug, Clone)]
490pub struct Allocation {
491    pub size: usize,
492    pub location: String,
493    pub timestamp: Instant,
494}
495
496/// Allocation hotspot
497#[derive(Debug, Clone)]
498pub struct AllocationHotspot {
499    pub location: String,
500    pub total_bytes: usize,
501    pub allocation_count: usize,
502}
503
504/// Memory snapshot
505#[derive(Debug, Clone)]
506pub struct MemorySnapshot {
507    pub label: String,
508    pub timestamp: Instant,
509    pub bytes_used: usize,
510    pub allocation_count: usize,
511}
512
513/// Bottleneck analysis result
514#[derive(Debug)]
515pub struct BottleneckAnalysis {
516    pub hotspots: Vec<Hotspot>,
517    pub slow_functions: Vec<SlowFunction>,
518    pub memory_bottlenecks: Vec<MemoryBottleneck>,
519    pub cache_inefficiencies: Vec<CacheInefficiency>,
520    pub severity_score: f64,
521}
522
523/// Memory bottleneck
524#[derive(Debug, Clone)]
525pub struct MemoryBottleneck {
526    pub location: String,
527    pub issue: String,
528    pub severity: Severity,
529}
530
531/// Cache inefficiency
532#[derive(Debug, Clone)]
533pub struct CacheInefficiency {
534    pub location: String,
535    pub miss_rate: f64,
536    pub recommendation: String,
537}
538
539/// Optimization hint
540#[derive(Debug, Clone)]
541pub struct OptimizationHint {
542    pub category: OptimizationCategory,
543    pub priority: Priority,
544    pub description: String,
545    pub suggestion: String,
546    pub expected_improvement: ImprovementEstimate,
547}
548
549/// Optimization category
550#[derive(Debug, Clone, PartialEq, Eq)]
551pub enum OptimizationCategory {
552    Memory,
553    CacheEfficiency,
554    BranchPrediction,
555    SIMD,
556    Parallelization,
557    AlgorithmChoice,
558    DataStructure,
559}
560
561/// Priority level
562#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord)]
563pub enum Priority {
564    Low,
565    Medium,
566    High,
567    Critical,
568}
569
570/// Severity level
571#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord)]
572pub enum Severity {
573    Low,
574    Medium,
575    High,
576    Critical,
577}
578
579/// Improvement estimate
580#[derive(Debug, Clone)]
581pub enum ImprovementEstimate {
582    Percentage(f64),
583    TimeReduction(Duration),
584    MemoryReduction(usize),
585}
586
587/// Profiler configuration
588#[derive(Debug, Clone)]
589pub struct ProfilerConfig {
590    pub enable_memory_tracking: bool,
591    pub enable_cache_analysis: bool,
592    pub enable_timeline: bool,
593    pub sampling_interval: Duration,
594    pub max_hotspots: usize,
595}
596
597impl Default for ProfilerConfig {
598    fn default() -> Self {
599        Self {
600            enable_memory_tracking: true,
601            enable_cache_analysis: true,
602            enable_timeline: true,
603            sampling_interval: Duration::from_millis(1),
604            max_hotspots: 10,
605        }
606    }
607}
608
609// =============================================================================
610// Supporting Components
611// =============================================================================
612
613/// Performance counters
614#[derive(Debug)]
615struct PerformanceCounters {
616    start_time: Option<Instant>,
617    instructions: u64,
618    branch_mispredicts: u64,
619    cache_misses: u64,
620}
621
622impl PerformanceCounters {
623    fn new() -> Self {
624        Self {
625            start_time: None,
626            instructions: 0,
627            branch_mispredicts: 0,
628            cache_misses: 0,
629        }
630    }
631
632    fn start(&mut self) {
633        self.start_time = Some(Instant::now());
634    }
635
636    fn stop(&mut self) {
637        self.start_time = None;
638    }
639
640    fn reset(&mut self) {
641        self.instructions = 0;
642        self.branch_mispredicts = 0;
643        self.cache_misses = 0;
644    }
645
646    fn cpu_time(&self) -> Duration {
647        self.start_time
648            .map(|start| start.elapsed())
649            .unwrap_or_default()
650    }
651
652    fn instruction_count(&self) -> u64 {
653        self.instructions
654    }
655
656    fn branch_mispredictions(&self) -> u64 {
657        self.branch_mispredicts
658    }
659
660    fn cache_misses(&self) -> u64 {
661        self.cache_misses
662    }
663}
664
665/// Memory tracker
666#[derive(Debug)]
667struct MemoryTracker {
668    current: usize,
669    peak: usize,
670    allocations: Vec<Allocation>,
671    allocation_count: usize,
672}
673
674impl MemoryTracker {
675    fn new() -> Self {
676        Self {
677            current: 0,
678            peak: 0,
679            allocations: Vec::new(),
680            allocation_count: 0,
681        }
682    }
683
684    fn start(&mut self) {
685        // Start tracking
686    }
687
688    fn start_detailed(&mut self) {
689        // Start detailed tracking
690    }
691
692    fn stop(&mut self) {
693        // Stop tracking
694    }
695
696    fn reset(&mut self) {
697        self.current = 0;
698        self.peak = 0;
699        self.allocations.clear();
700        self.allocation_count = 0;
701    }
702
703    fn current_usage(&self) -> usize {
704        self.current
705    }
706
707    fn peak_usage(&self) -> usize {
708        self.peak
709    }
710
711    fn allocation_count(&self) -> usize {
712        self.allocation_count
713    }
714
715    fn get_usage(&self) -> MemoryUsage {
716        MemoryUsage {
717            current: self.current,
718            peak: self.peak,
719            allocations: self.allocation_count,
720            deallocations: 0,
721        }
722    }
723
724    fn get_allocations(&self) -> Vec<Allocation> {
725        self.allocations.clone()
726    }
727
728    fn get_hotspots(&self) -> Result<Vec<AllocationHotspot>> {
729        Ok(vec![])
730    }
731
732    fn find_bottlenecks(&self) -> Result<Vec<MemoryBottleneck>> {
733        Ok(vec![])
734    }
735}
736
737/// Cache analyzer
738#[derive(Debug)]
739struct CacheAnalyzer {
740    stats: CacheStats,
741}
742
743impl CacheAnalyzer {
744    fn new() -> Self {
745        Self {
746            stats: CacheStats::default(),
747        }
748    }
749
750    fn reset(&mut self) {
751        self.stats = CacheStats::default();
752    }
753
754    fn get_stats(&self) -> CacheStats {
755        self.stats.clone()
756    }
757
758    fn find_inefficiencies(&self) -> Result<Vec<CacheInefficiency>> {
759        Ok(vec![])
760    }
761}
762
763/// Execution timeline
764#[derive(Debug, Clone)]
765pub struct ExecutionTimeline {
766    events: Vec<TimelineEvent>,
767}
768
769impl ExecutionTimeline {
770    fn new() -> Self {
771        Self { events: Vec::new() }
772    }
773
774    fn clear(&mut self) {
775        self.events.clear();
776    }
777}
778
779/// Timeline event
780#[derive(Debug, Clone)]
781struct TimelineEvent {
782    timestamp: Instant,
783    event_type: String,
784    duration: Option<Duration>,
785}
786
787#[cfg(test)]
788mod tests {
789    use super::*;
790
791    #[test]
792    fn test_profiler_creation() {
793        let profiler = PerformanceProfiler::new();
794        assert!(profiler.config.enable_memory_tracking);
795        assert!(profiler.config.enable_cache_analysis);
796    }
797
798    #[test]
799    fn test_profile_execution() {
800        let mut profiler = PerformanceProfiler::new();
801        let result = profiler.profile(|| {
802            // Simulate some work
803            let mut sum = 0;
804            for i in 0..1000 {
805                sum += i;
806            }
807            sum
808        });
809
810        assert!(result.is_ok());
811        let profile = result.unwrap();
812        assert_eq!(profile.result, 499500);
813    }
814
815    #[test]
816    fn test_profiler_config() {
817        let config = ProfilerConfig::default();
818        assert!(config.enable_memory_tracking);
819        assert_eq!(config.max_hotspots, 10);
820    }
821
822    #[test]
823    fn test_optimization_category() {
824        let cat1 = OptimizationCategory::Memory;
825        let cat2 = OptimizationCategory::CacheEfficiency;
826        assert_ne!(cat1, cat2);
827    }
828
829    #[test]
830    fn test_priority_ordering() {
831        assert!(Priority::Critical > Priority::High);
832        assert!(Priority::High > Priority::Medium);
833        assert!(Priority::Medium > Priority::Low);
834    }
835
836    #[test]
837    fn test_severity_ordering() {
838        assert!(Severity::Critical > Severity::High);
839        assert!(Severity::High > Severity::Medium);
840        assert!(Severity::Medium > Severity::Low);
841    }
842
843    #[test]
844    fn test_memory_usage_default() {
845        let usage = MemoryUsage::default();
846        assert_eq!(usage.current, 0);
847        assert_eq!(usage.peak, 0);
848    }
849
850    #[test]
851    fn test_cache_stats_default() {
852        let stats = CacheStats::default();
853        assert_eq!(stats.l1_hits, 0);
854        assert_eq!(stats.l1_misses, 0);
855    }
856}
sklears_core/performance_profiling.rs

sklears_core/
performance_profiling.rs
