scirs2_core/gpu/
auto_tuning.rs

1//! Automatic kernel tuning and optimization for GPU operations
2//!
3//! This module provides capabilities for automatically tuning GPU kernel parameters
4//! to achieve optimal performance on different hardware configurations and workloads.
5
6use crate::gpu::{GpuBackend, GpuError, GpuKernelHandle};
7use rand::Rng;
8use std::collections::HashMap;
9use std::sync::{Arc, Mutex};
10use std::time::{Duration, Instant};
11use thiserror::Error;
12
13/// Error types for auto-tuning operations
14#[derive(Error, Debug)]
15pub enum AutoTuningError {
16    /// No tuning configurations available
17    #[error("No tuning configurations available for kernel: {0}")]
18    NoConfigurations(String),
19
20    /// Tuning process failed
21    #[error("Auto-tuning failed: {0}")]
22    TuningFailed(String),
23
24    /// Invalid parameter configuration
25    #[error("Invalid parameter configuration: {0}")]
26    InvalidConfiguration(String),
27
28    /// Benchmark execution failed
29    #[error("Benchmark execution failed: {0}")]
30    BenchmarkFailed(String),
31
32    /// Underlying GPU error
33    #[error("GPU error: {0}")]
34    GpuError(#[from] GpuError),
35}
36
37/// Tunable kernel parameters
38#[derive(Debug, Clone, PartialEq)]
39pub struct KernelParameters {
40    /// Work group size (local work size)
41    pub work_group_size: [u32; 3],
42    /// Global work size
43    pub global_work_size: [u32; 3],
44    /// Local memory usage per work group
45    pub local_memory_size: usize,
46    /// Register usage per thread
47    pub register_usage: Option<usize>,
48    /// Cache configuration hints
49    pub cacheconfig: CacheConfig,
50    /// Custom parameters for kernel-specific tuning
51    pub custom_params: HashMap<String, ParameterValue>,
52}
53
54impl Default for KernelParameters {
55    fn default() -> Self {
56        Self {
57            work_group_size: [16, 16, 1],
58            global_work_size: [1024, 1024, 1],
59            local_memory_size: 0,
60            register_usage: None,
61            cacheconfig: CacheConfig::Balanced,
62            custom_params: HashMap::new(),
63        }
64    }
65}
66
67/// Parameter value types for kernel tuning
68#[derive(Debug, Clone, PartialEq)]
69pub enum ParameterValue {
70    /// Integer parameter
71    Int(i64),
72    /// Floating point parameter
73    Float(f64),
74    /// String parameter
75    String(String),
76    /// Boolean parameter
77    Bool(bool),
78    /// Array of integers
79    IntArray(Vec<i64>),
80    /// Array of floats
81    FloatArray(Vec<f64>),
82}
83
84impl ParameterValue {
85    /// Convert to integer if possible
86    pub fn as_int(&self) -> Option<i64> {
87        match self {
88            ParameterValue::Int(val) => Some(*val),
89            ParameterValue::Float(val) => Some(*val as i64),
90            _ => None,
91        }
92    }
93
94    /// Convert to float if possible
95    pub fn as_float(&self) -> Option<f64> {
96        match self {
97            ParameterValue::Float(val) => Some(*val),
98            ParameterValue::Int(val) => Some(*val as f64),
99            _ => None,
100        }
101    }
102
103    /// Convert to string
104    pub fn as_string(&self) -> String {
105        match self {
106            ParameterValue::String(val) => val.clone(),
107            ParameterValue::Int(val) => val.to_string(),
108            ParameterValue::Float(val) => val.to_string(),
109            ParameterValue::Bool(val) => val.to_string(),
110            _ => format!("{self:?}"),
111        }
112    }
113}
114
115/// Cache configuration strategies
116#[derive(Debug, Clone, Copy, PartialEq, Eq)]
117pub enum CacheConfig {
118    /// Prefer L1 cache for local memory
119    PreferL1,
120    /// Prefer shared memory over L1 cache
121    PreferShared,
122    /// Balanced cache usage
123    Balanced,
124    /// Optimize for read-only data
125    ReadOnly,
126    /// Optimize for write-through patterns
127    WriteThrough,
128}
129
130/// Performance metrics for kernel execution
131#[derive(Debug, Clone)]
132pub struct PerformanceMetrics {
133    /// Execution time
134    pub execution_time: Duration,
135    /// Throughput (operations per second)
136    pub throughput: f64,
137    /// Memory bandwidth utilization
138    pub memorybandwidth_util: f64,
139    /// Compute utilization
140    pub compute_utilization: f64,
141    /// Energy efficiency (operations per joule)
142    pub energy_efficiency: Option<f64>,
143    /// Cache hit rates
144    pub cache_metrics: CacheMetrics,
145}
146
147impl Default for PerformanceMetrics {
148    fn default() -> Self {
149        Self {
150            execution_time: Duration::from_millis(0),
151            throughput: 0.0,
152            memorybandwidth_util: 0.0,
153            compute_utilization: 0.0,
154            energy_efficiency: None,
155            cache_metrics: CacheMetrics::default(),
156        }
157    }
158}
159
160/// Cache performance metrics
161#[derive(Debug, Clone, Default)]
162pub struct CacheMetrics {
163    /// L1 cache hit rate
164    pub l1_hit_rate: f64,
165    /// L2 cache hit rate
166    pub l2_hit_rate: f64,
167    /// Shared memory bank conflicts
168    pub shared_memory_conflicts: usize,
169    /// Global memory coalescing efficiency
170    pub coalescing_efficiency: f64,
171    /// Memory throughput in GB/s
172    pub memory_throughput: f64,
173    /// Cache pressure indicator
174    pub cache_pressure: f64,
175}
176
177/// Auto-tuning strategy configuration
178#[derive(Debug, Clone)]
179pub struct TuningStrategy {
180    /// Search algorithm to use
181    pub search_algorithm: SearchAlgorithm,
182    /// Maximum number of configurations to test
183    pub max_evaluations: usize,
184    /// Time budget for tuning process
185    pub time_budget: Duration,
186    /// Number of benchmark runs per configuration
187    pub benchmark_runs: usize,
188    /// Convergence criteria
189    pub convergence_threshold: f64,
190    /// Whether to use historical data
191    pub use_history: bool,
192}
193
194impl Default for TuningStrategy {
195    fn default() -> Self {
196        Self {
197            search_algorithm: SearchAlgorithm::GridSearch,
198            max_evaluations: 100,
199            time_budget: Duration::from_secs(60),
200            benchmark_runs: 3,
201            convergence_threshold: 0.01, // 1% improvement required
202            use_history: true,
203        }
204    }
205}
206
207/// Search algorithms for parameter optimization
208#[derive(Debug, Clone, Copy, PartialEq, Eq)]
209pub enum SearchAlgorithm {
210    /// Exhaustive grid search
211    GridSearch,
212    /// Random search
213    RandomSearch,
214    /// Bayesian optimization
215    BayesianOptimization,
216    /// Genetic algorithm
217    GeneticAlgorithm,
218    /// Simulated annealing
219    SimulatedAnnealing,
220    /// Differential evolution
221    DifferentialEvolution,
222    /// Particle swarm optimization
223    ParticleSwarm,
224}
225
226/// Tuning configuration space
227#[derive(Debug, Clone)]
228pub struct TuningSpace {
229    /// Work group size options
230    pub work_group_sizes: Vec<[u32; 3]>,
231    /// Local memory size options
232    pub local_memory_sizes: Vec<usize>,
233    /// Cache configuration options
234    pub cache_configs: Vec<CacheConfig>,
235    /// Custom parameter spaces
236    pub custom_spaces: HashMap<String, Vec<ParameterValue>>,
237}
238
239impl Default for TuningSpace {
240    fn default() -> Self {
241        Self {
242            work_group_sizes: vec![
243                [8, 8, 1],
244                [16, 16, 1],
245                [32, 32, 1],
246                [64, 8, 1],
247                [8, 64, 1],
248                [128, 1, 1],
249                [256, 1, 1],
250                [512, 1, 1],
251            ],
252            local_memory_sizes: vec![0, 1024, 2048, 4096, 8192, 16384],
253            cache_configs: vec![
254                CacheConfig::Balanced,
255                CacheConfig::PreferL1,
256                CacheConfig::PreferShared,
257                CacheConfig::ReadOnly,
258            ],
259            custom_spaces: HashMap::new(),
260        }
261    }
262}
263
264/// Auto-tuning result
265#[derive(Debug, Clone)]
266pub struct TuningResult {
267    /// Best parameters found
268    pub best_params: KernelParameters,
269    /// Best performance achieved
270    pub best_performance: PerformanceMetrics,
271    /// Number of configurations evaluated
272    pub evaluations: usize,
273    /// Total tuning time
274    pub tuning_time: Duration,
275    /// Convergence information
276    pub converged: bool,
277    /// Performance improvement over baseline
278    pub improvement_factor: f64,
279}
280
281/// Automatic kernel tuner
282#[derive(Debug)]
283pub struct AutoTuner {
284    backend: GpuBackend,
285    strategy: TuningStrategy,
286    tuning_cache: Arc<Mutex<HashMap<String, TuningResult>>>,
287    device_info: DeviceInfo,
288}
289
290/// Device information for tuning
291#[derive(Debug, Clone)]
292struct DeviceInfo {
293    compute_capability: String,
294    #[allow(dead_code)]
295    memory_size: usize,
296    max_work_group_size: usize,
297    max_local_memory_size: usize,
298    #[allow(dead_code)]
299    warp_size: usize,
300}
301
302impl AutoTuner {
303    /// Create a new auto-tuner for the given backend
304    pub fn new(backend: GpuBackend, strategy: TuningStrategy) -> Result<Self, AutoTuningError> {
305        let device_info = Self::detect_device_info(backend)?;
306
307        Ok(Self {
308            backend,
309            strategy,
310            tuning_cache: Arc::new(Mutex::new(HashMap::new())),
311            device_info,
312        })
313    }
314
315    /// Auto-tune a kernel for optimal performance
316    pub fn tune(
317        &self,
318        kernel: &GpuKernelHandle,
319        kernel_name: &str,
320        problemsize: &[usize],
321        tuning_space: TuningSpace,
322    ) -> Result<TuningResult, AutoTuningError> {
323        let cache_key = self.generate_cache_key(kernel_name, problemsize);
324
325        // Check cache first
326        if self.strategy.use_history {
327            if let Some(cached_result) = self
328                .tuning_cache
329                .lock()
330                .expect("Operation failed")
331                .get(&cache_key)
332            {
333                return Ok(cached_result.clone());
334            }
335        }
336
337        let start_time = Instant::now();
338        let mut best_params = KernelParameters::default();
339        let mut best_performance: Option<PerformanceMetrics> = None;
340        let mut evaluations = 0;
341
342        // Generate parameter configurations to test
343        let configurations = self.generate_configurations(&tuning_space)?;
344
345        for (i, params) in configurations.iter().enumerate() {
346            if start_time.elapsed() > self.strategy.time_budget {
347                break;
348            }
349
350            if evaluations >= self.strategy.max_evaluations {
351                break;
352            }
353
354            // Benchmark this configuration
355            match self.benchmark_configuration(kernel, params, problemsize) {
356                Ok(metrics) => {
357                    evaluations += 1;
358
359                    if best_performance.is_none()
360                        || metrics.throughput
361                            > best_performance
362                                .as_ref()
363                                .expect("Operation failed")
364                                .throughput
365                    {
366                        best_params = params.clone();
367                        best_performance = Some(metrics);
368                    }
369
370                    // Check convergence
371                    if let Some(ref best) = best_performance {
372                        if self.check_convergence(best, i) {
373                            break;
374                        }
375                    }
376                }
377                Err(e) => {
378                    // Log benchmark failure but continue
379                    eprintln!("Benchmark failed for configuration {params:?}: {e}");
380                }
381            }
382        }
383
384        let best_performance = best_performance.ok_or_else(|| {
385            AutoTuningError::TuningFailed("No successful configurations".to_string())
386        })?;
387
388        let tuning_time = start_time.elapsed();
389        let improvement_factor = 1.0; // Would compare against baseline
390
391        let result = TuningResult {
392            best_params,
393            best_performance,
394            evaluations,
395            tuning_time,
396            converged: evaluations < self.strategy.max_evaluations,
397            improvement_factor,
398        };
399
400        // Cache the result
401        self.tuning_cache
402            .lock()
403            .expect("Operation failed")
404            .insert(cache_key, result.clone());
405
406        Ok(result)
407    }
408
409    /// Get cached tuning results
410    pub fn get_cached_results(&self) -> HashMap<String, TuningResult> {
411        self.tuning_cache.lock().expect("Operation failed").clone()
412    }
413
414    /// Clear tuning cache
415    pub fn clear_cache(&self) {
416        self.tuning_cache.lock().expect("Operation failed").clear();
417    }
418
419    /// Generate parameter configurations to test
420    fn generate_configurations(
421        &self,
422        space: &TuningSpace,
423    ) -> Result<Vec<KernelParameters>, AutoTuningError> {
424        match self.strategy.search_algorithm {
425            SearchAlgorithm::GridSearch => self.grid_search_configurations(space),
426            SearchAlgorithm::RandomSearch => self.random_search_configurations(space),
427            _ => {
428                // For other algorithms, fall back to grid search for now
429                self.grid_search_configurations(space)
430            }
431        }
432    }
433
434    /// Generate configurations using grid search
435    fn grid_search_configurations(
436        &self,
437        space: &TuningSpace,
438    ) -> Result<Vec<KernelParameters>, AutoTuningError> {
439        let mut configurations = Vec::new();
440
441        for &work_group_size in &space.work_group_sizes {
442            for &local_memory_size in &space.local_memory_sizes {
443                for &cache_config in &space.cache_configs {
444                    // Validate configuration against device limits
445                    if self.is_valid_configuration(work_group_size, local_memory_size) {
446                        configurations.push(KernelParameters {
447                            work_group_size,
448                            global_work_size: [1024, 1024, 1], // Default
449                            local_memory_size,
450                            register_usage: None,
451                            cacheconfig: cache_config,
452                            custom_params: HashMap::new(),
453                        });
454                    }
455                }
456            }
457        }
458
459        Ok(configurations)
460    }
461
462    /// Generate configurations using random search
463    fn random_search_configurations(
464        &self,
465        space: &TuningSpace,
466    ) -> Result<Vec<KernelParameters>, AutoTuningError> {
467        let mut configurations = Vec::new();
468        let num_samples = self.strategy.max_evaluations.min(100);
469
470        for _ in 0..num_samples {
471            let work_group_size =
472                space.work_group_sizes[rand::rng().random_range(0..space.work_group_sizes.len())];
473            let local_memory_size = space.local_memory_sizes
474                [rand::rng().random_range(0..space.local_memory_sizes.len())];
475            let cache_config =
476                space.cache_configs[rand::rng().random_range(0..space.cache_configs.len())];
477
478            if self.is_valid_configuration(work_group_size, local_memory_size) {
479                configurations.push(KernelParameters {
480                    work_group_size,
481                    global_work_size: [1024, 1024, 1],
482                    local_memory_size,
483                    register_usage: None,
484                    cacheconfig: cache_config,
485                    custom_params: HashMap::new(),
486                });
487            }
488        }
489
490        Ok(configurations)
491    }
492
493    /// Validate if a configuration is valid for the device
494    fn is_valid_configuration(&self, work_group_size: [u32; 3], local_memorysize: usize) -> bool {
495        let total_threads = work_group_size[0] * work_group_size[1] * work_group_size[2];
496
497        total_threads <= self.device_info.max_work_group_size as u32
498            && local_memorysize <= self.device_info.max_local_memory_size
499    }
500
501    /// Benchmark a specific configuration
502    fn benchmark_configuration(
503        &self,
504        kernel: &GpuKernelHandle,
505        params: &KernelParameters,
506        problemsize: &[usize],
507    ) -> Result<PerformanceMetrics, AutoTuningError> {
508        let mut execution_times = Vec::new();
509
510        // Run multiple iterations for stable timing
511        for _ in 0..self.strategy.benchmark_runs {
512            let start = Instant::now();
513
514            // Execute kernel with these parameters
515            kernel.dispatch(params.work_group_size);
516
517            // In a real implementation, we would:
518            // 1. Set up proper synchronization
519            // 2. Configure kernel parameters
520            // 3. Measure actual GPU execution time
521            // 4. Collect performance counters
522
523            let execution_time = start.elapsed();
524            execution_times.push(execution_time);
525        }
526
527        // Calculate average execution time
528        let avg_time = execution_times.iter().sum::<Duration>() / execution_times.len() as u32;
529
530        // Calculate throughput (simplified)
531        let total_ops = problemsize.iter().product::<usize>() as f64;
532        let throughput = total_ops / avg_time.as_secs_f64();
533
534        Ok(PerformanceMetrics {
535            execution_time: avg_time,
536            throughput,
537            memorybandwidth_util: 0.8, // Mock value
538            compute_utilization: 0.9,  // Mock value
539            energy_efficiency: None,
540            cache_metrics: CacheMetrics::default(),
541        })
542    }
543
544    /// Check if tuning has converged
545    fn check_convergence(&self, performance: &PerformanceMetrics, iteration: usize) -> bool {
546        // Simple convergence check based on iteration count
547        // In practice, would compare recent improvements
548        iteration > 10 && iteration % 10 == 0
549    }
550
551    /// Generate cache key for tuning results
552    fn generate_cache_key(&self, kernel_name: &str, problemsize: &[usize]) -> String {
553        format!(
554            "{}_{}_{}_{:?}",
555            self.backend, self.device_info.compute_capability, kernel_name, problemsize
556        )
557    }
558
559    /// Detect device information for tuning
560    fn detect_device_info(backend: GpuBackend) -> Result<DeviceInfo, AutoTuningError> {
561        // In a real implementation, this would query the actual device
562        match backend {
563            GpuBackend::Cuda => Ok(DeviceInfo {
564                compute_capability: "8.0".to_string(),
565                memory_size: 12 * 1024 * 1024 * 1024, // 12 GB
566                max_work_group_size: 1024,
567                max_local_memory_size: 48 * 1024, // 48 KB
568                warp_size: 32,
569            }),
570            GpuBackend::Rocm => Ok(DeviceInfo {
571                compute_capability: "RDNA2".to_string(),
572                memory_size: 16 * 1024 * 1024 * 1024, // 16 GB
573                max_work_group_size: 1024,
574                max_local_memory_size: 64 * 1024, // 64 KB
575                warp_size: 64,                    // Wavefront size
576            }),
577            _ => Ok(DeviceInfo {
578                compute_capability: "Unknown".to_string(),
579                memory_size: 8 * 1024 * 1024 * 1024, // 8 GB
580                max_work_group_size: 256,
581                max_local_memory_size: 16 * 1024, // 16 KB
582                warp_size: 32,
583            }),
584        }
585    }
586}
587
588/// Convenience functions for common auto-tuning scenarios
589pub mod presets {
590    use super::*;
591
592    /// Get tuning space optimized for matrix multiplication
593    pub fn matrix_multiply_space() -> TuningSpace {
594        TuningSpace {
595            work_group_sizes: vec![
596                [16, 16, 1],
597                [32, 32, 1],
598                [8, 32, 1],
599                [32, 8, 1],
600                [64, 4, 1],
601                [4, 64, 1],
602                [128, 2, 1],
603                [2, 128, 1],
604            ],
605            local_memory_sizes: vec![0, 2048, 4096, 8192, 16384],
606            cache_configs: vec![CacheConfig::PreferShared, CacheConfig::Balanced],
607            custom_spaces: HashMap::new(),
608        }
609    }
610
611    /// Get tuning space optimized for convolution operations
612    pub fn convolution_space() -> TuningSpace {
613        TuningSpace {
614            work_group_sizes: vec![
615                [8, 8, 1],
616                [16, 16, 1],
617                [32, 8, 1],
618                [8, 32, 1],
619                [64, 1, 1],
620                [32, 4, 1],
621                [4, 32, 1],
622            ],
623            local_memory_sizes: vec![1024, 2048, 4096, 8192],
624            cache_configs: vec![CacheConfig::PreferL1, CacheConfig::ReadOnly],
625            custom_spaces: HashMap::new(),
626        }
627    }
628
629    /// Get tuning space optimized for reduction operations
630    pub fn reduction_space() -> TuningSpace {
631        TuningSpace {
632            work_group_sizes: vec![
633                [64, 1, 1],
634                [128, 1, 1],
635                [256, 1, 1],
636                [512, 1, 1],
637                [1024, 1, 1],
638                [32, 2, 1],
639                [16, 4, 1],
640            ],
641            local_memory_sizes: vec![512, 1024, 2048, 4096],
642            cache_configs: vec![CacheConfig::PreferShared],
643            custom_spaces: HashMap::new(),
644        }
645    }
646}
647
648#[cfg(test)]
649mod tests {
650    use super::*;
651
652    #[test]
653    fn test_parameter_value_conversion() {
654        let int_val = ParameterValue::Int(42);
655        assert_eq!(int_val.as_int(), Some(42));
656        assert_eq!(int_val.as_float(), Some(42.0));
657
658        let float_val = ParameterValue::Float(3.5);
659        assert_eq!(float_val.as_float(), Some(3.5));
660        assert_eq!(float_val.as_int(), Some(3));
661    }
662
663    #[test]
664    fn test_kernel_parameters_default() {
665        let params = KernelParameters::default();
666        assert_eq!(params.work_group_size, [16, 16, 1]);
667        assert_eq!(params.local_memory_size, 0);
668    }
669
670    #[test]
671    fn test_tuning_strategy_default() {
672        let strategy = TuningStrategy::default();
673        assert_eq!(strategy.search_algorithm, SearchAlgorithm::GridSearch);
674        assert_eq!(strategy.max_evaluations, 100);
675    }
676
677    #[test]
678    fn test_tuning_space_default() {
679        let space = TuningSpace::default();
680        assert!(!space.work_group_sizes.is_empty());
681        assert!(!space.cache_configs.is_empty());
682    }
683
684    #[test]
685    fn testmatrix_multiply_preset() {
686        let space = presets::matrix_multiply_space();
687        assert!(space.work_group_sizes.contains(&[16, 16, 1]));
688        assert!(space.cache_configs.contains(&CacheConfig::PreferShared));
689    }
690
691    #[test]
692    fn test_device_info_detection() {
693        let device_info = AutoTuner::detect_device_info(GpuBackend::Cuda);
694        assert!(device_info.is_ok());
695
696        let info = device_info.expect("Operation failed");
697        assert!(info.max_work_group_size > 0);
698        assert!(info.max_local_memory_size > 0);
699    }
700}
scirs2_core/gpu/auto_tuning.rs

scirs2_core/gpu/
auto_tuning.rs
