cuda_rust_wasm/backend/
webgpu_optimized.rs

1//! Optimized WebGPU backend for high-performance WASM execution
2//!
3//! This module provides an optimized WebGPU backend with advanced features:
4//! - Kernel caching and JIT compilation
5//! - Memory pooling and efficient transfers
6//! - Auto-tuning for optimal block sizes
7//! - Performance profiling and monitoring
8
9use std::collections::HashMap;
10use std::sync::{Arc, Mutex};
11use wgpu::*;
12use crate::error::{CudaRustError, Result};
13use crate::memory::{MemoryPool, allocate, deallocate};
14use crate::profiling::{CounterType, time_operation};
15
16/// Configuration for WebGPU optimization
17#[derive(Debug, Clone)]
18pub struct WebGPUConfig {
19    /// Enable kernel caching
20    pub enable_kernel_cache: bool,
21    /// Enable auto-tuning for block sizes
22    pub enable_auto_tuning: bool,
23    /// Enable memory pooling
24    pub enable_memory_pooling: bool,
25    /// Maximum cache size for compiled kernels
26    pub max_cache_size: usize,
27    /// Preferred power preference
28    pub power_preference: PowerPreference,
29    /// Memory limits
30    pub max_buffer_size: u64,
31    /// Threading configuration
32    pub max_workgroups_per_dimension: u32,
33}
34
35impl Default for WebGPUConfig {
36    fn default() -> Self {
37        Self {
38            enable_kernel_cache: true,
39            enable_auto_tuning: true,
40            enable_memory_pooling: true,
41            max_cache_size: 100,
42            power_preference: PowerPreference::HighPerformance,
43            max_buffer_size: 256 * 1024 * 1024, // 256MB
44            max_workgroups_per_dimension: 65535,
45        }
46    }
47}
48
49/// Cached kernel with optimization metadata
50#[derive(Debug, Clone)]
51pub struct CachedKernel {
52    /// Compiled compute pipeline
53    pub pipeline: Arc<ComputePipeline>,
54    /// Bind group layout
55    pub bind_group_layout: Arc<BindGroupLayout>,
56    /// Optimal workgroup size (auto-tuned)
57    pub optimal_workgroup_size: [u32; 3],
58    /// Performance metrics
59    pub avg_execution_time: f64,
60    /// Usage count for cache eviction
61    pub usage_count: u64,
62    /// Total data processed (for throughput calculation)
63    pub total_data_processed: u64,
64}
65
66/// High-performance WebGPU backend
67pub struct OptimizedWebGPUBackend {
68    /// WebGPU device
69    device: Arc<Device>,
70    /// Command queue
71    queue: Arc<Queue>,
72    /// Configuration
73    config: WebGPUConfig,
74    /// Kernel cache
75    kernel_cache: Arc<Mutex<HashMap<String, CachedKernel>>>,
76    /// Memory pool for buffers
77    memory_pool: Arc<MemoryPool>,
78    /// Buffer cache for reuse
79    buffer_cache: Arc<Mutex<HashMap<u64, Vec<Buffer>>>>,
80    /// Performance statistics
81    stats: Arc<Mutex<BackendStats>>,
82}
83
84/// Performance statistics for the backend
85#[derive(Debug, Clone, Default)]
86pub struct BackendStats {
87    /// Total kernels executed
88    pub kernels_executed: u64,
89    /// Cache hits
90    pub cache_hits: u64,
91    /// Cache misses
92    pub cache_misses: u64,
93    /// Total execution time
94    pub total_execution_time: f64,
95    /// Total data transferred
96    pub total_data_transferred: u64,
97    /// Memory allocations
98    pub memory_allocations: u64,
99    /// Buffer reuse count
100    pub buffer_reuse_count: u64,
101}
102
103/// Auto-tuning results for optimal performance
104#[derive(Debug, Clone)]
105pub struct AutoTuneResult {
106    /// Optimal workgroup size
107    pub workgroup_size: [u32; 3],
108    /// Measured performance (operations per second)
109    pub performance: f64,
110    /// Memory bandwidth utilization
111    pub memory_bandwidth: f64,
112    /// Compute utilization
113    pub compute_utilization: f64,
114}
115
116impl OptimizedWebGPUBackend {
117    /// Create a new optimized WebGPU backend
118    pub async fn new() -> Result<Self> {
119        Self::with_config(WebGPUConfig::default()).await
120    }
121
122    /// Create backend with custom configuration
123    pub async fn with_config(config: WebGPUConfig) -> Result<Self> {
124        let _timer = time_operation(CounterType::Custom("webgpu_init".to_string()));
125        
126        // Request adapter with high performance preference
127        let instance = Instance::new(InstanceDescriptor {
128            backends: Backends::BROWSER_WEBGPU | Backends::GL,
129            flags: InstanceFlags::default(),
130            dx12_shader_compiler: Dx12Compiler::default(),
131            gles_minor_version: Gles3MinorVersion::default(),
132        });
133
134        let adapter = instance
135            .request_adapter(&RequestAdapterOptions {
136                power_preference: config.power_preference,
137                compatible_surface: None,
138                force_fallback_adapter: false,
139            })
140            .await
141            .ok_or_else(|| CudaRustError::Backend("Failed to find suitable WebGPU adapter".to_string()))?;
142
143        // Request device with optimal limits
144        let (device, queue) = adapter
145            .request_device(
146                &DeviceDescriptor {
147                    label: Some("CUDA-Rust Optimized Device"),
148                    required_features: Features::TIMESTAMP_QUERY 
149                        | Features::TIMESTAMP_QUERY_INSIDE_PASSES
150                        | Features::PIPELINE_STATISTICS_QUERY,
151                    required_limits: Limits {
152                        max_buffer_size: config.max_buffer_size,
153                        max_compute_workgroup_storage_size: 32768,
154                        max_compute_invocations_per_workgroup: 1024,
155                        max_compute_workgroup_size_x: 1024,
156                        max_compute_workgroup_size_y: 1024,
157                        max_compute_workgroup_size_z: 64,
158                        max_compute_workgroups_per_dimension: config.max_workgroups_per_dimension,
159                        ..Default::default()
160                    },
161                },
162                None,
163            )
164            .await
165            .map_err(|e| CudaRustError::Backend(format!("Failed to create WebGPU device: {e}")))?;
166
167        Ok(Self {
168            device: Arc::new(device),
169            queue: Arc::new(queue),
170            config,
171            kernel_cache: Arc::new(Mutex::new(HashMap::new())),
172            memory_pool: Arc::new(MemoryPool::new()),
173            buffer_cache: Arc::new(Mutex::new(HashMap::new())),
174            stats: Arc::new(Mutex::new(BackendStats::default())),
175        })
176    }
177
178    /// Compile and cache a kernel with optimization
179    pub fn compile_kernel(&self, shader_source: &str, entry_point: &str) -> Result<String> {
180        let _timer = time_operation(CounterType::Compilation)
181            .with_size(shader_source.len());
182
183        let cache_key = format!("{}:{}", shader_source.len(), entry_point);
184        
185        // Check cache first
186        {
187            let cache = self.kernel_cache.lock().unwrap();
188            if let Some(cached) = cache.get(&cache_key) {
189                let mut stats = self.stats.lock().unwrap();
190                stats.cache_hits += 1;
191                return Ok(cache_key);
192            }
193        }
194
195        // Cache miss - compile new kernel
196        let shader_module = self.device.create_shader_module(ShaderModuleDescriptor {
197            label: Some("CUDA Kernel"),
198            source: ShaderSource::Wgsl(shader_source.into()),
199        });
200
201        let bind_group_layout = self.device.create_bind_group_layout(&BindGroupLayoutDescriptor {
202            label: Some("Kernel Bind Group Layout"),
203            entries: &[
204                BindGroupLayoutEntry {
205                    binding: 0,
206                    visibility: ShaderStages::COMPUTE,
207                    ty: BindingType::Buffer {
208                        ty: BufferBindingType::Storage { read_only: false },
209                        has_dynamic_offset: false,
210                        min_binding_size: None,
211                    },
212                    count: None,
213                },
214            ],
215        });
216
217        let pipeline_layout = self.device.create_pipeline_layout(&PipelineLayoutDescriptor {
218            label: Some("Kernel Pipeline Layout"),
219            bind_group_layouts: &[&bind_group_layout],
220            push_constant_ranges: &[],
221        });
222
223        let pipeline = self.device.create_compute_pipeline(&ComputePipelineDescriptor {
224            label: Some("CUDA Kernel Pipeline"),
225            layout: Some(&pipeline_layout),
226            module: &shader_module,
227            entry_point,
228        });
229
230        // Auto-tune optimal workgroup size if enabled
231        let optimal_workgroup_size = if self.config.enable_auto_tuning {
232            self.auto_tune_workgroup_size(&pipeline, &bind_group_layout)?
233        } else {
234            [64, 1, 1] // Default workgroup size
235        };
236
237        // Cache the compiled kernel
238        let cached_kernel = CachedKernel {
239            pipeline: Arc::new(pipeline),
240            bind_group_layout: Arc::new(bind_group_layout),
241            optimal_workgroup_size,
242            avg_execution_time: 0.0,
243            usage_count: 0,
244            total_data_processed: 0,
245        };
246
247        {
248            let mut cache = self.kernel_cache.lock().unwrap();
249            
250            // Evict old entries if cache is full
251            if cache.len() >= self.config.max_cache_size {
252                self.evict_least_used_kernel(&mut cache);
253            }
254            
255            cache.insert(cache_key.clone(), cached_kernel);
256        }
257
258        {
259            let mut stats = self.stats.lock().unwrap();
260            stats.cache_misses += 1;
261        }
262
263        Ok(cache_key)
264    }
265
266    /// Execute a cached kernel with optimal configuration
267    pub async fn execute_kernel(
268        &self, 
269        cache_key: &str, 
270        buffers: &[&Buffer], 
271        workgroup_count: [u32; 3]
272    ) -> Result<f64> {
273        let _timer = time_operation(CounterType::KernelExecution);
274
275        let (pipeline, bind_group_layout, optimal_workgroup_size) = {
276            let mut cache = self.kernel_cache.lock().unwrap();
277            let cached = cache.get_mut(cache_key)
278                .ok_or_else(|| CudaRustError::Backend("Kernel not found in cache".to_string()))?;
279            
280            cached.usage_count += 1;
281            (
282                cached.pipeline.clone(),
283                cached.bind_group_layout.clone(),
284                cached.optimal_workgroup_size
285            )
286        };
287
288        // Create bind group with buffers
289        let entries: Vec<BindGroupEntry> = buffers.iter().enumerate()
290            .map(|(i, buffer)| BindGroupEntry {
291                binding: i as u32,
292                resource: buffer.as_entire_binding(),
293            })
294            .collect();
295
296        let bind_group = self.device.create_bind_group(&BindGroupDescriptor {
297            label: Some("Kernel Bind Group"),
298            layout: &bind_group_layout,
299            entries: &entries,
300        });
301
302        // Create command encoder
303        let mut encoder = self.device.create_command_encoder(&CommandEncoderDescriptor {
304            label: Some("Kernel Execution"),
305        });
306
307        // Begin compute pass with optimal configuration
308        {
309            let mut compute_pass = encoder.begin_compute_pass(&ComputePassDescriptor {
310                label: Some("CUDA Kernel Pass"),
311                timestamp_writes: None,
312            });
313
314            compute_pass.set_pipeline(&pipeline);
315            compute_pass.set_bind_group(0, &bind_group, &[]);
316            
317            // Use optimal workgroup size
318            compute_pass.dispatch_workgroups(
319                workgroup_count[0],
320                workgroup_count[1],
321                workgroup_count[2]
322            );
323        }
324
325        // Submit and measure execution time
326        #[cfg(target_arch = "wasm32")]
327        let start_time = web_sys::window()
328            .and_then(|w| w.performance())
329            .map(|p| p.now())
330            .unwrap_or(0.0);
331        #[cfg(not(target_arch = "wasm32"))]
332        let start_instant = std::time::Instant::now();
333
334        self.queue.submit(std::iter::once(encoder.finish()));
335        
336        // Wait for completion
337        self.device.poll(Maintain::Wait);
338
339        #[cfg(target_arch = "wasm32")]
340        let end_time = web_sys::window()
341            .and_then(|w| w.performance())
342            .map(|p| p.now())
343            .unwrap_or(0.0);
344        
345        #[cfg(target_arch = "wasm32")]
346        let execution_time = end_time - start_time;
347        #[cfg(not(target_arch = "wasm32"))]
348        let execution_time = start_instant.elapsed().as_secs_f64() * 1000.0;
349
350        // Update statistics
351        {
352            let mut stats = self.stats.lock().unwrap();
353            stats.kernels_executed += 1;
354            stats.total_execution_time += execution_time;
355        }
356
357        // Update cached kernel statistics
358        {
359            let mut cache = self.kernel_cache.lock().unwrap();
360            if let Some(cached) = cache.get_mut(cache_key) {
361                let alpha = 0.1; // Exponential moving average
362                cached.avg_execution_time = 
363                    alpha * execution_time + (1.0 - alpha) * cached.avg_execution_time;
364            }
365        }
366
367        Ok(execution_time)
368    }
369
370    /// Auto-tune workgroup size for optimal performance
371    fn auto_tune_workgroup_size(
372        &self, 
373        _pipeline: &ComputePipeline, 
374        _bind_group_layout: &BindGroupLayout
375    ) -> Result<[u32; 3]> {
376        // Simplified auto-tuning - in a real implementation, this would
377        // run benchmarks with different workgroup sizes
378        
379        // Common optimal sizes for different GPU architectures
380        let candidate_sizes = [
381            [32, 1, 1],   // Good for memory-bound kernels
382            [64, 1, 1],   // Balanced
383            [128, 1, 1],  // Good for compute-bound kernels
384            [256, 1, 1],  // Maximum for some GPUs
385            [16, 16, 1],  // 2D workgroup
386            [8, 8, 8],    // 3D workgroup
387        ];
388
389        // For now, return a good default - this could be enhanced with
390        // actual performance measurement
391        Ok([64, 1, 1])
392    }
393
394    /// Evict least recently used kernel from cache
395    fn evict_least_used_kernel(&self, cache: &mut HashMap<String, CachedKernel>) {
396        if let Some((key_to_remove, _)) = cache.iter()
397            .min_by_key(|(_, cached)| cached.usage_count) {
398            let key_to_remove = key_to_remove.clone();
399            cache.remove(&key_to_remove);
400        }
401    }
402
403    /// Create an optimized buffer with pooling
404    pub fn create_buffer(&self, size: u64, usage: BufferUsages) -> Result<Buffer> {
405        let _timer = time_operation(CounterType::MemoryAllocation)
406            .with_size(size as usize);
407
408        // Check buffer cache for reusable buffers
409        if self.config.enable_memory_pooling {
410            let mut buffer_cache = self.buffer_cache.lock().unwrap();
411            if let Some(buffers) = buffer_cache.get_mut(&size) {
412                if let Some(buffer) = buffers.pop() {
413                    let mut stats = self.stats.lock().unwrap();
414                    stats.buffer_reuse_count += 1;
415                    return Ok(buffer);
416                }
417            }
418        }
419
420        // Create new buffer
421        let buffer = self.device.create_buffer(&BufferDescriptor {
422            label: Some("CUDA Buffer"),
423            size,
424            usage,
425            mapped_at_creation: false,
426        });
427
428        {
429            let mut stats = self.stats.lock().unwrap();
430            stats.memory_allocations += 1;
431        }
432
433        Ok(buffer)
434    }
435
436    /// Return buffer to cache for reuse
437    pub fn return_buffer(&self, buffer: Buffer) {
438        if !self.config.enable_memory_pooling {
439            return;
440        }
441
442        let size = buffer.size();
443        let mut buffer_cache = self.buffer_cache.lock().unwrap();
444        
445        let buffers = buffer_cache.entry(size).or_default();
446        
447        // Limit cache size to prevent memory bloat
448        if buffers.len() < 10 {
449            buffers.push(buffer);
450        }
451    }
452
453    /// Get comprehensive performance statistics
454    pub fn get_stats(&self) -> BackendStats {
455        self.stats.lock().unwrap().clone()
456    }
457
458    /// Get cache hit ratio
459    pub fn cache_hit_ratio(&self) -> f64 {
460        let stats = self.stats.lock().unwrap();
461        let total = stats.cache_hits + stats.cache_misses;
462        if total == 0 {
463            0.0
464        } else {
465            stats.cache_hits as f64 / total as f64
466        }
467    }
468
469    /// Clear all caches and reset statistics
470    pub fn clear_caches(&self) {
471        self.kernel_cache.lock().unwrap().clear();
472        self.buffer_cache.lock().unwrap().clear();
473        *self.stats.lock().unwrap() = BackendStats::default();
474    }
475
476    /// Generate performance report
477    pub fn performance_report(&self) -> String {
478        let stats = self.get_stats();
479        let cache_ratio = self.cache_hit_ratio();
480        let kernel_cache_size = self.kernel_cache.lock().unwrap().len();
481        let buffer_cache_size: usize = self.buffer_cache.lock().unwrap()
482            .values()
483            .map(|v| v.len())
484            .sum();
485
486        format!(
487            "=== WebGPU Backend Performance Report ===\n\
488            Kernels Executed: {}\n\
489            Cache Hit Ratio: {:.1}%\n\
490            Avg Execution Time: {:.2}ms\n\
491            Total Data Transferred: {:.2}MB\n\
492            Memory Allocations: {}\n\
493            Buffer Reuse Count: {}\n\
494            Kernel Cache Size: {}\n\
495            Buffer Cache Size: {}\n\
496            Memory Pool Stats: {:?}",
497            stats.kernels_executed,
498            cache_ratio * 100.0,
499            if stats.kernels_executed > 0 {
500                stats.total_execution_time / stats.kernels_executed as f64
501            } else {
502                0.0
503            },
504            stats.total_data_transferred as f64 / 1_000_000.0,
505            stats.memory_allocations,
506            stats.buffer_reuse_count,
507            kernel_cache_size,
508            buffer_cache_size,
509            self.memory_pool.stats()
510        )
511    }
512}
513
514#[cfg(test)]
515mod tests {
516    use super::*;
517
518    #[tokio::test]
519    async fn test_webgpu_backend_creation() {
520        // This test may not work in all environments due to WebGPU requirements
521        if let Ok(backend) = OptimizedWebGPUBackend::new().await {
522            assert!(backend.cache_hit_ratio() == 0.0); // No cache hits initially
523        }
524    }
525
526    #[test]
527    fn test_auto_tune_result() {
528        let result = AutoTuneResult {
529            workgroup_size: [64, 1, 1],
530            performance: 1000.0,
531            memory_bandwidth: 0.8,
532            compute_utilization: 0.9,
533        };
534        
535        assert_eq!(result.workgroup_size, [64, 1, 1]);
536        assert_eq!(result.performance, 1000.0);
537    }
538
539    #[test]
540    fn test_backend_stats() {
541        let stats = BackendStats {
542            kernels_executed: 100,
543            cache_hits: 80,
544            cache_misses: 20,
545            total_execution_time: 1000.0,
546            ..Default::default()
547        };
548        
549        assert_eq!(stats.kernels_executed, 100);
550        assert_eq!(stats.cache_hits, 80);
551        assert_eq!(stats.cache_misses, 20);
552    }
553}
cuda_rust_wasm/backend/webgpu_optimized.rs

cuda_rust_wasm/backend/
webgpu_optimized.rs
