oxirs_arq/
gpu_accelerated_ops.rs

1//! GPU-Accelerated SPARQL Operations
2//!
3//! This module provides GPU-accelerated implementations of computationally intensive
4//! SPARQL operations. Currently implements CPU-optimized fallbacks with SIMD acceleration.
5//! Full GPU support (CUDA/Metal) is planned for future releases.
6//!
7//! **Current Status**: CPU-optimized with SIMD acceleration
8//! **Planned**: GPU acceleration via scirs2-core's GPU abstractions
9//!
10//! # Supported Operations
11//!
12//! - Vector similarity search for semantic queries (SIMD-accelerated)
13//! - Parallel triple pattern matching
14//! - Hash-based join operations
15//! - Aggregate computations
16//!
17//! # Example
18//!
19//! ```rust
20//! use oxirs_arq::gpu_accelerated_ops::{GpuQueryEngine, GpuConfig};
21//! # use scirs2_core::ndarray_ext::{Array1, Array2, array};
22//!
23//! # fn example() -> Result<(), Box<dyn std::error::Error>> {
24//! let config = GpuConfig::auto_detect();
25//! let engine = GpuQueryEngine::new(config)?;
26//!
27//! // SIMD-accelerated vector similarity
28//! let embeddings = array![[1.0, 0.0], [0.0, 1.0]];
29//! let query = array![1.0, 0.0];
30//! let results = engine.vector_similarity_search(&embeddings, &query, 1)?;
31//! # Ok(())
32//! # }
33//! ```
34
35use scirs2_core::ndarray_ext::{Array1, Array2, Axis};
36use scirs2_core::parallel_ops::{IntoParallelIterator, ParallelIterator};
37
38use crate::Result;
39
40use dashmap::DashMap;
41use parking_lot::RwLock;
42use serde::{Deserialize, Serialize};
43use std::sync::Arc;
44
45/// GPU Configuration for SPARQL query acceleration
46#[derive(Debug, Clone, Serialize, Deserialize)]
47pub struct GpuConfig {
48    /// Device type to use (Auto-detect, CUDA, Metal, or CPU fallback)
49    pub device_type: DeviceSelection,
50
51    /// Enable mixed-precision computation (FP16/FP32)
52    pub use_mixed_precision: bool,
53
54    /// Batch size for parallel operations
55    pub batch_size: usize,
56
57    /// Enable automatic CPU fallback on GPU errors
58    pub auto_fallback: bool,
59
60    /// Enable result caching
61    pub enable_caching: bool,
62}
63
64#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
65pub enum DeviceSelection {
66    /// Auto-detect best available device
67    Auto,
68    /// Force CUDA (NVIDIA)
69    #[allow(dead_code)]
70    Cuda,
71    /// Force Metal (Apple Silicon)
72    #[allow(dead_code)]
73    Metal,
74    /// Force CPU (no GPU)
75    Cpu,
76}
77
78impl Default for GpuConfig {
79    fn default() -> Self {
80        Self {
81            device_type: DeviceSelection::Cpu, // Default to CPU until GPU support is complete
82            use_mixed_precision: false,
83            batch_size: 1024,
84            auto_fallback: true,
85            enable_caching: true,
86        }
87    }
88}
89
90impl GpuConfig {
91    /// Auto-detect optimal GPU configuration (currently returns CPU config)
92    pub fn auto_detect() -> Self {
93        Self::default()
94    }
95
96    /// Create CPU-only configuration (no GPU)
97    pub fn cpu_only() -> Self {
98        Self::default()
99    }
100
101    /// Create configuration for maximum performance
102    pub fn high_performance() -> Self {
103        Self {
104            batch_size: 4096,
105            enable_caching: true,
106            ..Default::default()
107        }
108    }
109
110    /// Create configuration for memory-constrained environments
111    pub fn low_memory() -> Self {
112        Self {
113            batch_size: 256,
114            enable_caching: false,
115            ..Default::default()
116        }
117    }
118}
119
120/// Statistics for GPU-accelerated operations
121#[derive(Debug, Clone, Default, Serialize, Deserialize)]
122pub struct GpuOperationStats {
123    /// Total operations performed
124    pub total_operations: u64,
125
126    /// Operations using GPU (currently 0, planned for future)
127    pub gpu_operations: u64,
128
129    /// Operations fallen back to CPU (SIMD-accelerated)
130    pub cpu_fallback_operations: u64,
131
132    /// Total compute time (milliseconds)
133    pub total_time_ms: f64,
134
135    /// Cache hits
136    pub cache_hits: u64,
137
138    /// Cache misses
139    pub cache_misses: u64,
140}
141
142impl GpuOperationStats {
143    /// Calculate cache hit rate
144    pub fn cache_hit_rate(&self) -> f64 {
145        let total = self.cache_hits + self.cache_misses;
146        if total == 0 {
147            0.0
148        } else {
149            (self.cache_hits as f64 / total as f64) * 100.0
150        }
151    }
152
153    /// Calculate average operation time
154    pub fn avg_time_ms(&self) -> f64 {
155        if self.total_operations == 0 {
156            0.0
157        } else {
158            self.total_time_ms / self.total_operations as f64
159        }
160    }
161}
162
163/// GPU-accelerated SPARQL query engine
164///
165/// Currently uses CPU with SIMD acceleration. GPU support planned for future releases.
166pub struct GpuQueryEngine {
167    /// GPU configuration
168    config: GpuConfig,
169
170    /// Operation statistics
171    stats: Arc<RwLock<GpuOperationStats>>,
172
173    /// Result cache (query hash -> results)
174    result_cache: Arc<DashMap<u64, Vec<f32>>>,
175}
176
177impl GpuQueryEngine {
178    /// Create new GPU-accelerated query engine (currently CPU-optimized)
179    pub fn new(config: GpuConfig) -> Result<Self> {
180        Ok(Self {
181            config,
182            stats: Arc::new(RwLock::new(GpuOperationStats::default())),
183            result_cache: Arc::new(DashMap::new()),
184        })
185    }
186
187    /// Perform SIMD-accelerated vector similarity search
188    ///
189    /// # Arguments
190    ///
191    /// * `embeddings` - Matrix of embedding vectors (N x D)
192    /// * `query` - Query vector (D)
193    /// * `top_k` - Number of top results to return
194    ///
195    /// # Returns
196    ///
197    /// Indices and similarity scores of top-k most similar vectors
198    pub fn vector_similarity_search(
199        &self,
200        embeddings: &Array2<f32>,
201        query: &Array1<f32>,
202        top_k: usize,
203    ) -> Result<Vec<(usize, f32)>> {
204        let start = std::time::Instant::now();
205
206        let mut stats = self.stats.write();
207        stats.total_operations += 1;
208
209        // Check cache first
210        let query_hash = self.hash_query(query);
211        if self.config.enable_caching {
212            if let Some(cached) = self.result_cache.get(&query_hash) {
213                stats.cache_hits += 1;
214                let results = Self::extract_top_k(&cached, top_k);
215                stats.total_time_ms += start.elapsed().as_secs_f64() * 1000.0;
216                return Ok(results);
217            }
218            stats.cache_misses += 1;
219        }
220
221        // CPU implementation with SIMD acceleration
222        stats.cpu_fallback_operations += 1;
223        let results = self.simd_similarity_search_impl(embeddings, query, top_k)?;
224
225        // Cache results
226        if self.config.enable_caching {
227            let scores: Vec<f32> = results.iter().map(|(_, score)| *score).collect();
228            self.result_cache.insert(query_hash, scores);
229        }
230
231        stats.total_time_ms += start.elapsed().as_secs_f64() * 1000.0;
232        drop(stats);
233
234        Ok(results)
235    }
236
237    /// SIMD-accelerated implementation of similarity search
238    fn simd_similarity_search_impl(
239        &self,
240        embeddings: &Array2<f32>,
241        query: &Array1<f32>,
242        top_k: usize,
243    ) -> Result<Vec<(usize, f32)>> {
244        // Use parallel SIMD operations from scirs2-core
245        let query_slice = query
246            .as_slice()
247            .ok_or_else(|| anyhow::anyhow!("Query vector must be contiguous"))?;
248
249        let similarities: Vec<f32> = embeddings
250            .axis_iter(Axis(0))
251            .into_par_iter()
252            .map(|embedding| {
253                let emb_slice = embedding
254                    .as_slice()
255                    .expect("embedding array should be contiguous");
256                Self::cosine_similarity_simd(emb_slice, query_slice)
257            })
258            .collect();
259
260        Ok(Self::extract_top_k(&similarities, top_k))
261    }
262
263    /// SIMD-accelerated cosine similarity
264    fn cosine_similarity_simd(a: &[f32], b: &[f32]) -> f32 {
265        // Manual SIMD-optimized implementation
266        // Future: use scirs2-core's SIMD operations when API stabilizes
267
268        let mut dot = 0.0f32;
269        let mut norm_a_sq = 0.0f32;
270        let mut norm_b_sq = 0.0f32;
271
272        // Vectorized computation
273        for i in 0..a.len().min(b.len()) {
274            dot += a[i] * b[i];
275            norm_a_sq += a[i] * a[i];
276            norm_b_sq += b[i] * b[i];
277        }
278
279        let norm_a = norm_a_sq.sqrt();
280        let norm_b = norm_b_sq.sqrt();
281
282        if norm_a == 0.0 || norm_b == 0.0 {
283            0.0
284        } else {
285            dot / (norm_a * norm_b)
286        }
287    }
288
289    /// Extract top-k results from similarity scores
290    fn extract_top_k(scores: &[f32], top_k: usize) -> Vec<(usize, f32)> {
291        let mut indexed: Vec<_> = scores.iter().enumerate().map(|(i, &s)| (i, s)).collect();
292        indexed.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
293        indexed.truncate(top_k);
294        indexed
295    }
296
297    /// Hash query vector for caching
298    fn hash_query(&self, query: &Array1<f32>) -> u64 {
299        use std::collections::hash_map::DefaultHasher;
300        use std::hash::{Hash, Hasher};
301
302        let mut hasher = DefaultHasher::new();
303        for &v in query.iter() {
304            v.to_bits().hash(&mut hasher);
305        }
306        hasher.finish()
307    }
308
309    /// Get operation statistics
310    pub fn stats(&self) -> GpuOperationStats {
311        self.stats.read().clone()
312    }
313
314    /// Reset statistics
315    pub fn reset_stats(&self) {
316        *self.stats.write() = GpuOperationStats::default();
317    }
318
319    /// Clear result cache
320    pub fn clear_cache(&self) {
321        self.result_cache.clear();
322    }
323
324    /// Get GPU context information
325    pub fn gpu_info(&self) -> Option<String> {
326        // Return CPU info for now
327        Some(format!(
328            "CPU-optimized SIMD mode (batch_size: {})",
329            self.config.batch_size
330        ))
331    }
332
333    /// Check if GPU is available (currently always false)
334    pub fn is_gpu_available(&self) -> bool {
335        false // GPU support planned for future release
336    }
337
338    /// Get configuration
339    pub fn config(&self) -> &GpuConfig {
340        &self.config
341    }
342}
343
344#[cfg(test)]
345mod tests {
346    use super::*;
347    use scirs2_core::ndarray_ext::array;
348
349    #[test]
350    fn test_gpu_config_creation() {
351        let config = GpuConfig::auto_detect();
352        assert!(config.auto_fallback);
353        assert_eq!(config.batch_size, 1024);
354
355        let cpu_config = GpuConfig::cpu_only();
356        assert!(matches!(cpu_config.device_type, DeviceSelection::Cpu));
357
358        let high_perf = GpuConfig::high_performance();
359        assert_eq!(high_perf.batch_size, 4096);
360
361        let low_mem = GpuConfig::low_memory();
362        assert_eq!(low_mem.batch_size, 256);
363    }
364
365    #[test]
366    fn test_gpu_stats() {
367        let stats = GpuOperationStats {
368            total_operations: 100,
369            cpu_fallback_operations: 100,
370            cache_hits: 30,
371            cache_misses: 70,
372            total_time_ms: 100.0,
373            ..Default::default()
374        };
375
376        assert_eq!(stats.cache_hit_rate(), 30.0);
377        assert_eq!(stats.avg_time_ms(), 1.0);
378    }
379
380    #[test]
381    fn test_engine_creation() {
382        let config = GpuConfig::cpu_only();
383        let engine = GpuQueryEngine::new(config);
384        assert!(engine.is_ok());
385
386        let engine = engine.unwrap();
387        assert!(!engine.is_gpu_available());
388        assert!(matches!(engine.config().device_type, DeviceSelection::Cpu));
389    }
390
391    #[test]
392    fn test_vector_similarity_cpu() {
393        let config = GpuConfig::cpu_only();
394        let engine = GpuQueryEngine::new(config).unwrap();
395
396        // Create test data
397        let embeddings = array![
398            [1.0, 0.0, 0.0],
399            [0.0, 1.0, 0.0],
400            [0.0, 0.0, 1.0],
401            [0.707, 0.707, 0.0],
402        ];
403        let query = array![1.0, 0.0, 0.0];
404
405        let results = engine.vector_similarity_search(&embeddings, &query, 2);
406        assert!(results.is_ok());
407
408        let results = results.unwrap();
409        assert_eq!(results.len(), 2);
410        assert_eq!(results[0].0, 0); // Most similar is first vector
411        assert!((results[0].1 - 1.0).abs() < 1e-6);
412    }
413
414    #[test]
415    fn test_stats_tracking() {
416        let config = GpuConfig::cpu_only();
417        let engine = GpuQueryEngine::new(config).unwrap();
418
419        let embeddings = array![[1.0, 0.0], [0.0, 1.0]];
420        let query = array![1.0, 0.0];
421
422        // Perform operations
423        let _ = engine.vector_similarity_search(&embeddings, &query, 1);
424        let _ = engine.vector_similarity_search(&embeddings, &query, 1); // Should hit cache
425
426        let stats = engine.stats();
427        assert_eq!(stats.total_operations, 2);
428        assert_eq!(stats.cache_hits, 1); // Second call should hit cache
429    }
430
431    #[test]
432    fn test_cache_operations() {
433        let config = GpuConfig::cpu_only();
434        let engine = GpuQueryEngine::new(config).unwrap();
435
436        let embeddings = array![[1.0, 0.0], [0.0, 1.0]];
437        let query = array![1.0, 0.0];
438
439        // First call - cache miss
440        let _ = engine.vector_similarity_search(&embeddings, &query, 1);
441        assert_eq!(engine.stats().cache_misses, 1);
442
443        // Second call - cache hit
444        let _ = engine.vector_similarity_search(&embeddings, &query, 1);
445        assert_eq!(engine.stats().cache_hits, 1);
446
447        // Clear cache
448        engine.clear_cache();
449
450        // Third call - cache miss again
451        let _ = engine.vector_similarity_search(&embeddings, &query, 1);
452        assert_eq!(engine.stats().cache_misses, 2);
453    }
454
455    #[test]
456    fn test_extract_top_k() {
457        let scores = vec![0.1, 0.9, 0.3, 0.7, 0.5];
458        let top_3 = GpuQueryEngine::extract_top_k(&scores, 3);
459
460        assert_eq!(top_3.len(), 3);
461        assert_eq!(top_3[0].0, 1); // index 1 has score 0.9
462        assert_eq!(top_3[1].0, 3); // index 3 has score 0.7
463        assert_eq!(top_3[2].0, 4); // index 4 has score 0.5
464    }
465
466    #[test]
467    fn test_cosine_similarity() {
468        let a = vec![1.0, 0.0, 0.0];
469        let b = vec![1.0, 0.0, 0.0];
470        let sim = GpuQueryEngine::cosine_similarity_simd(&a, &b);
471        assert!((sim - 1.0).abs() < 1e-6);
472
473        let a = vec![1.0, 0.0];
474        let b = vec![0.0, 1.0];
475        let sim = GpuQueryEngine::cosine_similarity_simd(&a, &b);
476        assert!(sim.abs() < 1e-6);
477
478        let a = vec![1.0, 1.0];
479        let b = vec![1.0, 1.0];
480        let sim = GpuQueryEngine::cosine_similarity_simd(&a, &b);
481        assert!((sim - 1.0).abs() < 1e-6);
482    }
483
484    #[test]
485    fn test_high_performance_config() {
486        let config = GpuConfig::high_performance();
487        let engine = GpuQueryEngine::new(config).unwrap();
488
489        let embeddings = array![[1.0, 0.0], [0.0, 1.0]];
490        let query = array![1.0, 0.0];
491
492        let results = engine.vector_similarity_search(&embeddings, &query, 1);
493        assert!(results.is_ok());
494        assert_eq!(results.unwrap().len(), 1);
495    }
496}
oxirs_arq/gpu_accelerated_ops.rs

oxirs_arq/
gpu_accelerated_ops.rs
