scirs2_optimize/gpu/
mod.rs

1//! GPU acceleration for optimization algorithms
2//!
3//! This module provides GPU-accelerated implementations of various optimization
4//! algorithms and supporting functionality. It leverages scirs2-core's GPU
5//! abstractions to provide high-performance computing capabilities.
6
7use crate::error::{ScirsError, ScirsResult};
8use ndarray::{Array1, Array2, ArrayView1};
9use scirs2_core::gpu::{GpuBackend, GpuBuffer, GpuContext};
10use statrs::statistics::Statistics;
11use std::sync::Arc;
12
13// Note: Error conversion handled through scirs2_core::error system
14// GPU errors are automatically converted via CoreError type alias
15
16// Real GPU array type backed by scirs2-core (using GpuBuffer instead of GpuArray)
17pub type OptimGpuArray<T> = GpuBuffer<T>;
18
19pub mod acceleration;
20pub mod cuda_kernels;
21pub mod memory_management;
22pub mod tensor_core_optimization;
23
24/// GPU-accelerated optimization configuration
25#[derive(Clone)]
26pub struct GpuOptimizationConfig {
27    /// GPU context to use
28    pub context: Arc<GpuContext>,
29    /// Batch size for parallel evaluation
30    pub batch_size: usize,
31    /// Memory limit in bytes
32    pub memory_limit: Option<usize>,
33    /// Whether to use tensor cores (if available)
34    pub use_tensor_cores: bool,
35    /// Precision mode (f32 or f64)
36    pub precision: GpuPrecision,
37    /// Stream count for concurrent execution
38    pub stream_count: usize,
39}
40
41impl Default for GpuOptimizationConfig {
42    fn default() -> Self {
43        Self {
44            context: Arc::new(GpuContext::new(GpuBackend::default()).unwrap_or_else(|_| {
45                // Fallback to CPU if GPU context creation fails
46                GpuContext::new(GpuBackend::Cpu).expect("CPU backend should always work")
47            })),
48            batch_size: 1024,
49            memory_limit: None,
50            use_tensor_cores: true,
51            precision: GpuPrecision::F64,
52            stream_count: 4,
53        }
54    }
55}
56
57/// GPU precision modes
58#[derive(Debug, Clone, Copy, PartialEq)]
59pub enum GpuPrecision {
60    F32,
61    F64,
62    Mixed, // Use F32 for computation, F64 for accumulation
63}
64
65/// GPU-accelerated function evaluation interface
66pub trait GpuFunction {
67    /// Evaluate function on GPU for a batch of points
68    fn evaluate_batch_gpu(&self, points: &OptimGpuArray<f64>) -> ScirsResult<OptimGpuArray<f64>>;
69
70    /// Evaluate gradient on GPU for a batch of points
71    fn gradient_batch_gpu(&self, points: &OptimGpuArray<f64>) -> ScirsResult<OptimGpuArray<f64>>;
72
73    /// Evaluate hessian on GPU (if supported)
74    fn hessian_batch_gpu(&self, points: &OptimGpuArray<f64>) -> ScirsResult<OptimGpuArray<f64>> {
75        Err(ScirsError::NotImplementedError(
76            scirs2_core::error::ErrorContext::new("Hessian evaluation not implemented".to_string()),
77        ))
78    }
79
80    /// Check if function supports GPU acceleration
81    fn supports_gpu(&self) -> bool {
82        true
83    }
84}
85
86/// GPU-accelerated optimization context
87pub struct GpuOptimizationContext {
88    config: GpuOptimizationConfig,
89    context: Arc<GpuContext>,
90    memory_pool: memory_management::GpuMemoryPool,
91}
92
93impl GpuOptimizationContext {
94    /// Create a new GPU optimization context
95    pub fn new(config: GpuOptimizationConfig) -> ScirsResult<Self> {
96        let context = config.context.clone();
97        let memory_pool = memory_management::GpuMemoryPool::new_stub();
98
99        Ok(Self {
100            config,
101            context,
102            memory_pool,
103        })
104    }
105
106    /// Get the configuration
107    pub fn config(&self) -> &GpuOptimizationConfig {
108        &self.config
109    }
110
111    /// Get the GPU context
112    pub fn context(&self) -> &Arc<GpuContext> {
113        &self.context
114    }
115
116    /// Allocate GPU memory for optimization data
117    pub fn allocate_workspace(
118        &mut self,
119        size: usize,
120    ) -> ScirsResult<memory_management::GpuWorkspace> {
121        self.memory_pool.allocate_workspace(size)
122    }
123
124    /// Transfer data from CPU to GPU using scirs2-core GPU abstractions
125    pub fn transfer_to_gpu<T>(&self, data: &Array2<T>) -> ScirsResult<OptimGpuArray<T>>
126    where
127        T: Clone + Send + Sync + 'static + scirs2_core::GpuDataType,
128    {
129        // Use scirs2-core GPU buffer creation
130        let shape = data.dim();
131        let total_size = shape.0 * shape.1;
132        let flat_data = data.as_slice().unwrap();
133
134        // Create a GPU buffer with the flattened data
135        // Note: GpuBuffer creation API may vary based on scirs2-core implementation
136        // For now, we'll return an error since we can't directly create a GpuBuffer
137        Err(ScirsError::NotImplementedError(
138            scirs2_core::error::ErrorContext::new(
139                "GPU buffer creation not yet implemented".to_string(),
140            ),
141        ))
142    }
143
144    /// Transfer data from GPU to CPU using scirs2-core GPU abstractions
145    pub fn transfer_from_gpu<T>(&self, gpu_data: &OptimGpuArray<T>) -> ScirsResult<Array2<T>>
146    where
147        T: Clone + Send + Sync + Default + 'static + scirs2_core::GpuDataType,
148    {
149        // For now, assume shape is known from context
150        // In real implementation, shape would be stored with the buffer
151        // This is a simplification since GpuBuffer doesn't have shape method
152        let total_size = gpu_data.len();
153        let dims = (total_size as f64).sqrt() as usize;
154
155        // Allocate host memory and copy from GPU
156        let mut host_data = vec![T::default(); total_size];
157        gpu_data.copy_to_host(&mut host_data)?;
158
159        // Reshape to ndarray (assume square for now)
160        Array2::from_shape_vec((dims, dims), host_data).map_err(|e| {
161            ScirsError::ComputationError(scirs2_core::error::ErrorContext::new(format!(
162                "Shape error: {}",
163                e
164            )))
165        })
166    }
167
168    /// Upload array to GPU (alias for transfer_to_gpu)
169    pub fn upload_array<T>(&self, data: &Array2<T>) -> ScirsResult<OptimGpuArray<T>>
170    where
171        T: Clone + Send + Sync + 'static + scirs2_core::GpuDataType,
172    {
173        self.transfer_to_gpu(data)
174    }
175
176    /// Download array from GPU (alias for transfer_from_gpu)
177    pub fn download_array<T>(&self, gpu_data: &OptimGpuArray<T>) -> ScirsResult<Array2<T>>
178    where
179        T: Clone + Send + Sync + Default + 'static + scirs2_core::GpuDataType,
180    {
181        self.transfer_from_gpu(gpu_data)
182    }
183
184    /// Execute batch function evaluation
185    pub fn evaluate_function_batch<F>(
186        &self,
187        function: &F,
188        points: &Array2<f64>,
189    ) -> ScirsResult<Array1<f64>>
190    where
191        F: GpuFunction,
192    {
193        if !function.supports_gpu() {
194            return Err(ScirsError::InvalidInput(
195                scirs2_core::error::ErrorContext::new(
196                    "Function does not support GPU acceleration".to_string(),
197                ),
198            ));
199        }
200
201        let gpu_points = self.transfer_to_gpu(points)?;
202        let gpu_results = function.evaluate_batch_gpu(&gpu_points)?;
203        let cpu_results = self.transfer_from_gpu(&gpu_results)?;
204
205        // Convert 2D result to 1D (assuming single output per point)
206        Ok(cpu_results.column(0).to_owned())
207    }
208
209    /// Execute batch gradient evaluation
210    pub fn evaluate_gradient_batch<F>(
211        &self,
212        function: &F,
213        points: &Array2<f64>,
214    ) -> ScirsResult<Array2<f64>>
215    where
216        F: GpuFunction,
217    {
218        if !function.supports_gpu() {
219            return Err(ScirsError::InvalidInput(
220                scirs2_core::error::ErrorContext::new(
221                    "Function does not support GPU acceleration".to_string(),
222                ),
223            ));
224        }
225
226        let gpu_points = self.transfer_to_gpu(points)?;
227        let gpu_gradients = function.gradient_batch_gpu(&gpu_points)?;
228        self.transfer_from_gpu(&gpu_gradients)
229    }
230
231    /// Compute gradient using finite differences
232    pub fn compute_gradient_finite_diff<F>(
233        &self,
234        function: &F,
235        x: &Array1<f64>,
236        h: f64,
237    ) -> ScirsResult<Array1<f64>>
238    where
239        F: Fn(&ArrayView1<f64>) -> f64,
240    {
241        let n = x.len();
242        let mut gradient = Array1::zeros(n);
243
244        // CPU finite differences for now - could be GPU-accelerated in the future
245        for i in 0..n {
246            let mut x_plus = x.clone();
247            let mut x_minus = x.clone();
248            x_plus[i] += h;
249            x_minus[i] -= h;
250
251            gradient[i] = (function(&x_plus.view()) - function(&x_minus.view())) / (2.0 * h);
252        }
253
254        Ok(gradient)
255    }
256
257    /// Compute search direction (simple steepest descent for now)
258    pub fn compute_search_direction(&self, gradient: &Array1<f64>) -> ScirsResult<Array1<f64>> {
259        // Simple steepest descent - could be enhanced with GPU-accelerated quasi-Newton methods
260        Ok(-gradient.clone())
261    }
262}
263
264/// GPU-accelerated optimization algorithms
265pub mod algorithms {
266    use super::*;
267    use crate::result::OptimizeResults;
268
269    /// GPU-accelerated differential evolution
270    pub struct GpuDifferentialEvolution {
271        context: GpuOptimizationContext,
272        population_size: usize,
273        max_nit: usize,
274        f_scale: f64,
275        crossover_rate: f64,
276    }
277
278    impl GpuDifferentialEvolution {
279        /// Create a new GPU-accelerated differential evolution optimizer
280        pub fn new(
281            context: GpuOptimizationContext,
282            population_size: usize,
283            max_nit: usize,
284        ) -> Self {
285            Self {
286                context,
287                population_size,
288                max_nit,
289                f_scale: 0.8,
290                crossover_rate: 0.7,
291            }
292        }
293
294        /// Set mutation scale factor
295        pub fn with_f_scale(mut self, f_scale: f64) -> Self {
296            self.f_scale = f_scale;
297            self
298        }
299
300        /// Set crossover rate
301        pub fn with_crossover_rate(mut self, crossover_rate: f64) -> Self {
302            self.crossover_rate = crossover_rate;
303            self
304        }
305
306        /// Optimize function using GPU-accelerated differential evolution
307        pub fn optimize<F>(
308            &mut self,
309            function: &F,
310            bounds: &[(f64, f64)],
311        ) -> ScirsResult<OptimizeResults<f64>>
312        where
313            F: GpuFunction,
314        {
315            let dims = bounds.len();
316
317            // Initialize population on GPU
318            let mut population = self.initialize_population_gpu(bounds)?;
319            let mut fitness = self.evaluate_population_gpu(function, &population)?;
320
321            let mut best_idx = 0;
322            let mut best_fitness = fitness[0];
323            for (i, &f) in fitness.iter().enumerate() {
324                if f < best_fitness {
325                    best_fitness = f;
326                    best_idx = i;
327                }
328            }
329
330            let mut function_evaluations = self.population_size;
331
332            for iteration in 0..self.max_nit {
333                // Generate trial population using GPU kernels
334                let trial_population = self.generate_trial_population_gpu(&population)?;
335                let trial_fitness = self.evaluate_population_gpu(function, &trial_population)?;
336
337                // Selection on GPU
338                self.selection_gpu(
339                    &mut population,
340                    &mut fitness,
341                    &trial_population,
342                    &trial_fitness,
343                )?;
344
345                function_evaluations += self.population_size;
346
347                // Update best solution
348                for (i, &f) in fitness.iter().enumerate() {
349                    if f < best_fitness {
350                        best_fitness = f;
351                        best_idx = i;
352                    }
353                }
354
355                // Convergence check (simplified)
356                if iteration % 10 == 0 {
357                    let fitness_std = self.calculate_fitness_std(&fitness);
358                    if fitness_std < 1e-12 {
359                        break;
360                    }
361                }
362            }
363
364            // Transfer best solution back to CPU
365            let best_x = population.row(best_idx).to_owned();
366
367            Ok(OptimizeResults::<f64> {
368                x: best_x,
369                fun: best_fitness,
370                success: true,
371                message: "GPU differential evolution completed".to_string(),
372                nit: self.max_nit,
373                nfev: function_evaluations,
374                ..OptimizeResults::default()
375            })
376        }
377
378        fn initialize_population_gpu(&self, bounds: &[(f64, f64)]) -> ScirsResult<Array2<f64>> {
379            use rand::Rng;
380            let mut rng = rand::rng();
381
382            let dims = bounds.len();
383            let mut population = Array2::zeros((self.population_size, dims));
384
385            for i in 0..self.population_size {
386                for j in 0..dims {
387                    let (low, high) = bounds[j];
388                    population[[i, j]] = rng.gen_range(low..=high);
389                }
390            }
391
392            Ok(population)
393        }
394
395        fn evaluate_population_gpu<F>(
396            &self,
397            function: &F,
398            population: &Array2<f64>,
399        ) -> ScirsResult<Array1<f64>>
400        where
401            F: GpuFunction,
402        {
403            self.context.evaluate_function_batch(function, population)
404        }
405
406        fn generate_trial_population_gpu(
407            &self,
408            population: &Array2<f64>,
409        ) -> ScirsResult<Array2<f64>> {
410            // For now, implement on CPU and transfer to GPU
411            // In a full implementation, this would use GPU kernels
412            use rand::Rng;
413            let mut rng = rand::rng();
414
415            let (pop_size, dims) = population.dim();
416            let mut trial_population = Array2::zeros((pop_size, dims));
417
418            for i in 0..pop_size {
419                // Select three random individuals different from current
420                let mut indices = Vec::new();
421                while indices.len() < 3 {
422                    let idx = rng.gen_range(0..pop_size);
423                    if idx != i && !indices.contains(&idx) {
424                        indices.push(idx);
425                    }
426                }
427
428                let [a, b, c] = [indices[0], indices[1], indices[2]];
429
430                // Mutation and crossover
431                let j_rand = rng.gen_range(0..dims);
432                for j in 0..dims {
433                    if rng.gen_range(0.0..1.0) < self.crossover_rate || j == j_rand {
434                        trial_population[[i, j]] = population[[a, j]]
435                            + self.f_scale * (population[[b, j]] - population[[c, j]]);
436                    } else {
437                        trial_population[[i, j]] = population[[i, j]];
438                    }
439                }
440            }
441
442            Ok(trial_population)
443        }
444
445        fn selection_gpu(
446            &self,
447            population: &mut Array2<f64>,
448            fitness: &mut Array1<f64>,
449            trial_population: &Array2<f64>,
450            trial_fitness: &Array1<f64>,
451        ) -> ScirsResult<()> {
452            for i in 0..population.nrows() {
453                if trial_fitness[i] <= fitness[i] {
454                    for j in 0..population.ncols() {
455                        population[[i, j]] = trial_population[[i, j]];
456                    }
457                    fitness[i] = trial_fitness[i];
458                }
459            }
460            Ok(())
461        }
462
463        fn calculate_fitness_std(&self, fitness: &Array1<f64>) -> f64 {
464            let mean = fitness.view().mean();
465            let variance =
466                fitness.iter().map(|&x| (x - mean).powi(2)).sum::<f64>() / fitness.len() as f64;
467            variance.sqrt()
468        }
469    }
470
471    /// GPU-accelerated particle swarm optimization
472    pub struct GpuParticleSwarm {
473        context: GpuOptimizationContext,
474        swarm_size: usize,
475        max_nit: usize,
476        w: f64,  // Inertia weight
477        c1: f64, // Cognitive parameter
478        c2: f64, // Social parameter
479    }
480
481    impl GpuParticleSwarm {
482        /// Create a new GPU-accelerated particle swarm optimizer
483        pub fn new(context: GpuOptimizationContext, swarm_size: usize, max_nit: usize) -> Self {
484            Self {
485                context,
486                swarm_size,
487                max_nit,
488                w: 0.729,
489                c1: 1.49445,
490                c2: 1.49445,
491            }
492        }
493
494        /// Set PSO parameters
495        pub fn with_parameters(mut self, w: f64, c1: f64, c2: f64) -> Self {
496            self.w = w;
497            self.c1 = c1;
498            self.c2 = c2;
499            self
500        }
501
502        /// Optimize function using GPU-accelerated particle swarm optimization
503        pub fn optimize<F>(
504            &mut self,
505            function: &F,
506            bounds: &[(f64, f64)],
507        ) -> ScirsResult<OptimizeResults<f64>>
508        where
509            F: GpuFunction,
510        {
511            let dims = bounds.len();
512
513            // Initialize swarm positions and velocities
514            let mut positions = self.initialize_positions_gpu(bounds)?;
515            let mut velocities = Array2::zeros((self.swarm_size, dims));
516            let mut personal_best = positions.clone();
517            let mut personal_best_fitness = self.evaluate_population_gpu(function, &positions)?;
518
519            // Find global best
520            let mut global_best_idx = 0;
521            let mut global_best_fitness = personal_best_fitness[0];
522            for (i, &fitness) in personal_best_fitness.iter().enumerate() {
523                if fitness < global_best_fitness {
524                    global_best_fitness = fitness;
525                    global_best_idx = i;
526                }
527            }
528            let mut global_best = personal_best.row(global_best_idx).to_owned();
529
530            let mut function_evaluations = self.swarm_size;
531
532            for iteration in 0..self.max_nit {
533                // Update velocities and positions on GPU
534                self.update_swarm_gpu(
535                    &mut positions,
536                    &mut velocities,
537                    &personal_best,
538                    &global_best,
539                    bounds,
540                )?;
541
542                // Evaluate new positions
543                let fitness = self.evaluate_population_gpu(function, &positions)?;
544                function_evaluations += self.swarm_size;
545
546                // Update personal bests
547                for i in 0..self.swarm_size {
548                    if fitness[i] < personal_best_fitness[i] {
549                        personal_best_fitness[i] = fitness[i];
550                        for j in 0..dims {
551                            personal_best[[i, j]] = positions[[i, j]];
552                        }
553
554                        // Update global best
555                        if fitness[i] < global_best_fitness {
556                            global_best_fitness = fitness[i];
557                            global_best = positions.row(i).to_owned();
558                        }
559                    }
560                }
561
562                // Convergence check
563                if iteration % 10 == 0 {
564                    let fitness_std = self.calculate_fitness_std(&personal_best_fitness);
565                    if fitness_std < 1e-12 {
566                        break;
567                    }
568                }
569            }
570
571            Ok(OptimizeResults::<f64> {
572                x: global_best,
573                fun: global_best_fitness,
574                success: true,
575                message: "GPU particle swarm optimization completed".to_string(),
576                nit: self.max_nit,
577                nfev: function_evaluations,
578                ..OptimizeResults::default()
579            })
580        }
581
582        fn initialize_positions_gpu(&self, bounds: &[(f64, f64)]) -> ScirsResult<Array2<f64>> {
583            use rand::Rng;
584            let mut rng = rand::rng();
585
586            let dims = bounds.len();
587            let mut positions = Array2::zeros((self.swarm_size, dims));
588
589            for i in 0..self.swarm_size {
590                for j in 0..dims {
591                    let (low, high) = bounds[j];
592                    positions[[i, j]] = rng.gen_range(low..=high);
593                }
594            }
595
596            Ok(positions)
597        }
598
599        fn evaluate_population_gpu<F>(
600            &self,
601            function: &F,
602            population: &Array2<f64>,
603        ) -> ScirsResult<Array1<f64>>
604        where
605            F: GpuFunction,
606        {
607            self.context.evaluate_function_batch(function, population)
608        }
609
610        fn update_swarm_gpu(
611            &self,
612            positions: &mut Array2<f64>,
613            velocities: &mut Array2<f64>,
614            personal_best: &Array2<f64>,
615            global_best: &Array1<f64>,
616            bounds: &[(f64, f64)],
617        ) -> ScirsResult<()> {
618            use rand::Rng;
619            let mut rng = rand::rng();
620
621            let (swarm_size, dims) = positions.dim();
622
623            for i in 0..swarm_size {
624                for j in 0..dims {
625                    let r1: f64 = rng.gen_range(0.0..1.0);
626                    let r2: f64 = rng.gen_range(0.0..1.0);
627
628                    // Update velocity
629                    velocities[[i, j]] = self.w * velocities[[i, j]]
630                        + self.c1 * r1 * (personal_best[[i, j]] - positions[[i, j]])
631                        + self.c2 * r2 * (global_best[j] - positions[[i, j]]);
632
633                    // Update position
634                    positions[[i, j]] += velocities[[i, j]];
635
636                    // Apply bounds
637                    let (low, high) = bounds[j];
638                    if positions[[i, j]] < low {
639                        positions[[i, j]] = low;
640                        velocities[[i, j]] = 0.0;
641                    } else if positions[[i, j]] > high {
642                        positions[[i, j]] = high;
643                        velocities[[i, j]] = 0.0;
644                    }
645                }
646            }
647
648            Ok(())
649        }
650
651        fn calculate_fitness_std(&self, fitness: &Array1<f64>) -> f64 {
652            let mean = fitness.view().mean();
653            let variance =
654                fitness.iter().map(|&x| (x - mean).powi(2)).sum::<f64>() / fitness.len() as f64;
655            variance.sqrt()
656        }
657    }
658}
659
660/// Utility functions for GPU optimization
661pub mod utils {
662    use super::*;
663
664    /// Check if GPU acceleration is available and beneficial
665    pub fn should_use_gpu(_problem_size: usize, batch_size: usize) -> bool {
666        // Heuristic: GPU is beneficial for large problems or large batches
667        _problem_size * batch_size > 10000
668    }
669
670    /// Estimate optimal batch size for GPU evaluation
671    pub fn estimate_optimal_batch_size(
672        problem_dims: usize,
673        available_memory: usize,
674        precision: GpuPrecision,
675    ) -> usize {
676        let element_size = match precision {
677            GpuPrecision::F32 => 4,
678            GpuPrecision::F64 => 8,
679            GpuPrecision::Mixed => 6, // Average of F32 and F64
680        };
681
682        let memory_per_point = problem_dims * element_size * 3; // Input, output, temp
683        let batch_size = available_memory / memory_per_point;
684
685        // Ensure batch size is reasonable
686        batch_size.max(1).min(65536)
687    }
688
689    /// Create optimal GPU configuration for a given problem
690    pub fn create_optimal_config(
691        problem_dims: usize,
692        expected_evaluations: usize,
693    ) -> ScirsResult<GpuOptimizationConfig> {
694        let context = GpuContext::new(scirs2_core::gpu::GpuBackend::Cuda)?;
695        let available_memory = 1024 * 1024 * 1024; // Default 1GB, should be queried from device
696
697        let batch_size = estimate_optimal_batch_size(
698            problem_dims,
699            available_memory / 2, // Use half of available memory
700            GpuPrecision::F64,
701        );
702
703        Ok(GpuOptimizationConfig {
704            context: Arc::new(context),
705            batch_size,
706            memory_limit: Some(available_memory / 2),
707            use_tensor_cores: true,
708            precision: GpuPrecision::F64,
709            stream_count: 4,
710        })
711    }
712}
713
714#[cfg(test)]
715mod tests {
716    use super::*;
717
718    #[test]
719    fn test_gpu_config_creation() {
720        let config = GpuOptimizationConfig::default();
721        assert_eq!(config.batch_size, 1024);
722        assert_eq!(config.precision, GpuPrecision::F64);
723        assert!(config.use_tensor_cores);
724    }
725
726    #[test]
727    fn test_optimal_batch_size_estimation() {
728        let batch_size = utils::estimate_optimal_batch_size(
729            10,          // 10-dimensional problem
730            1024 * 1024, // 1MB memory
731            GpuPrecision::F64,
732        );
733        assert!(batch_size > 0);
734        assert!(batch_size <= 65536);
735    }
736
737    #[test]
738    fn test_gpu_usage_heuristic() {
739        assert!(!utils::should_use_gpu(10, 10)); // Small problem
740        assert!(utils::should_use_gpu(1000, 100)); // Large problem
741        assert!(utils::should_use_gpu(100, 1000)); // Large batch
742    }
743}
scirs2_optimize/gpu/mod.rs

scirs2_optimize/gpu/
mod.rs
