scirs2_optimize/gpu/
cuda_kernels.rs

1//! CUDA kernels for GPU-accelerated optimization algorithms
2//!
3//! This module provides low-level CUDA kernel implementations for common
4//! optimization operations, leveraging scirs2-core's GPU abstractions.
5
6use super::GpuContext;
7use crate::error::OptimizeError;
8use scirs2_core::gpu::async_execution::GpuStream;
9use scirs2_core::gpu::{GpuBuffer, GpuKernelHandle};
10use std::sync::Arc;
11
12type ScirsResult<T> = Result<T, OptimizeError>;
13
14/// CUDA kernel for parallel function evaluation
15pub struct FunctionEvaluationKernel {
16    context: Arc<GpuContext>,
17    kernel: GpuKernelHandle,
18}
19
20impl FunctionEvaluationKernel {
21    /// Create a new function evaluation kernel
22    pub fn new(context: Arc<GpuContext>) -> ScirsResult<Self> {
23        let kernel_source = r#"
24            __global__ void evaluate_batch(
25                const double* points,
26                double* results,
27                int n_points,
28                int n_dims,
29                int function_type
30            ) {
31                int idx = blockIdx.x * blockDim.x + threadIdx.x;
32                if (idx >= n_points) return;
33                
34                // Placeholder implementation - would contain actual function evaluation
35                double sum = 0.0;
36                for (int i = 0; i < n_dims; i++) {
37                    double x = points[idx * n_dims + i];
38                    sum += x * x;
39                }
40                results[idx] = sum;
41            }
42        "#;
43
44        let kernel = context.execute(|compiler| compiler.compile(kernel_source))?;
45
46        Ok(Self { context, kernel })
47    }
48
49    /// Evaluate a batch of points using the specified function type
50    /// Note: This is a placeholder implementation that compiles correctly
51    pub fn evaluate_batch(
52        &self,
53        points: &GpuBuffer<f64>,
54        _function_type: i32,
55        _stream: Option<&GpuStream>,
56    ) -> ScirsResult<GpuBuffer<f64>> {
57        // Placeholder implementation
58        let n_points = points.len();
59        let results = self.context.create_buffer::<f64>(n_points);
60
61        // In a real implementation, we would launch the kernel here
62        // For now, just return a buffer of zeros
63        Ok(results)
64    }
65}
66
67/// CUDA kernel for gradient computation using finite differences
68pub struct GradientKernel {
69    context: Arc<GpuContext>,
70    kernel: GpuKernelHandle,
71}
72
73impl GradientKernel {
74    /// Create a new gradient computation kernel
75    pub fn new(context: Arc<GpuContext>) -> ScirsResult<Self> {
76        let kernel_source = r#"
77            __global__ void compute_gradient_finite_diff(
78                const double* points,
79                const double* function_values,
80                double* gradients,
81                int n_points,
82                int n_dims,
83                int function_type,
84                double h
85            ) {
86                int idx = blockIdx.x * blockDim.x + threadIdx.x;
87                if (idx >= n_points) return;
88                
89                // Placeholder implementation
90                for (int i = 0; i < n_dims; i++) {
91                    gradients[idx * n_dims + i] = 0.0;
92                }
93            }
94        "#;
95
96        let kernel = context.execute(|compiler| compiler.compile(kernel_source))?;
97
98        Ok(Self { context, kernel })
99    }
100
101    /// Compute gradients for a batch of points using finite differences
102    /// Note: This is a placeholder implementation that compiles correctly
103    pub fn compute_gradients(
104        &self,
105        points: &GpuBuffer<f64>,
106        _function_values: &GpuBuffer<f64>,
107        _function_type: i32,
108        _h: f64,
109        _stream: Option<&GpuStream>,
110    ) -> ScirsResult<GpuBuffer<f64>> {
111        // Placeholder implementation
112        let n_points = points.len();
113        let gradients = self.context.create_buffer::<f64>(n_points);
114
115        // In a real implementation, we would launch the kernel here
116        // For now, just return a buffer of zeros
117        Ok(gradients)
118    }
119}
120
121/// CUDA kernel for particle swarm optimization updates
122pub struct ParticleSwarmKernel {
123    context: Arc<GpuContext>,
124    kernel: GpuKernelHandle,
125}
126
127impl ParticleSwarmKernel {
128    /// Create a new particle swarm kernel
129    pub fn new(context: Arc<GpuContext>) -> ScirsResult<Self> {
130        let kernel_source = r#"
131            __global__ void update_particles(
132                double* positions,
133                double* velocities,
134                const double* personal_best,
135                const double* global_best,
136                int n_particles,
137                int n_dims,
138                double w,
139                double c1,
140                double c2
141            ) {
142                int idx = blockIdx.x * blockDim.x + threadIdx.x;
143                if (idx >= n_particles) return;
144                
145                // Placeholder implementation
146                for (int i = 0; i < n_dims; i++) {
147                    velocities[idx * n_dims + i] *= 0.9;
148                    positions[idx * n_dims + i] += velocities[idx * n_dims + i];
149                }
150            }
151        "#;
152
153        let kernel = context.execute(|compiler| compiler.compile(kernel_source))?;
154
155        Ok(Self { context, kernel })
156    }
157
158    /// Update particle positions and velocities
159    /// Note: This is a placeholder implementation that compiles correctly
160    pub fn update_particles(
161        &self,
162        positions: &mut GpuBuffer<f64>,
163        velocities: &mut GpuBuffer<f64>,
164        _personal_best: &GpuBuffer<f64>,
165        _global_best: &GpuBuffer<f64>,
166        _w: f64,
167        _c1: f64,
168        _c2: f64,
169        _stream: Option<&GpuStream>,
170    ) -> ScirsResult<()> {
171        // Placeholder implementation
172        let _ = (positions, velocities);
173        Ok(())
174    }
175}
176
177/// CUDA kernel for differential evolution mutations
178pub struct DifferentialEvolutionKernel {
179    context: Arc<GpuContext>,
180    kernel: GpuKernelHandle,
181}
182
183impl DifferentialEvolutionKernel {
184    /// Create a new differential evolution kernel
185    pub fn new(context: Arc<GpuContext>) -> ScirsResult<Self> {
186        let kernel_source = r#"
187            __global__ void mutate_population(
188                const double* population,
189                double* mutant_vectors,
190                int* indices,
191                int n_population,
192                int n_dims,
193                double F
194            ) {
195                int idx = blockIdx.x * blockDim.x + threadIdx.x;
196                if (idx >= n_population) return;
197                
198                // Placeholder implementation
199                for (int i = 0; i < n_dims; i++) {
200                    mutant_vectors[idx * n_dims + i] = population[idx * n_dims + i];
201                }
202            }
203        "#;
204
205        let kernel = context.execute(|compiler| compiler.compile(kernel_source))?;
206
207        Ok(Self { context, kernel })
208    }
209
210    /// Generate mutant vectors for differential evolution
211    /// Note: This is a placeholder implementation that compiles correctly
212    pub fn generate_mutants(
213        &self,
214        population: &GpuBuffer<f64>,
215        _indices: &GpuBuffer<i32>,
216        _f: f64,
217        _stream: Option<&GpuStream>,
218    ) -> ScirsResult<GpuBuffer<f64>> {
219        // Placeholder implementation
220        let n_points = population.len();
221        let mutants = self.context.create_buffer::<f64>(n_points);
222
223        // In a real implementation, we would launch the kernel here
224        // For now, just return a buffer of zeros
225        Ok(mutants)
226    }
227}
228
229#[allow(dead_code)]
230pub fn placeholder() {
231    // Placeholder function to prevent unused module warnings
232}
scirs2_optimize/gpu/cuda_kernels.rs

scirs2_optimize/gpu/
cuda_kernels.rs
