scirs2_fft/
sparse_fft_gpu.rs

1//! GPU-accelerated Sparse Fast Fourier Transform
2//!
3//! This module extends the sparse FFT functionality with GPU acceleration
4//! using CUDA, HIP (ROCm), or SYCL backends for high-performance computing.
5
6use crate::error::{FFTError, FFTResult};
7use crate::sparse_fft::{
8    SparseFFTAlgorithm, SparseFFTConfig, SparseFFTResult, SparsityEstimationMethod, WindowFunction,
9};
10use scirs2_core::numeric::Complex64;
11use scirs2_core::numeric::NumCast;
12use std::fmt::Debug;
13use std::time::Instant;
14
15/// GPU acceleration backend
16#[derive(Debug, Clone, Copy, PartialEq, Eq)]
17pub enum GPUBackend {
18    /// CUDA backend (NVIDIA GPUs)
19    CUDA,
20    /// HIP backend (AMD GPUs)
21    HIP,
22    /// SYCL backend (cross-platform)
23    SYCL,
24    /// CPU fallback when no GPU is available
25    CPUFallback,
26}
27
28/// GPU-accelerated sparse FFT configuration
29#[derive(Debug, Clone)]
30pub struct GPUSparseFFTConfig {
31    /// Base sparse FFT configuration
32    pub base_config: SparseFFTConfig,
33    /// GPU backend to use
34    pub backend: GPUBackend,
35    /// Device ID to use (-1 for auto-select)
36    pub device_id: i32,
37    /// Batch size for processing multiple signals
38    pub batch_size: usize,
39    /// Maximum memory usage in bytes (0 for unlimited)
40    pub max_memory: usize,
41    /// Enable mixed precision computation
42    pub use_mixed_precision: bool,
43    /// Use in-place computation when possible
44    pub use_inplace: bool,
45    /// Stream count for parallel execution on GPU
46    pub stream_count: usize,
47}
48
49impl Default for GPUSparseFFTConfig {
50    fn default() -> Self {
51        Self {
52            base_config: SparseFFTConfig::default(),
53            backend: GPUBackend::CPUFallback,
54            device_id: -1,
55            batch_size: 1,
56            max_memory: 0,
57            use_mixed_precision: false,
58            use_inplace: true,
59            stream_count: 1,
60        }
61    }
62}
63
64/// GPU-accelerated sparse FFT processor
65pub struct GPUSparseFFT {
66    /// Configuration
67    _config: GPUSparseFFTConfig,
68    /// GPU resources initialized
69    gpu_initialized: bool,
70    /// GPU device information
71    device_info: Option<String>,
72}
73
74impl GPUSparseFFT {
75    /// Create a new GPU-accelerated sparse FFT processor with the given configuration
76    pub fn new(config: GPUSparseFFTConfig) -> Self {
77        Self {
78            _config: config,
79            gpu_initialized: false,
80            device_info: None,
81        }
82    }
83
84    /// Create a new GPU-accelerated sparse FFT processor with default configuration
85    pub fn with_default_config() -> Self {
86        Self::new(GPUSparseFFTConfig::default())
87    }
88
89    /// Initialize GPU resources
90    fn initialize_gpu(&mut self) -> FFTResult<()> {
91        // Placeholder for actual GPU initialization code
92        // In a real implementation, this would set up CUDA/HIP/SYCL context and resources
93
94        match self._config.backend {
95            GPUBackend::CUDA => {
96                // Initialize CUDA resources
97                self.device_info = Some("CUDA GPU device (simulated)".to_string());
98            }
99            GPUBackend::HIP => {
100                // Initialize HIP resources
101                self.device_info = Some("ROCm GPU device (simulated)".to_string());
102            }
103            GPUBackend::SYCL => {
104                // Initialize SYCL resources
105                self.device_info = Some("SYCL device (simulated)".to_string());
106            }
107            GPUBackend::CPUFallback => {
108                self.device_info = Some("CPU fallback device".to_string());
109            }
110        }
111
112        self.gpu_initialized = true;
113        Ok(())
114    }
115
116    /// Get GPU device information
117    pub fn get_device_info(&mut self) -> FFTResult<String> {
118        if !self.gpu_initialized {
119            self.initialize_gpu()?;
120        }
121
122        Ok(self
123            .device_info
124            .clone()
125            .unwrap_or_else(|| "Unknown device".to_string()))
126    }
127
128    /// Perform GPU-accelerated sparse FFT on a signal
129    pub fn sparse_fft<T>(&mut self, signal: &[T]) -> FFTResult<SparseFFTResult>
130    where
131        T: NumCast + Copy + Debug + 'static,
132    {
133        if !self.gpu_initialized {
134            self.initialize_gpu()?;
135        }
136
137        // For demonstration purposes, use the CPU implementation
138        // In a real implementation, this would use the GPU
139        let start = Instant::now();
140
141        // Convert input to complex for processing
142        let signal_complex: Vec<Complex64> = signal
143            .iter()
144            .map(|&val| {
145                let val_f64 = NumCast::from(val).ok_or_else(|| {
146                    FFTError::ValueError(format!("Could not convert {val:?} to f64"))
147                })?;
148                Ok(Complex64::new(val_f64, 0.0))
149            })
150            .collect::<FFTResult<Vec<_>>>()?;
151
152        // Create a CPU sparse FFT processor to handle computation for now
153        // This is a temporary solution until actual GPU implementation is provided
154        let mut cpu_processor = crate::sparse_fft::SparseFFT::new(self._config.base_config.clone());
155        let result = cpu_processor.sparse_fft(&signal_complex)?;
156
157        // Record computation time including any data transfers
158        let computation_time = start.elapsed();
159
160        // Return result with updated computation time
161        Ok(SparseFFTResult {
162            values: result.values,
163            indices: result.indices,
164            estimated_sparsity: result.estimated_sparsity,
165            computation_time,
166            algorithm: self._config.base_config.algorithm,
167        })
168    }
169
170    /// Perform batch processing of multiple signals
171    pub fn batch_sparse_fft<T>(&mut self, signals: &[Vec<T>]) -> FFTResult<Vec<SparseFFTResult>>
172    where
173        T: NumCast + Copy + Debug + 'static,
174    {
175        if !self.gpu_initialized {
176            self.initialize_gpu()?;
177        }
178
179        // Process each signal and collect results
180        signals
181            .iter()
182            .map(|signal| self.sparse_fft(signal))
183            .collect()
184    }
185}
186
187/// Performs GPU-accelerated sparse FFT on a signal
188///
189/// This is a convenience function that creates a GPU sparse FFT processor
190/// with the specified backend and performs the computation.
191///
192/// # Arguments
193///
194/// * `signal` - Input signal
195/// * `k` - Expected sparsity (number of significant frequency components)
196/// * `backend` - GPU backend to use
197/// * `algorithm` - Sparse FFT algorithm variant
198/// * `window_function` - Window function to apply before FFT
199///
200/// # Returns
201///
202/// * Sparse FFT result containing frequency components, indices, and timing information
203#[allow(dead_code)]
204pub fn gpu_sparse_fft<T>(
205    signal: &[T],
206    k: usize,
207    backend: GPUBackend,
208    algorithm: Option<SparseFFTAlgorithm>,
209    window_function: Option<WindowFunction>,
210) -> FFTResult<SparseFFTResult>
211where
212    T: NumCast + Copy + Debug + 'static,
213{
214    // Create a base configuration
215    let base_config = SparseFFTConfig {
216        estimation_method: SparsityEstimationMethod::Manual,
217        sparsity: k,
218        algorithm: algorithm.unwrap_or(SparseFFTAlgorithm::Sublinear),
219        window_function: window_function.unwrap_or(WindowFunction::None),
220        ..SparseFFTConfig::default()
221    };
222
223    // Create a GPU configuration
224    let gpu_config = GPUSparseFFTConfig {
225        base_config,
226        backend,
227        ..GPUSparseFFTConfig::default()
228    };
229
230    // Create processor and perform computation
231    let mut processor = GPUSparseFFT::new(gpu_config);
232    processor.sparse_fft(signal)
233}
234
235/// Perform GPU-accelerated batch processing of multiple signals
236///
237/// # Arguments
238///
239/// * `signals` - List of input signals
240/// * `k` - Expected sparsity
241/// * `backend` - GPU backend to use
242/// * `algorithm` - Sparse FFT algorithm variant
243/// * `window_function` - Window function to apply before FFT
244///
245/// # Returns
246///
247/// * List of sparse FFT results for each input signal
248#[allow(dead_code)]
249pub fn gpu_batch_sparse_fft<T>(
250    signals: &[Vec<T>],
251    k: usize,
252    backend: GPUBackend,
253    algorithm: Option<SparseFFTAlgorithm>,
254    window_function: Option<WindowFunction>,
255) -> FFTResult<Vec<SparseFFTResult>>
256where
257    T: NumCast + Copy + Debug + 'static,
258{
259    // Create a base configuration
260    let base_config = SparseFFTConfig {
261        estimation_method: SparsityEstimationMethod::Manual,
262        sparsity: k,
263        algorithm: algorithm.unwrap_or(SparseFFTAlgorithm::Sublinear),
264        window_function: window_function.unwrap_or(WindowFunction::None),
265        ..SparseFFTConfig::default()
266    };
267
268    // Create a GPU configuration
269    let gpu_config = GPUSparseFFTConfig {
270        base_config,
271        backend,
272        batch_size: signals.len(),
273        ..GPUSparseFFTConfig::default()
274    };
275
276    // Create processor and perform batch computation
277    let mut processor = GPUSparseFFT::new(gpu_config);
278    processor.batch_sparse_fft(signals)
279}
280
281#[cfg(test)]
282mod tests {
283    use super::*;
284    use std::f64::consts::PI;
285
286    // Helper function to create a sparse signal
287    fn create_sparse_signal(n: usize, frequencies: &[(usize, f64)]) -> Vec<f64> {
288        let mut signal = vec![0.0; n];
289
290        for i in 0..n {
291            let t = 2.0 * PI * (i as f64) / (n as f64);
292            for &(freq, amp) in frequencies {
293                signal[i] += amp * (freq as f64 * t).sin();
294            }
295        }
296
297        signal
298    }
299
300    #[test]
301    fn test_gpu_sparse_fft_cpu_fallback() {
302        // Create a signal with 3 frequency components
303        let n = 256;
304        let frequencies = vec![(3, 1.0), (7, 0.5), (15, 0.25)];
305        let signal = create_sparse_signal(n, &frequencies);
306
307        // Test the GPU-accelerated function with CPU fallback
308        let result = gpu_sparse_fft(
309            &signal,
310            6,
311            GPUBackend::CPUFallback,
312            Some(SparseFFTAlgorithm::Sublinear),
313            Some(WindowFunction::Hann),
314        )
315        .unwrap();
316
317        // Should find the frequency components
318        assert!(!result.values.is_empty());
319        assert_eq!(result.algorithm, SparseFFTAlgorithm::Sublinear);
320    }
321
322    #[test]
323    fn test_gpu_batch_processing() {
324        // Create multiple signals
325        let n = 128;
326        let signals = vec![
327            create_sparse_signal(n, &[(3, 1.0), (7, 0.5)]),
328            create_sparse_signal(n, &[(5, 1.0), (10, 0.7)]),
329            create_sparse_signal(n, &[(2, 0.8), (12, 0.6)]),
330        ];
331
332        // Test batch processing with CPU fallback
333        let results = gpu_batch_sparse_fft(
334            &signals,
335            4,
336            GPUBackend::CPUFallback,
337            Some(SparseFFTAlgorithm::Sublinear),
338            None,
339        )
340        .unwrap();
341
342        // Should return the same number of results as input signals
343        assert_eq!(results.len(), signals.len());
344
345        // Each result should have frequency components
346        for result in results {
347            assert!(!result.values.is_empty());
348        }
349    }
350}
scirs2_fft/sparse_fft_gpu.rs

scirs2_fft/
sparse_fft_gpu.rs
