scirs2-fft 0.3.3

//! GPU-accelerated Sparse Fast Fourier Transform
//!
//! This module extends the sparse FFT functionality with GPU acceleration
//! using CUDA, HIP (ROCm), or SYCL backends for high-performance computing.

use crate::error::{FFTError, FFTResult};
use crate::sparse_fft::{
    SparseFFTAlgorithm, SparseFFTConfig, SparseFFTResult, SparsityEstimationMethod, WindowFunction,
};
use scirs2_core::numeric::Complex64;
use scirs2_core::numeric::NumCast;
use std::fmt::Debug;
use std::time::Instant;

/// GPU acceleration backend
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum GPUBackend {
    /// CUDA backend (NVIDIA GPUs)
    CUDA,
    /// HIP backend (AMD GPUs)
    HIP,
    /// SYCL backend (cross-platform)
    SYCL,
    /// CPU fallback when no GPU is available
    CPUFallback,
}

/// GPU-accelerated sparse FFT configuration
#[derive(Debug, Clone)]
pub struct GPUSparseFFTConfig {
    /// Base sparse FFT configuration
    pub base_config: SparseFFTConfig,
    /// GPU backend to use
    pub backend: GPUBackend,
    /// Device ID to use (-1 for auto-select)
    pub device_id: i32,
    /// Batch size for processing multiple signals
    pub batch_size: usize,
    /// Maximum memory usage in bytes (0 for unlimited)
    pub max_memory: usize,
    /// Enable mixed precision computation
    pub use_mixed_precision: bool,
    /// Use in-place computation when possible
    pub use_inplace: bool,
    /// Stream count for parallel execution on GPU
    pub stream_count: usize,
}

impl Default for GPUSparseFFTConfig {
    fn default() -> Self {
        Self {
            base_config: SparseFFTConfig::default(),
            backend: GPUBackend::CPUFallback,
            device_id: -1,
            batch_size: 1,
            max_memory: 0,
            use_mixed_precision: false,
            use_inplace: true,
            stream_count: 1,
        }
    }
}

/// GPU-accelerated sparse FFT processor
pub struct GPUSparseFFT {
    /// Configuration
    _config: GPUSparseFFTConfig,
    /// GPU resources initialized
    gpu_initialized: bool,
    /// GPU device information
    device_info: Option<String>,
}

impl GPUSparseFFT {
    /// Create a new GPU-accelerated sparse FFT processor with the given configuration
    pub fn new(config: GPUSparseFFTConfig) -> Self {
        Self {
            _config: config,
            gpu_initialized: false,
            device_info: None,
        }
    }

    /// Create a new GPU-accelerated sparse FFT processor with default configuration
    pub fn with_default_config() -> Self {
        Self::new(GPUSparseFFTConfig::default())
    }

    /// Initialize GPU resources
    fn initialize_gpu(&mut self) -> FFTResult<()> {
        // Placeholder for actual GPU initialization code
        // In a real implementation, this would set up CUDA/HIP/SYCL context and resources

        match self._config.backend {
            GPUBackend::CUDA => {
                // Initialize CUDA resources
                self.device_info = Some("CUDA GPU device (simulated)".to_string());
            }
            GPUBackend::HIP => {
                // Initialize HIP resources
                self.device_info = Some("ROCm GPU device (simulated)".to_string());
            }
            GPUBackend::SYCL => {
                // Initialize SYCL resources
                self.device_info = Some("SYCL device (simulated)".to_string());
            }
            GPUBackend::CPUFallback => {
                self.device_info = Some("CPU fallback device".to_string());
            }
        }

        self.gpu_initialized = true;
        Ok(())
    }

    /// Get GPU device information
    pub fn get_device_info(&mut self) -> FFTResult<String> {
        if !self.gpu_initialized {
            self.initialize_gpu()?;
        }

        Ok(self
            .device_info
            .clone()
            .unwrap_or_else(|| "Unknown device".to_string()))
    }

    /// Perform GPU-accelerated sparse FFT on a signal
    pub fn sparse_fft<T>(&mut self, signal: &[T]) -> FFTResult<SparseFFTResult>
    where
        T: NumCast + Copy + Debug + 'static,
    {
        if !self.gpu_initialized {
            self.initialize_gpu()?;
        }

        // For demonstration purposes, use the CPU implementation
        // In a real implementation, this would use the GPU
        let start = Instant::now();

        // Convert input to complex for processing
        let signal_complex: Vec<Complex64> = signal
            .iter()
            .map(|&val| {
                let val_f64 = NumCast::from(val).ok_or_else(|| {
                    FFTError::ValueError(format!("Could not convert {val:?} to f64"))
                })?;
                Ok(Complex64::new(val_f64, 0.0))
            })
            .collect::<FFTResult<Vec<_>>>()?;

        // Create a CPU sparse FFT processor to handle computation for now
        // This is a temporary solution until actual GPU implementation is provided
        let mut cpu_processor = crate::sparse_fft::SparseFFT::new(self._config.base_config.clone());
        let result = cpu_processor.sparse_fft(&signal_complex)?;

        // Record computation time including any data transfers
        let computation_time = start.elapsed();

        // Return result with updated computation time
        Ok(SparseFFTResult {
            values: result.values,
            indices: result.indices,
            estimated_sparsity: result.estimated_sparsity,
            computation_time,
            algorithm: self._config.base_config.algorithm,
        })
    }

    /// Perform batch processing of multiple signals
    pub fn batch_sparse_fft<T>(&mut self, signals: &[Vec<T>]) -> FFTResult<Vec<SparseFFTResult>>
    where
        T: NumCast + Copy + Debug + 'static,
    {
        if !self.gpu_initialized {
            self.initialize_gpu()?;
        }

        // Process each signal and collect results
        signals
            .iter()
            .map(|signal| self.sparse_fft(signal))
            .collect()
    }
}

/// Performs GPU-accelerated sparse FFT on a signal
///
/// This is a convenience function that creates a GPU sparse FFT processor
/// with the specified backend and performs the computation.
///
/// # Arguments
///
/// * `signal` - Input signal
/// * `k` - Expected sparsity (number of significant frequency components)
/// * `backend` - GPU backend to use
/// * `algorithm` - Sparse FFT algorithm variant
/// * `window_function` - Window function to apply before FFT
///
/// # Returns
///
/// * Sparse FFT result containing frequency components, indices, and timing information
#[allow(dead_code)]
pub fn gpu_sparse_fft<T>(
    signal: &[T],
    k: usize,
    backend: GPUBackend,
    algorithm: Option<SparseFFTAlgorithm>,
    window_function: Option<WindowFunction>,
) -> FFTResult<SparseFFTResult>
where
    T: NumCast + Copy + Debug + 'static,
{
    // Create a base configuration
    let base_config = SparseFFTConfig {
        estimation_method: SparsityEstimationMethod::Manual,
        sparsity: k,
        algorithm: algorithm.unwrap_or(SparseFFTAlgorithm::Sublinear),
        window_function: window_function.unwrap_or(WindowFunction::None),
        ..SparseFFTConfig::default()
    };

    // Create a GPU configuration
    let gpu_config = GPUSparseFFTConfig {
        base_config,
        backend,
        ..GPUSparseFFTConfig::default()
    };

    // Create processor and perform computation
    let mut processor = GPUSparseFFT::new(gpu_config);
    processor.sparse_fft(signal)
}

/// Perform GPU-accelerated batch processing of multiple signals
///
/// # Arguments
///
/// * `signals` - List of input signals
/// * `k` - Expected sparsity
/// * `backend` - GPU backend to use
/// * `algorithm` - Sparse FFT algorithm variant
/// * `window_function` - Window function to apply before FFT
///
/// # Returns
///
/// * List of sparse FFT results for each input signal
#[allow(dead_code)]
pub fn gpu_batch_sparse_fft<T>(
    signals: &[Vec<T>],
    k: usize,
    backend: GPUBackend,
    algorithm: Option<SparseFFTAlgorithm>,
    window_function: Option<WindowFunction>,
) -> FFTResult<Vec<SparseFFTResult>>
where
    T: NumCast + Copy + Debug + 'static,
{
    // Create a base configuration
    let base_config = SparseFFTConfig {
        estimation_method: SparsityEstimationMethod::Manual,
        sparsity: k,
        algorithm: algorithm.unwrap_or(SparseFFTAlgorithm::Sublinear),
        window_function: window_function.unwrap_or(WindowFunction::None),
        ..SparseFFTConfig::default()
    };

    // Create a GPU configuration
    let gpu_config = GPUSparseFFTConfig {
        base_config,
        backend,
        batch_size: signals.len(),
        ..GPUSparseFFTConfig::default()
    };

    // Create processor and perform batch computation
    let mut processor = GPUSparseFFT::new(gpu_config);
    processor.batch_sparse_fft(signals)
}

#[cfg(test)]
mod tests {
    use super::*;
    use std::f64::consts::PI;

    // Helper function to create a sparse signal
    fn create_sparse_signal(n: usize, frequencies: &[(usize, f64)]) -> Vec<f64> {
        let mut signal = vec![0.0; n];

        for i in 0..n {
            let t = 2.0 * PI * (i as f64) / (n as f64);
            for &(freq, amp) in frequencies {
                signal[i] += amp * (freq as f64 * t).sin();
            }
        }

        signal
    }

    #[test]
    fn test_gpu_sparse_fft_cpu_fallback() {
        // Create a signal with 3 frequency components
        let n = 256;
        let frequencies = vec![(3, 1.0), (7, 0.5), (15, 0.25)];
        let signal = create_sparse_signal(n, &frequencies);

        // Test the GPU-accelerated function with CPU fallback
        let result = gpu_sparse_fft(
            &signal,
            6,
            GPUBackend::CPUFallback,
            Some(SparseFFTAlgorithm::Sublinear),
            Some(WindowFunction::Hann),
        )
        .expect("Operation failed");

        // Should find the frequency components
        assert!(!result.values.is_empty());
        assert_eq!(result.algorithm, SparseFFTAlgorithm::Sublinear);
    }

    #[test]
    fn test_gpu_batch_processing() {
        // Create multiple signals
        let n = 128;
        let signals = vec![
            create_sparse_signal(n, &[(3, 1.0), (7, 0.5)]),
            create_sparse_signal(n, &[(5, 1.0), (10, 0.7)]),
            create_sparse_signal(n, &[(2, 0.8), (12, 0.6)]),
        ];

        // Test batch processing with CPU fallback
        let results = gpu_batch_sparse_fft(
            &signals,
            4,
            GPUBackend::CPUFallback,
            Some(SparseFFTAlgorithm::Sublinear),
            None,
        )
        .expect("Operation failed");

        // Should return the same number of results as input signals
        assert_eq!(results.len(), signals.len());

        // Each result should have frequency components
        for result in results {
            assert!(!result.values.is_empty());
        }
    }
}