# GPU-Accelerated Sparse FFT Implementation Notes

This document provides information about the GPU-accelerated sparse FFT implementation in the `scirs2-fft` crate.

## Current Status

The GPU acceleration framework has been implemented with the following components:

- `sparse_fft_gpu.rs` module providing the core GPU-accelerated sparse FFT functionality
- Support for multiple GPU backends (CUDA, HIP/ROCm, SYCL)
- CPU fallback when no GPU device is available
- Configuration options for memory management and performance tuning
- Batch processing capabilities for handling multiple signals efficiently
- Example showing how to use the GPU-accelerated sparse FFT

## Implementation Details

### Architecture

The GPU implementation follows a layered approach:

1. **High-level API**: Simple functions (`gpu_sparse_fft`, `gpu_batch_sparse_fft`) for easy usage
2. **Core processor**: `GPUSparseFFT` struct with configuration options
3. **Backend abstraction**: `GPUBackend` enum to support different GPU platforms
4. **Resource management**: Memory allocation and device management

### Missing Functionality

The current implementation is a framework/placeholder for the actual GPU code. To make it fully functional:

1. **Add actual GPU kernel implementations**:
   - CUDA implementation using CUDA FFT (cuFFT)
   - HIP implementation using rocFFT
   - SYCL implementation using oneAPI DFT

2. **Implement memory management**:
   - Smart buffer allocation and reuse
   - Pinned memory for efficient host-device transfers
   - Stream-based execution for overlapping computation and transfers

3. **Add performance optimization**:
   - Auto-tuning for optimal kernel selection
   - Mixed-precision computation 
   - Multi-GPU support
   - Concurrent execution with multiple streams

## Usage Example

```rust
use scirs2_fft::{
    sparse_fft_gpu::{gpu_sparse_fft, GPUBackend},
    sparse_fft::{SparseFFTAlgorithm, WindowFunction}
};

// Create input signal
let signal = vec![/* ... */];

// Perform GPU-accelerated sparse FFT
let result = gpu_sparse_fft(
    &signal, 
    6,  // Expected sparsity
    GPUBackend::CUDA,  // Use CUDA backend
    Some(SparseFFTAlgorithm::Sublinear),
    Some(WindowFunction::Hann)
).unwrap();

// Process multiple signals in batch
let signals = vec![
    vec![/* signal 1 */],
    vec![/* signal 2 */],
    vec![/* signal 3 */],
];

let batch_results = gpu_batch_sparse_fft(
    &signals,
    6,
    GPUBackend::CUDA,
    Some(SparseFFTAlgorithm::Sublinear),
    Some(WindowFunction::Hann)
).unwrap();
```

## Performance Considerations

The intended performance benefits of the GPU implementation include:

1. **Faster processing for large signals**: GPU acceleration can provide significant speedups for signals with thousands of samples.

2. **Efficient batch processing**: Processing multiple signals simultaneously is where GPUs excel, as they can effectively utilize thousands of cores.

3. **Hybrid processing**: For some signal sizes, a hybrid approach that uses both CPU and GPU might be optimal.

## Next Steps

1. Implement the actual GPU kernels using CUDA/HIP/SYCL
2. Create benchmarks to compare CPU and GPU performance
3. Add memory optimization strategies for large signals
4. Implement advanced features like multi-GPU support