wgsl-fft 0.4.4

GPU-accelerated FFT using Webgpu compute shaders
Documentation 
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use std::time::Instant;
use num_complex::Complex;
use wgsl_fft::GpuFft;

fn main() {
    let fft = GpuFft::new().expect("Failed to create FFT instance");
    
    // Test parameters
    let fft_size = 1024;
    let batch_sizes = vec![1, 4, 16, 64, 256, 1024];
    
    println!("Batch Processing Performance Test");
    println!("FFT Size: {}", fft_size);
    println!("GPU: {:?}", std::env::consts::ARCH);
    println!();
    
    for &batch_size in &batch_sizes {
        // Generate test data
        let inputs: Vec<Vec<Complex<f32>>> = (0..batch_size)
            .map(|i| {
                (0..fft_size)
                    .map(|j| Complex::new((i * fft_size + j) as f32 * 0.1, 0.0))
                    .collect()
            })
            .collect();
        
        // Warm-up run
        let _ = fft.fft(&inputs).expect("FFT failed");
        
        // Timed run
        let start = Instant::now();
        let _results = fft.fft(&inputs).expect("FFT failed");
        let duration = start.elapsed();
        
        let total_samples = batch_size * fft_size;
        let ms_per_sample = duration.as_secs_f64() / total_samples as f64 * 1000.0;
        let samples_per_second = total_samples as f64 / duration.as_secs_f64();
        let mega_samples_per_second = samples_per_second / 1_000_000.0;
        
        println!("Batch Size {}: {:.2} MSa/s ({:.3} ms/sample)", 
                 batch_size, mega_samples_per_second, ms_per_sample);
    }
}