nvidia-cufft-cu11

import cupy as cp import numpy as np # Ensure a CUDA-enabled GPU is available if not cp.cuda.is_available(): print("CUDA is not available. CuPy and cuFFT require a GPU.") exit() # Create a CuPy array on the GPU n = 1024 x_gpu = cp.random.rand(n, dtype=cp.float32) # Perform a 1D FFT using CuPy (which uses cuFFT internally) y_gpu = cp.fft.fft(x_gpu) # Optionally, copy back to host and compare with NumPy y_cpu = cp.asnumpy(y_gpu) x_cpu = cp.asnumpy(x_gpu) y_numpy = np.fft.fft(x_cpu) print(f"Original GPU array type: {type(x_gpu)}") print(f"FFT result GPU array type: {type(y_gpu)}") print(f"First 5 elements of GPU FFT: {y_gpu[:5]}") print(f"First 5 elements of NumPy FFT: {y_numpy[:5]}") print(f"Max absolute difference between CuPy FFT and NumPy FFT: {cp.max(cp.abs(y_gpu - cp.asarray(y_numpy))):.6e}")