nvidia-cusparse-cu12

import os try: import cupy as cp import cupy.sparse as cps import numpy as np # Check for CUDA device if cp.cuda.is_available(): print(f"CUDA is available. CuPy version: {cp.__version__}") print(f"CUDA Device Name: {cp.cuda.Device().name}") # Create a sparse matrix on CPU (SciPy format) row = np.array([0, 1, 2, 0]) col = np.array([0, 1, 2, 1]) data = np.array([1, 2, 3, 4]) shape = (3, 3) sparse_cpu = cps.csr_matrix((data, (row, col)), shape=shape) print("\nCPU Sparse Matrix:\n", sparse_cpu.toarray()) # Transfer to GPU and perform a sparse matrix-vector multiplication sparse_gpu = cps.csr_matrix(sparse_cpu, dtype=cp.float32) # cuSPARSE generally works with float32/64 vector_gpu = cp.array([1.0, 2.0, 3.0], dtype=cp.float32) result_gpu = sparse_gpu @ vector_gpu print("\nGPU Sparse Matrix-Vector Product (using cuSPARSE via CuPy):\n", result_gpu) else: print("CUDA is not available. Please ensure a compatible NVIDIA GPU and driver are installed.") print("You may need to install cupy-cuda12x manually if using a specific CUDA version.") except ImportError: print("CuPy is not installed. To run this example, install CuPy compatible with CUDA 12:") print("pip install cupy-cuda12x") except Exception as e: print(f"An error occurred: {e}")