nvidia-cusparse

import cupy as cp import cupy.sparse as csp # Create a sparse matrix on GPU using CuPy, which utilizes cuSPARSE internally. # Example: Coordinate format (COO) row = cp.array([0, 1, 2, 0]) col = cp.array([0, 1, 2, 2]) data = cp.array([1.0, 2.0, 3.0, 4.0]) shape = (3, 3) coo_matrix = csp.coo_matrix((data, (row, col)), shape=shape) print("Sparse COO Matrix on GPU:") print(coo_matrix) print(f"Number of non-zero elements: {coo_matrix.nnz}") # Convert to Compressed Sparse Row (CSR) format csr_matrix = coo_matrix.tocsr() print("\nSparse CSR Matrix on GPU:") print(csr_matrix) # Perform a simple operation, e.g., matrix-vector multiplication vec = cp.array([10., 20., 30.]) result = csr_matrix @ vec print("\nMatrix-vector multiplication result:") print(result) # Note: The `nvidia-cusparse` package provides the underlying CUSPARSE runtime libraries. # Higher-level libraries like CuPy wrap these for Python usage.