Explain how CUDA interoperates with other programming languages and libraries. What are the benefits and challenges of integrating CUDA with existing codebases?

CUDA interoperability refers to the ability of CUDA code to interact with code written in other programming languages and to utilize external libraries within a CUDA application. This interoperability is essential for integrating CUDA acceleration into existing software systems and for leveraging specialized libraries for various tasks. Methods for CUDA Interoperability: 1. C/C++: - CUDA is primarily an extension of C/C++, making integration with C/C++ code relatively straightforward. CUDA code can be compiled using the NVIDIA CUDA Compiler (nvcc) and linked with existing C/C++ code. - Example: CUDA kernels can be called from C/C++ functions, and C/C++ functions can be called from CUDA kernels (with certain limitations). ```c++ // CUDA kernel __glob....