A. The libraries for cuda programming in python mainly include:
cupy、pycuda
1. The differences are as follows:
Supported GPU platforms:
PyCUDA: PyCUDA is a library for writing CUDA code in Python. It supports NVIDIA's CUDA platform and provides
similar functionality to the CUDA C/C++ interface. Therefore, PyCUDA is mainly used for applications that interact with NVIDIA GPUs.
CuPy: CuPy is a library for general-purpose GPU programming in Python. It not only supports NVIDIA's CUDA platform, but also supports general-purpose GPU programming interfaces from other manufacturers, such as OpenCL. This makes CuPy available on different GPU platforms.
Array operations and calculations:PyCUDA: PyCUDA is mainly focused on providing support for array operations and calculations on the GPU, similar to NumPy. It enables you to perform operations on the GPU similar to NumPy arrays, but the operations are accelerated on the GPU.
CuPy: CuPy also provides array operations and calculations similar to NumPy, but its goals are more general and supports running on different GPU platforms. Therefore, if you need to run code on different GPU hardware, CuPy may be more suitable.
API and usage:PyCUDA: Since it is closer to the underlying interface of CUDA, using PyCUDA may require more CUDA programming knowledge. It provides
Python bindings to CUDA C/C++, so if you are familiar with CUDA programming, it may be easier to get started.
CuPy: CuPy’s API is similar to NumPy, which means that if you are already familiar with NumPy, switching to CuPy will