CUDA vs CuPy: What are the differences?

Introduction

In this post, we will explore the key differences between CUDA and CuPy, two popular frameworks for accelerating scientific computations on GPUs.

1. Ease of Use: CUDA is a low-level parallel computing framework that requires programming in C or C++. On the other hand, CuPy is a high-level library that provides a NumPy-like interface for writing GPU-accelerated code using Python. This makes CuPy more accessible and easier to use for developers with Python experience.

2. Compatibility: CUDA is specific to NVIDIA GPUs and requires NVIDIA hardware and drivers to run. CuPy, on the other hand, is built on top of CUDA and is designed to be compatible with both NVIDIA GPUs and AMD GPUs through the ROCm platform. This allows users to take advantage of GPU acceleration regardless of the hardware they have.

3. Portability: CUDA code is tightly coupled with the NVIDIA hardware and requires specific compiler and library versions. In contrast, CuPy is built on top of CUDA and provides a higher level of abstraction, making it easier to port code between different GPU architectures and versions of CUDA. This means that CuPy code can potentially run on different CUDA-compatible systems without requiring significant modifications.

4. Supported Libraries: CUDA provides a rich ecosystem of libraries for various domains such as linear algebra, image processing, and machine learning. CuPy, being the high-level interface, also supports many of these CUDA libraries, allowing users to seamlessly integrate them into their code. However, CuPy also provides additional functionality through its own library, cuCIM, which is focused on accelerating imaging-related computations.

5. Community and Support: CUDA has been around for a longer time and has a larger user base and community support. This means that there are more resources, tutorials, and forums available for learning and troubleshooting CUDA-related issues. CuPy, although growing rapidly, is still relatively newer and may have a smaller community and fewer resources available.

6. Vendor Lock-in: CUDA is developed and maintained by NVIDIA, which means that it is tied to their hardware and software ecosystem. While CuPy is built on top of CUDA, it provides a higher level of abstraction that allows users to potentially switch between different hardware vendors and platforms without significant code changes. This reduces the vendor lock-in associated with using CUDA directly.

In summary, CuPy provides a high-level Python interface for programming GPU-accelerated computations using CUDA. It offers ease of use, compatibility with multiple GPU architectures, portability, and support for a wide range of CUDA libraries. However, CUDA has a larger community and may be more suitable for users who require specific NVIDIA hardware optimizations or more advanced low-level programming capabilities.