CUDA vs OpenCL: Which should I use?


If you are looking to get into GPU programming, you are currently faced with an annoying choice:

I maintain two packages for accelerated computing in Python, PyCuda and PyOpenCL, so obviously I can't decide either. Still, this is a common question, so this page compiles a number of facts to help you decide. Since the question is broad and difficult as it stands, this page will focus on the Python angle when there is any benefit in doing so.

This is a Wiki page on purpose. If you think you have something to add to this discussion, please do not hesitate to click "Edit" above.



As of right now, there is one vendor of CUDA implementations, namely Nvidia Corporation.

The following vendors have OpenCL implementations available:

The following groups are or may be producing CL implementations:

Other Free components

Code Portability




An Attempt at a Conclusion

(Careful: While the above collection is supposed to consist of objective facts, this section is for personal opinion. Feel free to add yours.)

Personally, I would like to see OpenCL succeed. It has the right ingredients as a standard--mainly run-time code generation and reasonable support of heterogeneous computing. On top of that, being in a multi-vendor marketplace is a good thing--also for Nvidia, although they might not immediately see it that way.

If I was starting something new, I would likely go with OpenCL, unless I desperately needed one of the proprietary CUDA libraries. --AndreasKloeckner

If you are on Mac OS X get started with PyOpencl because installing the CUDA Framework is painful right now (summer, 2010). OpenCL comes bundled with your OS and supports more cards so starting is a snap. I agree with Andreas that learning about GPU programming is similar for both frameworks. OpenGL interoperability helped me also since I knew some stuff about OpenGL. Holger

Adding my own opinion here. I am a Game Designer from RIT. I have been using OpenCL for the last 2 months or so, and feel that I have a basic understanding of it, if not a moderate view. My boss told me to look into the development environment for CUDA, due to the fact that OpenCL is SOOOO hard to debug and get working properly. The errors sometimes do not even report the actual problem (i.e. "Out of resources exception" != "Out of bounds exception").

That being said, I also have to have a separate program to debug syntax in OpenCL. CUDA can be used straight through Visual Studio, and it has intellisense. CUDA can also use variables straight out of code, due to it being code. OpenCL is parsed as a string. The CUDA environment is much more user friendly. OpenCL has more "customizable" options for it, but this just leads to code refactoring between machines. CUDA seems to be able to port much more consistently, and its easier to work with Development Environments with CUDA. Overall, I have done OpenCL for 2 months, CUDA for 2 days, and I have had more success with CUDA.

It's currently very difficult to ship binary OpenCL code (maybe necessary for a closed source company). CUDA allows you to compile to a single binary they guarantee various levels of compatibility based upon compiler flags. OpenCL is working on a new specification to alleviate this problem (and enable other cool stuff) called OpenCL-SPIR.

Other comparisons

CudaVsOpenCL (last edited 2018-09-27 14:10:51 by AndreasKloeckner)