If you are interested in getting into GPU programming, you are most probably facing a difficult choice right now: OpenCL or Cuda. Both OpenCL and Cuda are software frameworks that enable GPUs to help accelerate processing in applications where they are respectively supported. Traditionally, the CPU can send data to the GPU, but the GPU cannot pass information back to the CPU. However, general-purpose computing on graphics processing unit (GPGPU) is able to send back information to the CPU, allowing such bidirectional processing. If the application you use supports either OpenCL or Cuda, and you use it with hardware that supports the relevant GPGPU framework, you will get a huge performance boost. So, what are the differences between OpenCL and Cuda? As a developer, which one should you learn?
Right now, OpenCL is the leading open-source GPGPU framework. OpenCL is an open standard that is maintained by the non-profit tech consortium Khronos Group. Some time ago, only AMD products properly supported OpenCL, but now it has been supported by many others as well such ARM, Qualcomm, Altera, Intel, and even Nvidia. It can run on Android, FreeBSD, Linux, Windows, and macOS. So, OpenCL programs can function on various devices and models.
On the other hand, Cuda is currently the leading proprietary GPGPU framework. Even so, Cuda also supports other programming frameworks including OpenCL. It was developed and is maintained by Nvidia Corporation. As you may have expected, Cuda is only supported by Nvidia GPUs. If you write a program using Cuda, the program can only function on Nvidia GPUs – you will have to re-write your code in OpenCL if you want the program to run on other platforms. However, Cuda can run on Windows, macOS, and Linux.
It should be noted, however, that OpenCL is easy to set up, use, and compile in both Windows and Linux. Having display drivers is enough to run OpenCL programs, and you just need to install the SDK for programming. On the other hand, Cuda is known to work great on Windows, but Linux Cuda has strange requirements such as using a specific version of GCC. If you use a different GCC version, you won’t be able to compile.
Perhaps the downside of OpenCL is that it is packed with so many features and many ways to write your code. Hence, it may feel complicated to learn at first. Cuda is relatively more straightforward.
Regarding the performance, there have been numerous tests and benchmarks that try to show the performance comparisons between OpenCL and Cuda. However, they are actually memory bandwidth bound instead of compute bound, which means that their performance largely depends on the memory access patterns. So, it is difficult to tell for sure what performance to expect ahead of time. Still, both OpenCL and Cuda work well as intended, which is to increase the system’s processing power. Worth noting, though, that Nvidia GPUs generally give better performance with Cuda than with OpenCL.
|- Supported by various operating systems and GPUs||- Can run on several operating systems but only with Nvidia GPUs|
|- Works great on Linux||- Has particular requirements to compile on Linux|
|- More flexible||- Somewhat more straightforward|
|- Generally slower on a Nvidia GPU||- Generally faster on a Nvidia GPU|
Due to the open-source nature and flexibility, OpenCL should be the way to go if you want your programs to run on various platforms. But, with such flexibility, OpenCL may feel more complicated to learn. However, if you specifically aim to make use of Nvidia GPUs, Cuda is a nice choice. It is somewhat more straightforward, and, of course, on Nvidia GPUs, Cuda generally gives better performance.