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CUDA is a wonderful piece of tech that allows you to squeeze every bit out of your Nvidia GPU. However, it only works with NVIDIA, and it’s not easy to port your existing CUDA code to other platforms.
You look for an alternative to CUDA, obviously.
What are the alternatives to CUDA?
- OpenCL: An open standard for parallel programming across CPUs, GPUs, and other processors with some performance overhead compared to CUDA.
- AMD ROCm: An open-source GPU computing platform developed by AMD that allows the porting of CUDA code to AMD GPUs.
- SYCL: A higher-level programming model based on C++ for heterogeneous processors enabling code portability across CUDA and OpenCL through Intel’s DPC++ and hipSYCL.
- Vulkan Compute: It is a compute API of the Vulkan graphics framework, enabling GPU computing on a wide range of GPUs with lower-level control.
- Intel oneAPI: It is a cross-architecture programming model from Intel, including a DPC++ compiler for SYCL, offering an alternative to CUDA for Intel GPUs.
- OpenMP: It is an API for parallel programming on CPUs and GPUs. It uses compiler directives, and recent versions support GPU offloading as an alternative to CUDA.
Let’s address each with more depth.
1. OpenCL
OpenCL (Open Computing Language) is an open industry standard maintained by the Khronos Group that lets you utilise parallel programming across various platform architectures.
OpenCL allows you to write a program once, which it can then run on several different processors from different companies like AMD, Intel, and NVIDIA.
This can be useful if you want to use the hardware you already have or if you want to choose the best processor for a specific task, regardless of which company made it.
2. AMD ROCm
ROCm (Radeon Open Compute) is a platform designed by AMD to run code effectively on AMD GPUs. But the best part is that ROCm is open-source and can be accessed by everyone.
One of the most important parts of ROCm is called Heterogeneous-computing Interface for Portability, or HIP. HIP is quite close to CUDA programming in terms of syntax. This means if you know how to program CUDA then there’s no stiff learning curve if you’re switching over.
There’s even a tool called HIPIFY that can automatically convert CUDA code into code that works with HIP and AMD GPUs, with just a few minor changes required.
3. SYCL
SYCL (pronounced “sickle”) is a higher-level programming model based on standard C++ for heterogeneous processors. SYCL is built on top of the C++ programming language, enabling code portability across OpenCL devices.
The core idea of SYCL is to provide the performance of OpenCL with the flexibility of C++. Good examples of SYCL include Intel’s DPC++ (Data Parallel C++) based on Clang/LLVM that can target CUDA and OpenCL devices.
4. Vulkan Compute
Vulkan’s low-overhead design and close-to-metal nature can enable performance close to and sometimes even exceeding CUDA in many compute workloads. It provides compute shaders to enable GPU computing.
Since Vulkan Compute is a relatively new technology, its ecosystem is still maturing in terms of libraries, tools and language binding. It has a steeper learning curve, especially when graphics interoperability is also used.
However, new Vulkan Compute-focused frameworks like Kompute are emerging to make Vulkan GPU computing more accessible.
While Vulkan Compute can also interoperate with APIs like OpenCL, CUDA and DirectX 12, there are some very specific features like CUDA’s dynamic parallelism, that are not available with Vulkan.
5. Intel oneAPI
oneAPI is an open, unified programming model developed by Intel that aims to simplify development across diverse computing architectures (CPUs, GPUs, FPGAs, and other accelerators).
oneAPI consists of a core set of tools and libraries, including DPC++ language and libraries for deep learning, machine learning and more.
A key goal of oneAPI is to provide an alternative to proprietary models like NVIDIA’s CUDA. It aims to prevent vendor lock-in and allow code portability across Intel, NVIDIA, AMD and other hardware.
Furthermore, case studies have shown up to an 18x speedup for compute-intensive algorithms using oneAPI tools and Intel hardware.
6. OpenMP
Open Multi-Processing, or OpenMP, is an API that supports multi-platform shared-memory parallel programming in C, C++, and Fortran. It has also been used for parallel computing in CPUs.
Recent versions of OpenMP, starting from version 4.0, have introduced support for GPU offloading. This allows OpenMP to be used for GPU computing as an alternative to CUDA.
OpenMP provides a higher level of abstraction compared to CUDA. It handles many low-level details like data movement and kernel launches automatically, which can make it easier to use for some developers.
CUDA is a proprietary solution from Nvidia and it is fine-tuned to get the most out of Nvidia hardware. So, finding an exact replacement may not be possible as they’ll always have an advantage over any open-source platform. Sure, if you want to run parallel computation over other GPUs, then the given solution will get the job done in the most efficient way possible.
