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Nvidia's CUDA is closed-source, whereas AMD ROCm is open source. There is open-source software built on top of the closed-source CUDA, for instance RAPIDS. CUDA is able run on consumer GPUs, whereas ROCm support is mostly offered for professional hardware such as AMD Instinct and AMD Radeon Pro.
ROCm HIP targets Nvidia GPU, AMD GPU, and x86 CPU. HIP is a lower-level API that closely resembles CUDA's APIs. [47] For example, AMD released a tool called HIPIFY that can automatically translate CUDA code to HIP. [48] Therefore, many of the points mentioned in the comparison between CUDA and SYCL also apply to the comparison between HIP and ...
GPUOpen HIP: A thin abstraction layer on top of CUDA and ROCm intended for AMD and Nvidia GPUs. Has a conversion tool for importing CUDA C++ source. Supports CUDA 4.0 plus C++11 and float16. ZLUDA is a drop-in replacement for CUDA on AMD GPUs and formerly Intel GPUs with near-native performance. [33]
ROCm, launched in 2016, is AMD's open-source response to CUDA. It is, as of 2022, on par with CUDA with regards to features, [citation needed] and still lacking in consumer support. [citation needed] OpenVIDIA was developed at University of Toronto between 2003–2005, [14] in collaboration with Nvidia.
AMD support Year introduced Introduced with Rendering Computing / ROCm; Vulkan [17] OpenGL [18] Direct3D HSA OpenCL; Wonder: Fixed-pipeline [a] 1000 nm 800 nm — — — — — Ended 1986 Graphics Solutions Mach: 800 nm 600 nm 1991 Mach8 3D Rage: 500 nm 5.0 1996 3D Rage Rage Pro: 350 nm 1.1 6.0 1997 Rage Pro Rage 128: 250 nm 1.2 1998 Rage 128 ...
ROCm Created as part of AMD's GPUOpen, ROCm (Radeon Open Compute) is an open source Linux project built on OpenCL 1.2 with language support for 2.0. The system is compatible with all modern AMD CPUs and APUs (actual partly GFX 7, GFX 8 and 9), as well as Intel Gen7.5+ CPUs (only with PCI 3.0).
AMDgpu is an open source device driver for the Linux operating system developed by AMD to support its Radeon lineup of graphics cards (GPUs). It was announced in 2014 as the successor to the previous radeon device driver as part of AMD's new "unified" driver strategy, [3] and was released on April 20, 2015.
CUDA support ROCm support [1] Automatic differentiation [2] Has pretrained models Recurrent nets Convolutional nets RBM/DBNs Parallel execution (multi node) Actively developed BigDL: Jason Dai (Intel) 2016 Apache 2.0: Yes Apache Spark Scala Scala, Python No No Yes Yes Yes Yes Caffe: Berkeley Vision and Learning Center 2013 BSD: Yes Linux, macOS ...