Search results
Results From The WOW.Com Content Network
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, 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.
oneAPI is an open standard, adopted by Intel, [1] for a unified application programming interface (API) intended to be used across different computing accelerator (coprocessor) architectures, including GPUs, AI accelerators and field-programmable gate arrays.
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 ...
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. [32]
CuPy supports Nvidia CUDA GPU platform, and AMD ROCm GPU platform starting in v9.0. [ 4 ] [ 5 ] CuPy has been initially developed as a backend of Chainer deep learning framework, and later established as an independent project in 2017.
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 ...
Nicolas Thibieroz, AMD's Senior Manager of Worldwide Gaming Engineering, argues that "it can be difficult for developers to leverage their R&D investment on both consoles and PC because of the disparity between the two platforms" and that "proprietary libraries or tools chains with "black box" APIs prevent developers from accessing the code for maintenance, porting or optimizations purposes". [7]