Linux is a popular operating system for developing and running artificial intelligence (AI) applications, as it offers flexibility, stability, and compatibility with various hardware and software platforms. However, not all graphics processing units (GPUs), which are essential for accelerating AI computations, have open-source drivers that can fully utilize their features and performance. Therefore, some Linux distributions offer the option of installing proprietary graphics drivers from GPU vendors such as NVIDIA and AMD, which can provide better support and optimization for their products.
One of the benefits of proprietary graphics drivers is that they enable access to specific frameworks and libraries that are designed for GPU-based AI development. For example, NVIDIA’s CUDA is a parallel computing platform and application programming interface (API) that allows software to use NVIDIA GPUs for general-purpose processing. CUDA supports various programming languages, tools, and libraries for AI development, such as TensorFlow, PyTorch, CuPy, etc. Similarly, AMD’s ROCm is a general-purpose computing platform that supports multiple domains such as high-performance computing (HPC) and heterogeneous computing. ROCm also offers several programming models such as HIP (GPU-kernel-based programming), OpenMP/Message Passing Interface (MPI), and OpenCL. Some deep-learning frameworks already support a ROCm backend (e.g., TensorFlow, PyTorch, MXNet, ONNX, CuPy, etc.).
Another advantage of proprietary graphics drivers is that they can improve the performance and efficiency of GPU-based AI computations by leveraging the specific features and architectures of different GPU models. For instance, NVIDIA’s Tesla and RTX series GPUs have CUDA cores that can handle multiple calculations at the same time. They also have tensor cores that can accelerate matrix operations for deep learning applications. On the other hand, AMD’s Radeon Pro line GPUs have RDNA microarchitecture that can deliver high bandwidth and low latency for data-intensive workloads. They also have CDNA microarchitecture that can optimize HPC applications with enhanced floating-point performance.
In conclusion, proprietary graphics drivers can offer significant benefits for Linux-based AI development by enabling access to specialized frameworks and libraries as well as optimizing performance and efficiency based on different GPU features and architectures. Therefore, it is important for a Linux distribution to give the option of installing proprietary graphics drivers for users who want to take advantage of these benefits.
(Disclosure: I got chatGPT 4 to write this. )