AMD ROCm Platform,CUDA

CUDA® is a parallel computing platform and programming model developed by NVIDIA for general computing on graphics processing units (GPUs). With CUDA, developers can dramatically speed up computing applications by harnessing the power of GPUs.

The CUDA architecture is based on a three-level hierarchy of cores, threads, and blocks. Cores are the basic unit of computation while threads are the individual pieces of work that the cores work on. Blocks are collections of threads that are grouped together and can be run together. This architecture enables efficient use of GPU resources and makes it possible to run multiple applications at once.

The NVIDIA CUDA-X platform, which is built on CUDA®, brings together a collection of libraries, tools, and technologies that deliver significantly higher performance than competing solutions in multiple application areas ranging from artificial intelligence to high performance computing.

Graphics processing units (GPUs) are traditionally designed to handle graphics computing tasks, such as image and video processing and rendering, 2D and 3D graphics, vectorization, etc. General purpose computing on GPUs became more practical and popular after 2001, with the advent of programmable shaders and floating point support on graphics processors.

Notably, it involved problems with matrices and vectors, including two-, three-, or four-dimensional vectors. These were easily translated to GPU, which acts with native speed and support on these types. A milestone for general purpose GPUs (GPGPUs) was the year 2003, when a pair of research groups independently discovered GPU-based approaches for solving general linear algebra problems on working GPUs faster than on CPUs.

NVIDIA describes CUDA as a parallel computing platform and application programming interface (API) that allows software to use specific GPUs for general-purpose processing. CUDA is a software layer that provides direct access to the GPU’s virtual instruction set and parallel computing elements for running compute cores.

Most of NVIDIA’s Tesla and RTX series cards come with a series of CUDA cores designed to perform multiple calculations at the same time. These cores are similar to CPU cores, but they are integrated into the GPU and can process data in parallel. There can be thousands of these cores embedded in the GPU, making for incredibly efficient parallel systems capable of offloading CPU-centric tasks directly to the GPU.

Parallel computing is described as the process of breaking down larger problems into smaller, independent parts that can be executed simultaneously by multiple processors communicating through shared memory. These are then combined at the end as part of an overall algorithm. The primary purpose of parallel computing is to increase available computing power to speed up application processing and problem solving.

To this end, the CUDA architecture is designed to work with programming languages such as C, C++ and Fortran, allowing parallel programmers to more easily utilize GPU resources. This contrasts with previous APIs such as Direct3D and OpenGL, which required advanced graphics programming skills. CUDA-powered GPUs also support programming frameworks such as OpenMP, OpenACC, OpenCL, and HIP by compiling this code on CUDA.

As with most APIs, software development kits (SDKs), and software stacks, NVIDIA provides libraries, compiler directives, and extensions for the popular programming languages mentioned earlier, making programming easier and more effective. These include cuSPARCE, NVRTC runtime compilation, GameWorks Physx, MIG multi-instance GPU support, cuBLAS and many more.

A good portion of these software stacks are designed to handle AI-based applications, including machine learning and deep learning, computer vision, conversational AI, and recommender systems.

Computer vision applications use deep learning to acquire knowledge from digital images and videos. Conversational AI applications help computers understand and communicate through natural language. Recommender systems use a user’s images, language, and interests to deliver meaningful and relevant search results and services.

GPU-accelerated deep learning frameworks provide a level of flexibility to design and train custom neural networks and provide interfaces for commonly used programming languages. All major deep learning frameworks, such as TensorFlow, PyTorch, and others, are already GPU-accelerated, so data scientists and researchers can upgrade without GPU programming.

Current use of the CUDA architecture that goes beyond AI includes bioinformatics, distributed computing, simulations, molecular dynamics, medical analytics (CTI, MRI and other scanning imaging applications ), encryption, etc.

AMD’s ROCm software stack is similar to the CUDA platform, except it’s open source and uses the company’s GPUs to speed up computational tasks. The latest Radeon Pro W6000 and RX6000 series cards are equipped with compute cores, ray accelerators (ray tracing) and stream processors that take advantage of RDNA architecture for parallel processing, including GPGPU, HPC, HIP (CUDA-like programming model), MPI and OpenCL.

Since the ROCm ecosystem is composed of open technologies, including frameworks (TensorFlow/PyTorch), libraries (MIOpen/Blas/RCCL), programming models (HIP), interconnects (OCD), and support upstream Linux kernel load, the platform is regularly optimized. for performance and efficiency across a wide range of programming languages.

AMD’s ROCm is designed to scale, meaning it supports multi-GPU computing in and out of server-node communication via Remote Direct Memory Access (RDMA), which offers the ability to directly access host memory without CPU intervention. Thus, the more RAM the system has, the greater the processing loads that can be handled by ROCm.

ROCm also simplifies the stack when the driver directly integrates support for RDMA peer synchronization, making application development easier. Additionally, it includes ROCr System Runtime, which is language independent and leverages the HAS (Heterogeneous System Architecture) Runtime API, providing a foundation for running programming languages such as HIP and OpenMP.

As with CUDA, ROCm is an ideal solution for AI applications, as some deep learning frameworks already support a ROCm backend (e.g. TensorFlow, PyTorch, MXNet, ONNX, CuPy, etc.). According to AMD, any CPU/GPU vendor can take advantage of ROCm, as it is not a proprietary technology. This means that code written in CUDA or another platform can be ported to vendor-neutral HIP format, and from there users can compile code for the ROCm platform.

The company offers a series of libraries, add-ons and extensions to deepen the functionality of ROCm, including a solution (HCC) for the C++ programming language that allows users to integrate CPU and GPU in a single file.

The feature set for ROCm is extensive and incorporates multi-GPU support for coarse-grained virtual memory, the ability to handle concurrency and preemption, HSA and atomic signals, DMA and queues in user mode. It also offers standardized loader and code object formats, dynamic and offline compilation support, P2P multi-GPU operation with RDMA support, event tracking and collection API, as well as APIs and system management tools. On top of that, there is a growing third-party ecosystem that bundles custom ROCm distributions for a given application across a host of Linux flavors.

To further enhance the capability of exascale systems, AMD also announced the availability of its open source platform, AMD ROCm, which enables researchers to harness the power of AMD Instinct accelerators and drive scientific discovery. Built on the foundation of portability, the ROCm platform is capable of supporting environments from multiple vendors and accelerator architectures.

And with ROCm5.0, AMD extends its open platform powering the best HPC and AI applications with AMD Instinct MI200 series accelerators, increasing ROCm accessibility for developers and delivering industry-leading performance on workloads keys. And with AMD Infinity Hub, researchers, data scientists, and end users can easily find, download, and install containerized HPC applications and ML frameworks optimized and supported on AMD Instinct and ROCm.

The hub currently offers a range of containers supporting Radeon Instinct™ MI50, AMD Instinct™ MI100, or AMD Instinct MI200 accelerators, including several applications such as Chroma, CP2k, LAMMPS, NAMD, OpenMM, etc., as well as frameworks Popular TensorFlow and PyTorch MLs. New containers are continually being added to the hub.