What is a GPU?

GPUs (Graphics Processing Units), also known as graphics processors, are specialized electronic circuits originally developed for the rapid manipulation and display of images, videos, and animations. Compared to CPUs (Central Processing Units), GPUs are specialized in performing many calculations simultaneously (parallel processing), making them particularly effective for computationally intensive tasks.

Today, GPUs are indispensable components in High Performance Computing (HPC) and are the backbone of modern data centers for scientific simulations and AI applications.

A GPU, or graphics processor, is a specialized electronic circuit designed for the rapid manipulation and modification of memory to accelerate the creation and display of images, videos, and animations in a framebuffer. It is an essential component of modern computers, smartphones, gaming consoles, and numerous other devices that process graphical data. A GPU ensures that the commands processed by the CPU are graphically implemented on the screen.

In the HPC domain, however, the role of the GPU has fundamentally expanded: It has become the primary computational accelerator for scientific and industrial applications.

The functionality of a GPU is based on its ability to handle many tasks simultaneously. A GPU consists of a large number of small cores, referred to as "shaders" or in the HPC context as "CUDA cores" (for NVIDIA). These cores work together in parallel to perform the complex mathematical calculations required for generating graphics. They ensure that colors, shading, textures, and movements appear seamlessly on the screen.

Graphics processors work with a method known as parallel processing, where multiple processors handle separate parts of a single task. The GPU has its own memory (VRAM), specifically designed to store the large amounts of information that enter the graphics processor in highly intensive graphics applications. In graphics applications, the CPU sends instructions to the GPU to draw the graphic content on the screen, which the GPU then executes in parallel at high speed. Modern GPUs are also capable of using special techniques such as ray-tracing and deep learning to create even more realistic graphics.

In the HPC domain, GPUs utilize their parallel processing capabilities for scientific calculations, simulations, and AI training, achieving speed improvements of 10x to 100x compared to CPU solutions.

The GPU is indispensable today. It enables modern computers to display high-resolution images and videos, and ensures smooth performance in games as well as graphics applications. It plays an essential role in assisting the CPU in graphics-intensive tasks, allowing the CPU to use its resources for other important tasks. Without GPUs, many modern games and AI applications would not be feasible. GPUs are an essential enabler for emerging and future technologies such as machine learning (ML), artificial intelligence (AI), and blockchain by reducing the execution time of mathematical calculations.

In High Performance Computing, GPUs have become indispensable for managing the exponentially growing data volumes and computational complexity in science and industry.

The use of GPUs is not limited to graphics. These powerful chips are used in many different areas where large amounts of data need to be processed quickly:

• High Performance Computing (HPC): For genome research, climate modeling, particle physics, scientific simulations, and complex calculations. This is the primary focus for professional GPU systems.
• Artificial Intelligence (AI) and Machine Learning (ML) / Deep Learning (DL): For training AI models, image recognition, facial recognition, speech recognition, and accelerating the learning process. Modern Large Language Models are trained exclusively on GPU clusters.
• Science and Research: Molecular dynamics simulations, Computational Fluid Dynamics (CFD), weather forecasting, and astronomical calculations.
• Medical Imaging: Generating detailed body images in MRI or CT scans, as well as real-time image processing during medical procedures.
• Finance: Complex calculations for trading analysis, risk management, and algorithmic trading with real-time requirements.
• Gaming: For smooth, high-resolution graphics and an immersive gaming experience.
• Image and Video Editing / Content Creation: Accelerating effects, filters, retouch, and real-time video encoding.
• Architecture and Design: Creating 3D models and animations, CAD rendering.
• Cryptocurrency Mining / Blockchain: Fast, parallel mathematical calculations.
• Virtual Reality (VR) and Augmented Reality (AR): Creating immersive experiences.
• Cloud Gaming and Streaming

There are different types of GPUs developed for different purposes:

Integrated GPUs (iGPUs): These are integrated into the same chip as the CPU or directly on the motherboard. They share memory (RAM) with the CPU and are generally less powerful but cost-effective, energy-efficient, and space-saving. They are suitable for everyday tasks like browsing or word processing and are often installed in notebooks. They are not suitable for HPC applications.

Dedicated GPUs (dGPUs): These are standalone hardware components separate from the CPU. They have their own video memory (VRAM), require separate power supply and a powerful power supply unit. They offer significantly higher performance for demanding tasks such as gaming, 3D rendering, image and video editing, as well as AI and machine learning. All HPC GPUs fall into this category.

HPC-specific GPUs: NVIDIA offers specialized GPUs for data centers such as the B200, H200, H100, A100, as well as RTX PRO 6000 Blackwell Server Edition and L40S, designed for maximum computing power and 24/7 operation.

Virtual GPUs (vGPUs): These are software-based versions of a GPU for a cloud instance that don't require physical hardware. They are simpler and cheaper to maintain than their physical counterparts. vGPUs are essential for virtual desktops, cloud-based solutions, and virtualized applications.

Additionally, there are specific gaming GPUs, professional GPUs, mobile GPUs, server GPUs, and embedded GPUs, each optimized for their area of application.

GPU benchmarks are methods for evaluating GPU performance under various conditions. They are specialized software tools that provide users (e.g., gamers, 3D artists, system developers) insights into their GPUs and help identify performance issues such as bottlenecks, latencies, and compatibility. There are two main types: synthetic benchmarks that test raw performance in a standardized environment, and real-world benchmarks that test performance in specific applications. Benchmarking tools examine performance metrics such as speeds, frame rates, and memory bandwidth.

In the HPC domain, special benchmarks such as LINPACK, HPL-AI, and MLPerf are relevant, measuring performance in scientific calculations and AI workloads.

GPUs offer significant advantages over CPUs, particularly for applications requiring high parallelization. These advantages include:

• Unprecedented Parallel Processing Performance: GPUs are optimized for parallel processing and can execute thousands of calculations simultaneously, leading to faster model training and real-time inferences. In HPC, this means drastic time reductions for simulations.
• Accelerated Performance: GPUs significantly accelerate the learning process and processes in various fields, driving development forward. Climate models that would take months on CPUs can be calculated on GPUs in days.
• CPU Relief: By taking over graphics processing and computationally intensive tasks, GPUs reduce workloads of the CPU, which then can focus on other tasks, improving the computer's overall performance.
• Specialized Technologies: Modern GPUs offer specialized technologies such as raytracing and AI-based improvements that create realistic and impressive graphics. In the HPC domain, Tensor Cores and Multi-Instance GPU (MIG) are particularly relevant.
• Energy Efficiency: For certain tasks requiring many simultaneous calculations, GPUs are often more energy-efficient than CPUs. This is particularly important for large data centers.
• Scalability: GPU server plans enable flexible and real-time scalability of computing capacities to fit demand, ensuring consistent performance without oversizing.
• Cost Efficiency: Companies can reduce operating costs by using NVIDIA GPU servers because they deliver very high performance even with low infrastructure and avoid expensive hardware purchases and their maintenance.

A GPU server is a server class that performs the majority of its calculations in graphics processors (GPUs), as opposed to traditional servers that mainly rely on CPUs. GPU servers offer a drastic increase in performance for complex calculations and have become important building blocks for High Performance Computing (HPC). They are designed to work with high loads such as machine learning, scientific modeling, and real-time data. NVIDIA GPU servers have played a significant role in improving computing capabilities in recent years and are transforming various industries through their state-of-the-art computing power and ease of use.

NVIDIA DGX systems are the reference for HPC GPU servers and offer up to 8 GPUs in a system with optimized cooling and interconnected technology.

What is a GPU Server used for?

GPU servers are used wherever highly parallel computing processes are required. They offer significantly higher performance for compute-intensive workloads compared to classic CPU systems. Typical application areas for GPU servers include:

• AI Training, Machine Learning, and Deep Learning: Training Large Language Models and Computer Vision systems.
• Scientific Simulations and High Performance Computing (HPC): Molecular dynamics, climate research, fluid mechanics.
• CAD Rendering and 3D Modeling: For engineering and product development.
• Video Encoding and Video Editing: In professional production environments.
• Real-time Data Processing: For sensor data and IoT applications.
• Big Data Applications: Accelerated data analysis with RAPIDS.
• Medical Research: Drug discovery and genome analysis.
• Professional Graphics Applications: Such as architectural visualization, film and animation, and virtual reality.
• Cloud-based GPU servers accelerate important workloads in AI and graphics.

A Cloud GPU is a cloud-based GPU service or virtual GPU (vGPU) that eliminates the need for GPU hardware and software deployment on a local device. Companies can thus avoid expensive hardware purchases and their maintenance. Cloud GPUs offer:

• Flexible and scalable computing power as needed - ideal for HPC workloads with varying loads.
• Access to different GPU types for diverse workloads and budgets, including the latest NVIDIA models.
• Reduction of time, costs and the use of external resources.
• Suitable for applications such as 3D rendering, ML model training, gaming, medical imaging, financial risk management, generative AI, and data analysis.
• Many cloud service providers (CSPs) such as Google Cloud, AWS, Microsoft, and IBM Cloud offer Cloud GPUs, often with special HPC instances.

Both GPUs and CPUs are processors that are crucial in computers. The main difference lies in their specialization and architecture:

CPU (Central Processing Unit): The "heart" and "brain" of a computer. CPUs are general-purpose tools designed to handle a wide range of tasks sequentially and flexibly. They typically have fewer but more powerful cores (generally 8-64). A CPU can perform a single calculation faster because it has a higher clock frequency.

GPU (Graphics Processing Unit): Specifically designed for the rapid manipulation and display of images, videos, and animations. GPUs are specialized in performing many calculations simultaneously (in parallel). They reduce CPU workload off of graphics-intensive tasks. GPUs have hundreds or even thousands of cores (an NVIDIA H100 has over 16,000 CUDA cores) and can manage more threads per core than CPUs.

Ideally, in the HPC context, CPU and GPU complement each other: The CPU manages program flow and sequential tasks, while the GPU handles the compute-intensive parallel calculations.

High-End GPUs are powerful graphics cards designed for demanding tasks. They are generally dedicated graphics cards and offer significantly higher performance than integrated GPUs. Features of High-End GPUs for HPC:

• Have their own large video memory (VRAM), often 40 GB to 80 GB in HPC models, specifically used for large datasets.
• Require a separate power supply and a powerful power supply unit (up to 700W for the H100).
• Generate more heat and therefore require an effective cooling system, often with liquid cooling in data centers.
• Support specialized technologies such as Tensor Cores, Multi-Instance GPU (MIG), and NVLink for GPU-to-GPU communication.
• Examples for HPC include: NVIDIA B200 (Blackwell architecture), NVIDIA H100 & NVIDIA H200 (Hopper architecture), NVIDIA A100 (Ampere architecture), NVIDIA L40S, and the NVIDIA Grace Hopper Superchip.

GPU programming is the process of using graphics processors (GPUs) to perform general computational operations that don't necessarily have to be graphics-related. This revolutionizes the speed and efficiency of calculations in the HPC domain. Basic concepts of GPU programming include:

• Parallel Processing: The simultaneous execution of multiple computing processes leads to significant acceleration.
• Threads: The smallest units of process execution running in parallel on the GPU.
• Kernels: Functions executed on the GPU and processed by threads.
• Memory Management: Crucial for performance as it affects data management between CPU and GPU memory. Direct Memory Access (DMA) enables fast data transfers between RAM and GPU memory without CPU intervention.

NVIDIA has developed CUDA (Compute Unified Device Architecture), its own parallel computing architecture that has made GPU programming accessible to a broader audience. CUDA enables developers to perform application-specific calculations with NVIDIA graphics processors. In the HPC domain, CUDA-X Libraries (cuBLAS, cuFFT, cuDNN) and frameworks like OpenACC are just as important important.

Graphics cards play a central and indispensable role in the field of Artificial Intelligence (AI), specifically in the research and development of machine learning (ML) and deep learning (DL). They are important for several reasons:

• Parallel Processing: GPUs are optimized for parallel processing, meaning they can perform many calculations simultaneously. This is essential for training AI models that must process huge amounts of data.
• Acceleration of Training: GPUs significantly accelerate the learning process and enable complex models to be trained and iterated faster, driving AI development further. Training GPT models would be impossible without GPU clusters.
• Large Data Volumes: Graphics cards have high memory (VRAM) that allows storing large amounts of data and accessing them quickly, which is essential for efficient training of AI models.
• Specialized AI GPUs: There are special GPUs and architectures (e.g., NVIDIA's Tensor cores) directly optimized for deep learning workloads and AI applications.
• Framework Compatibility: Many AI frameworks and libraries (e.g., TensorFlow, PyTorch) are optimized for GPU usage to utilize maximum performance.

NVIDIA GPUs have been playing a significant role in developing and improving computing capabilities and are leaders in AI computing. They are well-suited for AI server solutions for the following reasons:

• Sophisticated Structure for Parallel Tasks: NVIDIA's graphics processors are specifically designed for processing parallel tasks.
• Tensor Cores: These were specifically developed to efficiently execute deep learning workloads, reducing training and inference time. They enable mixed precision calculations (16- and 32-bit floating point) to improve performance throughput.
• CUDA Architecture: NVIDIA's proprietary parallel computing architecture enables efficient handling of computing processes, making training and inference of AI models more effective. The CUDA toolkit simplifies setting up deep learning processes.
• Solid Compatibility: NVIDIA GPUs are characterized by robust compatibility with many AI frameworks and programming languages, enabling seamless integration into existing AI workflows.
• Scalability: They enable managing extended workloads while maintaining system state, making them suitable for increasing AI requirements in various sectors. NVIDIA GPU servers offer maximum performance, flexibility, and resource optimization
• Energy Efficiency: NVIDIA servers are designed to be energy-efficient while delivering high performance, reducing losses.