Understanding GPU Partitioning for Enhanced Performance

GPU partitioning is a technology that enables several users to share a single GPU by separating its resources into virtual segments. Sharing GPU resources is extremely useful when several workflows or users need access to GPU partitioning but do not require a complete dedicated GPU. However, as demand for specialised hardware grows and GPU partitioning continues, we should expect more companies to invest in purpose-built GPU architectures to remain competitive.

Unbelievably, organizations are lagging when technological advancement is not balanced with infrastructure and financial constraints. Renting parts of high-performance GPUs rather than full units is something that emerging firms also want to do.

Businesses face a conundrum: the need for processing power, support for new technologies like generative AI, and management of multiple workloads. They now have to decide whether to invest in sophisticated computing resources or postpone the project to manage it within the constraints of their present capabilities.

 

NVIDIA’S Innovation in GPU Partitioning

NVIDIA is a top provider of GPUs, which are essential for high performance computing applications such as AI and cloud computing. As a result, many businesses rely on NVIDIA GPU partitioning to meet their computing needs by maximizing their power and efficiency. NVIDIA recognized early on that GPUs could be programmed for general applications such as AI, science and engineering and built an ecosystem around them. They also provide software development kits and libraries to help with GPU partitioning. Lets take a look at what sets NVIDIA apart from its competitors:-

• The CUDA (Compute Unified Device Architecture) Advantage: CUDA is NVIDIA’s proprietary software platform that transformed GPUs from graphics-only chips into general-purpose powerhouses. CUDA gave developers a straightforward way to write programs that run on GPU hardware. Today, most AI frameworks and engineering software are built around CUDA by default.
• A Rich AI Ecosystem Out of the Box: NVIDIA not only sells hardware but also offers a comprehensive set of tools that make GPU programming easier. It includes efficient libraries and tools for training modules, compressing AI code, and managing many GPUs simultaneously. Engineers do not have to create these from scratch.
• The Cloud runs on NVIDIA: Every major cloud service provides NVIDIA GPUs as a key choice. Engineers can easily deploy and scale their applications using NVIDIA’s hardware and software ecosystem, making it the preferred choice for cloud-based AI and engineering workloads. In light of this extensive acceptance, when engineers or businesses require GPU power in the cloud, they nearly invariably use NVIDIA hardware.
• Raw Performance Built for Modern Workloads: NVIDIA GPUs are engineered to excel at exactly the tasks modern engineering demands, i.e. deep learning, image processing, physics simulations, and large-scale parallel computation. Their architecture is optimized to move massive amounts of data quickly, which is the core bottleneck in nearly every high-performance computing task. As a result, NVIDIA GPUs can provide unparalleled raw performance when it comes to handling these complex workloads.
• Safety and Stability Across Partitioned Workloads: When multiple processes share a GPU, one failing process can crash everything. NVIDIA’s architecture keeps each workload isolated at the driver level, so problems don’t cascade. By ensuring that each workload is partitioned and isolated, NVIDIA GPUs can prevent catastrophic system failures and maintain the reliability and efficiency of high-performance computing tasks.

How can hardware engineers benefit from GPUs?

It’s a well known fact that standard GPUs provide functionalities for multiple users without requiring any physical hardware. However, hardware engineers typically have to build systems that need real-time processing or high-performance calculations. This is where specialized GPU architectures known as bare-metal GPUs come into play.

Bare-metal GPUs offer direct hardware access, enabling fine-tuning of GPU performance for complex computational workloads. This level of control allows you to configure system parameters such as GPU clock speed, power limits, and memory management.

Hardware engineers can get around constraints posed by conventional operating systems using bare metal GPUs, which lowers latency and increases overall system responsiveness. Furthermore, the ability to change GPU settings based on your workload requirements maximizes the hardware’s overall efficiency.

Let’s take a look at some of the applications where bare metal GPUs can come in handy:-

• High-Fidelity Digital Twin Prototyping: Fractional GPU slices can be used to virtualize hardware logic and test RTOS code against virtual motors and sensors for a fraction of the expense of a dedicated local workstation.
• Edge AI and TinyML Model Optimization: Specialized TinyML models can be compiled on bare metal GPUs before being loaded onto resource-constrained hardware such as the ESP32 or ARM Cortex-M, which call for “shrinking” huge AI models to fit on-board memory.
• Accelerated HMI and Graphical Interface Design: Designing complicated dashboards for automotive or medical applications that require high-resolution rendering by means of virtual GPUs without exceeding the capabilities of conventional development laptops.
• Custom Kernel Development: Bare-metal environments provide the transparency and control required to fine-tune hardware configurations, which include creating custom CUDA kernels for enhancing specific scientific computations or simulations.
• Secure Hardware Isolation: Physical separation capabilities provided by bare-metal GPUs prevent the “noisy neighbor” effect of shared virtual environments from jeopardizing delicate AI tasks.

Some of the most frequently asked questions 

Q1: Is buying a high-end GPU for your Laptop better?

A: Not necessary, as long as you don’t need it for serious gaming or graphic design work. If you wish, you could use a cloud TPU or an open-source GPU that is available online and does not need to be installed on your device. This way, the battery life won’t be severely depleted or destroyed.

Q2: What does it mean when a Laptop has a “built-in” GPU?

A: The laptop’s CPU and GPU are merged into a single chip, which is known as integrated graphics. This setup maximizes energy efficiency, decreases physical space, and lowers manufacturing costs. Additional benefits include the same unit handling processing and graphics tasks.

Q3: How come Google TPUs are cheaper than Nvidia GPUs?

A: TPUs are based on systolic array architecture, which simplifies the physical hardware chip design by allowing data to flow across ALU grids in a wave-like fashion, reducing the need for back-and-forth reading and writing to memory registers. This minimises component prices, maximizes data reuse, and lowers manufacturing and utility costs. Hence, Google TPUs are ideal for cloud infrastructure rentals and large-scale training, whereas NVIDIA GPUs are better suited for initial purchases and low-volume applications. However, both can be utilized effectively depending on the specific needs and budget constraints of the user.

Q4: Is a core the same when it comes to CPUs and GPUs?

A: No. A CPU core is designed to execute instructions sequentially, whereas a GPU core is built for parallel processing. CPU cores are in charge of executing program instructions, completing calculations, and controlling data flow, among other components. GPU cores are designed to handle visuals, produce images, and process massive volumes of data at once.

Q5: What are the common NVIDIA GPU models, and what do their names signify?

A: NVIDIA identifies its GPUs with letters and numbers to indicate their generation and power level. In layman’s words, larger numbers indicate more power, and the order from weakest to strongest for AI jobs is T, V, A, and then H. Check this out:

• The T-Series (“T” = Tesla or “Turing”) are entry-level GPUs designed for mild AI jobs for beginners. For example, T4, T1000, and T2000.
• The V-Series (“V” = Volta) is from an older generation that was once the norm for data center AI work. For example, V100.
• The A-Series (“A” = Ampere, i.e., AI-focused GPUs) refers to the current workhorse GPUs designed exclusively for serious AI training. For example, A10, A30, A100.
• The H-Series (“H” = Hopper, i.e., Newest Generation) is the most recent and quickest generation. Intended to handle cutting-edge AI tasks. For example, H100, H200.
• The RTX-Series (“RTX” means Ray Tracing Texel Extreme) is designed for gaming and graphics, although it is also utilized by researchers and professionals. For example, RTX 3090 and RTX 4090

 

This post was inspired by (Rising AI demand drives shift to fractional GPU model by Shlipa Phadnis and Supriya Roy | Times of India)

References:-

• https://medium.com/@davide.ruti/a-deep-dive-into-gpu-sharing-technologies-5c32a4e1a6d4
• https://www.digitalocean.com/resources/articles/bare-metal-gpus
• https://research.colfax-intl.com/sharing-nvidia-gpus-at-the-system-level-time-sliced-and-mig-backed-vgpus/
• https://clear.ml/blog/open-source-fractional-gpus-for-everyone-now-available-from-clearml
• https://ieeexplore.ieee.org/document/8743200