Why Use GPUs Enhance HPC Application Performance？

High-performance computing (HPC) has revolutionised scientific research, engineering, and data analysis, enabling the resolution of previously unsolvable problems. Central to this transformation is the emergence of GPUs and their integration with HPC systems. This article explores why using GPUs enhances the performance of HPC applications and why they have become indispensable in modern computational science.

What Is the Difference Between a CPU and a GPU?

To understand why GPUs can accelerate computation, we first need to comprehend the differences between CPUs and GPUs. CPUs (Central Processing Units) and GPUs (Graphics Processing Units) are two different types of processors, with distinct design goals and application scenarios. These differences can be detailed in the following aspects:

Design Goals

CPUs are general-purpose processors designed to execute a wide range of computing tasks, such as managing operating systems, file processing, network communication, and running applications. In contrast, GPUs are specialised processors primarily used for graphics and image processing, such as 3D gaming, video editing, and computer-aided design.

Processing Method

CPUs employ a serial processing method, which means they can only process one instruction at a time. This requires multiple steps like fetching, decoding, and executing instructions to complete a task. GPUs, however, use parallel processing methods, allowing them to handle multiple instructions simultaneously and complete numerous tasks at the same time.

Processor Cores

CPUs typically have a few cores, each capable of handling one task. GPUs, on the other hand, usually have hundreds or even thousands of cores. This is because each pixel on an image requires processing, and the process and method of handling each pixel are very similar. GPUs use many simple computational units to perform large numbers of calculations, enabling them to process vast amounts of data simultaneously.

Memory

CPUs generally use high-speed cache and main memory to store data, while GPUs typically use video memory (VRAM) to store image and graphics data. VRAM has faster read and write speeds, allowing GPUs to process graphics and images more swiftly.

Why Use GPU Acceleration Computing?

While CPUs typically have fewer, high-speed cores, GPUs have many lower-speed processing cores. When given a task, the GPU divides it into thousands of smaller subtasks and processes them simultaneously. In graphics rendering, GPUs handle complex mathematical and geometric calculations to create realistic visuals and images.

Instructions must be executed simultaneously, drawing and redrawing images hundreds of times per second, to create a smooth visual experience. GPU pixel processing is a complex process, requiring significant computational power to render the complex textures needed for realistic graphics. This high level of processing power makes GPUs suitable for tasks involving large-scale computations such as machine learning and deep learning.

Which Applications Suit GPU-Accelerated Computing?

GPU-accelerated computing is suitable for applications that require substantial parallel computing, including but not limited to the following areas:

• Data Analysis: Especially in deep learning, the ability to rapidly process large datasets is crucial. GPUs excel at the repetitive parallel computations required by deep learning algorithms, transforming fields such as image and speech recognition, natural language processing, and autonomous driving.

• Computer Vision: This field requires extensive computation for feature extraction, classification, and recognition from images or videos. GPU acceleration can enhance processing speed and accuracy.

• Scientific Computing: High-efficiency numerical computation and simulation of large-scale data are essential for obtaining faster results and more detailed models. Examples include weather forecasting models, molecular dynamics simulations, and astrophysical calculations, all of which involve handling vast amounts of data.

• Cryptography: Cryptography involves substantial encryption and decryption computations, some of which can be accelerated using GPUs to enhance encryption speed and security.

It is important to note that not all applications are suitable for GPU acceleration. GPU-accelerated computing typically requires special code optimisation and parallelisation. While GPU computation speed is generally faster compared to CPU speed, GPUs have relatively weaker memory capacity and computational ability. Therefore, when using GPU-accelerated computing, the characteristics and computational needs of the application must be considered.

Utilising FS GPU-Driven HPC Systems

As HPC becomes a cornerstone of modern enterprises and scientific advancements, the underlying hardware of these systems evolves rapidly. HPC GPU architectures are specifically designed for advanced workloads. FS offers a customised version of the RS6460 server, primarily aimed at HPC, deep learning, and training large language models.

The new generation GPU server RS6460 is the latest 4U dual-socket rackmount accelerated computing server from FS. It features eight double-width GPU computing units within the 4U chassis, providing a high-speed architecture that enables the rapid training and deployment of the largest datasets in HPC. The PCIe 3.0 slots increase I/O flexibility, allowing you to choose NICs with suitable parameters as required. Additionally, a variety of customisation options are available.

The Future of GPUs

GPUs have profoundly impacted HPC by providing the computational power needed to solve complex problems efficiently. Their parallel processing capabilities, combined with advancements in software and infrastructure, make GPUs an indispensable part of fields ranging from scientific research to artificial intelligence and data analysis. Looking ahead, advancements in GPU technology and integration strategies are expected to further enhance HPC performance, driving innovation and development across various sectors.