AI is just one specific workload that is supported by High Performance Computing. CrunchYard’s pioneering Office HPC solution delivers all the computational power, speed and security required by most HPC & AI projects

HPC and AI are not synonymous. AI uses and presupposes HPC – it is very difficult to deliver the computational power required by large language models or other forms of AI without HPC – but the two are not the same. AI is just a specific type of workload.

AI requires HPC because of the following factors:

Computational Demands: AI, especially deep learning, involves intensive computations, including matrix multiplications and convolutions, which require high-performance processing power that HPC systems provide.

Parallel Processing: HPC infrastructure is designed for parallel processing, which is essential for AI workloads. Training AI models can be parallelised across multiple CPUs or GPUs, significantly reducing training time.

Data Processing: AI models often require the processing of large datasets, which HPC systems are well-equipped to handle due to their high data throughput capabilities and large memory.

Model Training: Training AI models, particularly deep neural networks, involves a large number of iterations over massive datasets, which is computationally expensive and suited to the power of HPC environments.

Resource Scalability: HPC systems offer scalable resources that can be adjusted based on the AI workload. This flexibility allows for the efficient use of computing resources, whether for training models or running complex simulations.

Distributed Computing: AI workloads often benefit from distributed computing, where the training of models is spread across multiple nodes in an HPC cluster, enabling faster computation and the handling of larger models.

High-Throughput Networking: HPC environments provide high-throughput, low-latency networking, which is crucial for AI workloads that require fast communication between distributed computing nodes.

GPU Acceleration: Many HPC infrastructures include GPUs, which are optimized for the types of calculations AI models require, such as tensor operations. GPUs significantly accelerate the training and inference processes in AI.

Memory Requirements: HPC systems often have large, fast memory architectures that are necessary for handling the large datasets and model parameters typical of AI workloads.

Optimized Storage Solutions: HPC infrastructures use advanced storage solutions that can manage and provide rapid access to the vast amounts of data required for AI training and inference tasks.

Energy Efficiency: HPC systems are designed to be energy-efficient at scale, which is critical for AI workloads that can be computationally intensive and run for extended periods.

Reliability and Fault Tolerance: HPC infrastructures are built with reliability in mind, with fault-tolerant systems that ensure AI workloads can run uninterrupted, even in the event of hardware failures.

Job Scheduling: HPC systems utilize advanced job scheduling and resource management techniques, ensuring that AI tasks are efficiently allocated resources and can run in parallel with other workloads.

Simulation and Modelling: AI is increasingly used in conjunction with traditional HPC tasks like simulations and modelling, where HPC provides the computational backbone, and AI enhances predictive accuracy and efficiency.

HPC can, however, be used for non-AI purposes as well. One example would be, for instance, Computational Fluid Dynamics (CFD). There are slightly different approaches that require customising HPC to the underlying functions required. There are also some differences in the requirements of CFD when compared to AI.

Differences in Computational Nature

AI workloads primarily involve large-scale matrix operations, tensor manipulations, and optimisation tasks, focusing on pattern recognition, classification, and predictions; whereas CFD workloads involve solving complex physical equations (like Navier-Stokes equations in CFD) using numerical methods to simulate real-world physical phenomena such as fluid flow, heat transfer, and turbulence.

Data Dependency

While AI workloads are highly data-driven, requiring vast datasets for training models (both quality and quantity of data significantly influence the performance and accuracy of AI models), traditional HPC workloads might be less dependent on large datasets. Instead, they rely on precise initial conditions, boundary conditions, and numerical algorithms to simulate physical processes.

Computational Intensity

AI Workloads tend to be more GPU-intensive due to the need for parallel processing of large-scale data, especially during model training. Computational intensity grows with the complexity of the model and the size of the dataset. In contrast, CFD workloads are typically more CPU-intensive, requiring high precision floating-point operations and significant memory bandwidth to handle the complex mathematical computations inherent in physical simulations.

Parallelism

AI Workloads often exhibit a high degree of parallelism, with tasks like matrix multiplications being easily distributed across multiple processing units (GPUs/TPUs). However, some AI models may face challenges with parallelisation due to dependencies in neural network layers. More traditional workloads, including CFD may also rely on parallelism, but are often constrained by the sequential nature of the numerical algorithms used. The efficiency of parallel execution can be limited by inter-process communication and synchronisation needs.

Outcome and Application

The primary outcome of AI is a trained model that can make predictions or classifications. AI is increasingly applied in fields like natural language processing, image recognition, and autonomous systems. The outcome of a more traditional workload is typically a detailed simulation or model of a physical system, used to understand and predict physical behaviour in fields like aerospace, automotive, and climate modelling. The focus is on precision and accuracy of the physical representation.

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