What Is High-Performance Computing?

With the ability to handle massive operations beyond the reach of regular machines,  High-Performance Computing (HPC) drastically reduces processing time.

Also known as supercomputing, HPC holds immense importance in our data-driven world. The exponential growth of data, particularly from IoT devices, is projected to reach nearly 80 zettabytes by 2025. A single factory equipped with IoT devices can generate hundreds of terabytes of data daily. Traditional computing methods fall short when dealing with such vast datasets, but HPC efficiently manages the workload by distributing operations across multiple computers using advanced software and network capabilities.

Discover why HPC is crucial and explore its diverse applications in various industries. Harness the potential of HPC to tackle data-intensive challenges, accelerate scientific research, and drive innovation to new heights.

High-performance computing (HPC) revolutionises data analysis and simulation by tackling immense volumes of data that are beyond the capabilities of standard computers. This remarkable capability drives significant advancements across diverse fields, particularly in scientific research.

Harnessing the potential of HPC has resulted in ground-breaking discoveries, ranging from transformative cancer treatments to the development of life-saving COVID-19 vaccines. By unlocking the computational power of HPC, researchers can delve deeper into complex data sets, unravel intricate patterns, and accelerate the pace of scientific breakthroughs.

A cluster, comprised of multiple computers known as nodes, forms a powerful high-performance computer. Each node boasts an operating system equipped with a multi-core processor, storage, and networking capabilities for seamless communication between nodes. In a smaller cluster, you might find 16 nodes with 64 cores, offering four cores per processor. The amalgamation of these components, along with advanced networking capabilities, enables this high-performance computer to deliver significantly faster computational performance compared to a standard computer.

The usage of High-Performance Computing (HPC) spans across various industries at present. However, it is expected that in the future, nearly all industries will embrace HPC to effectively handle vast amounts of data. Notably, industries requiring swift analysis of extensive data sets have shown remarkable enthusiasm in adopting HPC.  Some of these industries include:

Scientific Research

Astronomy

Machine Learning

Cybersecurity

Genome Sequencing

Animation

Molecular Dynamics

Visual Effects

Financial Services

Financial Risk Modeling

Market Data Analysis

Product Development

Greenfield Design

Computational Chemistry

Seismic Imaging

Weather Forecasting

Autonomous Driving

In particular, there are four factors driving the use of HPC:

Processing Power

In simple terms, the processing capacity of a single processor falls short when it comes to handling large data volumes. High-Performance Computing (HPC) overcomes this limitation by harnessing the parallel processing power of multiple interconnected centres. In this context, key concepts include:

For instance, a small HPC cluster might comprise 16 nodes, each equipped with four cores, resulting in a total of 64 cores operating simultaneously.

Contemporary HPC applications typically involve thousands of cores working in parallel to expedite processes. Infrastructure-as-a-service (IaaS) providers offer users the flexibility to access a large number of nodes on-demand, reducing the workload when necessary. This pay-as-you-go approach eliminates the need for substantial capital expenditure (CAPEX) on infrastructure development. Additionally, IaaS users often have the option to customize node configurations for specific applications.

Operating System

Operating systems act as vital interfaces between hardware and software components in High-Performance Computing (HPC) setups. The HPC environment predominantly relies on two major operating systems: Linux and Windows.

Linux takes centre stage in HPC, offering robust features and extensive support for parallel processing. Its flexibility and customizability make it a popular choice for meeting the demanding computational needs of HPC applications. Linux has become the go-to operating system for HPC, enabling efficient resource utilization and scalability.

Windows, on the other hand, finds its role in HPC environments when specific Windows-based applications are required. While not as commonly used as Linux in HPC, Windows serves as a viable option for scenarios where compatibility with Windows-specific software is paramount.

Network

High-Performance Computing (HPC) relies on a robust network infrastructure to connect computing hardware, storage, and users. HPC networks are purpose-built to handle substantial data bandwidths while ensuring low latency for efficient data transfers. Cluster managers, management services, and schedulers play a crucial role in facilitating data transmissions and managing clusters.

Cluster managers take charge of distributing workloads across diverse computational resources, including CPUs, FPGAs, GPUs, and disk drives. To ensure seamless resource management, these components must be interconnected within a unified network. When leveraging Infrastructure-as-a-Service (IaaS) providers, users benefit from automatic provisioning of essential infrastructure management facilities, simplifying the overall management process.

By embracing IaaS services, users can focus on their specific computational needs without the complexities of infrastructure management. This streamlined approach to HPC enhances productivity and efficiency, ultimately driving optimal results.

Storage

In High-Performance Computing (HPC), data is stored in a large data repository to facilitate processing. Given that the data can take various forms—structured, semi-structured, and unstructured—different types of databases may be required to accommodate the data.

Initially, raw data is stored in a data lake, which poses challenges for processing since it lacks a specific purpose. To address this, processed and refined data is stored in data warehouses, tailored to its intended use.

Despite its significance, storage is often overlooked in many HPC use cases. HPC comes into play when processing vast quantities of data in parallel, and the performance hinges on the seamless coordination of all architectural components. Traditional legacy storage solutions may prove inadequate, causing bottlenecks and impeding performance. To ensure optimal performance, HPC architectures often employ unified fast file and object (UFFO) storage solutions capable of keeping pace with the processing power of the setup.