What Is High Performance Computing (HPC)?

High performance computing (HPC) is based on parallel processing of complex computational operations. An HPC system divides workloads into smaller tasks and assigns them to multiple resources for simultaneous processing. These parallel computing capabilities enable HPC clusters to execute large workloads faster and more efficiently than a traditional compute model.

HPC systems can be designed to either scale up or scale out. Scale-up designs keep a job within a single system but break it up so that several individual processor cores can perform the work. The goal of a scale-up design is to maximize the use of an individual server. Scale-out designs also split a job into manageable parts that are distributed across multiple servers.

Why Is High Performance Computing Important?

High performance computing is not new. HPC workstations and supercomputers have long played an integral role in academic research, solving complex problems and spurring discoveries and innovations.

Scientists, engineers, and researchers rely on HPC for a wide variety of use cases, including weather forecasting, oil and gas exploration, physics, quantum mechanics, and other areas in academic research and commercial applications.

HPC’s parallel computing capabilities can greatly accelerate iterative processes compared to traditional computing. For example, HPC can reduce the run time to train deep learning models from days to hours. As AI and big data applications gain popularity and advanced compute resources become more accessible and affordable, HPC is being deployed to solve an increasingly broad range of challenges, enabling widespread innovation.

How Does HPC Work?

While HPC can be run on a single node, its real power comes from connecting multiple HPC nodes into a cluster for supercomputing and parallel computing. HPC clusters can compute extreme-scale simulations, AI inferencing, and data analyses that may not be feasible on a single system.

Modern supercomputers are large-scale HPC clusters made up of CPUs, accelerators, high-performance communication fabric, and sophisticated memory and storage, all working together across nodes to prevent bottlenecks and deliver the best performance.

HPC platform software libraries, optimized frameworks for big data and deep learning, and other software tools help to improve the design and effectiveness of HPC clusters.

What Are HPC Clusters?

An HPC cluster is a combination of separate servers, called nodes, that act as a unit for parallel computing. HPC clusters are interconnected over a fast network, and the distributed processing framework is coordinated via software. HPC clusters can scale to handle massive amounts of data and highly complex operations at high speeds.

Benefits of High Performance Computing

By performing computationally intensive operations across shared resources, HPC can achieve results faster and at a lower cost compared to traditional computing methods. In many cases, a traditional computer system would take an impractical or infeasible amount of time to solve a complex calculation or simulation or to train an especially complex AI model. The parallel nature of HPC enables efficiencies that can save hours or entire days of processing.

With the increased availability of scalable, high-performance processors and high-speed, high-capacity memory, storage, and networking, HPC technologies have become more accessible. As a result, HPC is increasingly used to solve complex problems, analyze massive datasets, and devise innovative solutions in government and commercial settings as well as in academia.

Cloud-based resources can also help make HPC more affordable. Scientists and engineers can run HPC workloads on their on-premises infrastructure or scale up and out in the cloud to help reduce the need for capital investment.

Challenges of High Performance Computing

HPC systems offer compelling benefits, but they can pose unique challenges as well. Because HPC is designed to handle complex problems, the systems themselves are often large, complex, and expensive. As HPC systems scale up to include hundreds or even thousands of processor cores, they consume tremendous energy and demand robust cooling, resulting in high operating costs. Additionally, it can be challenging and costly to retain a staff of qualified HPC experts to set up and run the system. In some cases, migrating key HPC processes to the cloud can help to reduce costs.

Security challenges are also heightened due to the complexity of HPC systems and the interconnected nature of parallel operations. HPC applications often rely on large datasets, including sensitive data, making them attractive targets for cybercrime and cyber espionage. HPC systems may also be shared among large groups of users, adding to the systems’ vulnerabilities. Stringent cybersecurity and data governance processes must include access control so that unauthorized users or malicious code cannot be introduced into the system.

Examples of High Performance Computing

Research labs, governments, and businesses increasingly rely on HPC for simulation and modeling in diverse applications, including traffic safety, autonomous driving, product design and manufacturing, weather forecasting, seismic data analysis, and energy production. HPC systems also contribute to advances in precision medicine, financial risk assessment, fraud detection, computational fluid dynamics, and other areas.

HPC and AI

HPC AI provides the parallel computing infrastructure to power advanced AI algorithms, enabling researchers and engineers to push the boundaries of AI and deep learning applications.

HPC in Financial Services

HPC can help streamline the deployment of AI in financial services, as well as handle increasingly large and complex data sets to support near-real-time market analysis and options pricing, transaction monitoring, and fraud detection.

HPC in the Automotive Industry

HPC systems support computer-aided design and engineering as well as simulation and testing for new vehicle models. The ongoing development of self-driving vehicles also relies on HPC systems to iteratively train AI models.

HPC in Healthcare and Life Sciences

HPC and AI technologies are used to accelerate and simplify genomic analysis to support precision medicine, as well as molecular dynamics simulations for discovering and testing new biopharmaceutical treatments.

HPC in Government

Public-sector agencies, universities, and research labs use HPC to accelerate discovery, automation, and data-informed decision-making.

HPC in Cybersecurity

HPC powers AI-enabled enhancements to cybersecurity solutions that help protect organizations, their systems, users, and data against increasingly sophisticated cyberattacks.

The Future of High Performance Computing

As HPC hardware and software continue to become more attainable and widespread in the data center and in the cloud, HPC technologies can be expected to drive innovation and productivity for businesses and government agencies of all sizes. HPC supercomputers are also on the verge of surpassing exascale limits, increasing their capacity to solve even more complex challenges. In the future, HPC systems may leverage quantum computing to achieve unprecedented processing power, although this technology is still in a highly experimental phase. As the capacity of HPC processing continues to expand, so too will the ability of systems to tackle our most complex engineering, scientific, and AI-related challenges.