High-Performance Computing (HPC) And Artificial Intelligence (AI)

What is High-Performance Computing (HPC)?

A desktop or a laptop with a 3 GHz processor can perform about 3 billion (10^9)calculations per second. Although this is much faster than what a human can do, it pales in comparison to High-Performance computing solutions, which can perform quadrillions (10^15) of calculations per second.

High-Performance Computing (HPC) refers to the aggregation of computing power in ways that provide far more processing power than traditional computers and servers. It involves the ability to perform large-scale calculations to solve complex problems that require processing large amounts of data.

A key principle of HPC is the ability to run code massively in parallel to benefit from large-scale runtime acceleration. HPC systems can sometimes reach very large sizes because applications are accelerated when processes are parallelized, that is when more computing cores are added. A typical HPC capability is around 100,000 cores. 

Supercomputers are one of the best-known types of HPC solutions. A supercomputer contains thousands of computing nodes that work together to perform one or more tasks. It’s like connecting thousands of PCs and combining their computing power to process tasks faster.

Most HPC applications are complex tasks that require processors to exchange their results. HPC systems require extremely fast storage and communication systems with low latency and high bandwidth (> 100 Gb/s) between processors and associated storage.

How does HPC work?

A standard computer solves problems by dividing the workload into a series of tasks and executing it one after the other on the same processor. On the contrary, a High-Performance Computing system leverages massive parallel computing, computer clusters, and high-performance components.

Parallel Computing

In Parallel Computing, multiple tasks run simultaneously on multiple computer processors or servers. In HPC, parallel computing is done using millions of processors or processor cores.

Computer Clusters

A High-Performance Computing cluster consists of numerous high-speed servers networked together with a centralized scheduler managing parallel workloads. The Computers, called nodes, use high-performance multi-core CPUs or, more likely Graphical Processing Units (GPUs), which are well-suited for rigorous mathematical computations, graphics-intensive tasks, and machine-learning models. A single HPC cluster can contain over 100,000 such nodes.

High-Performance Components

The other components in an HPC cluster like networking, storage, memory, and file system are high-speed and low-latency components that can optimize and improve the performance of the cluster.

High-Performance Computing & AI

AI can be used in High-Performance Computing to augment the analysis of datasets and produce faster results at the same accuracy level. The implementation of HPC and AI requires similar architectures – both achieve results by processing large data sets that typically grow in size using high computing and storage capacity, large bandwidth, and high-bandwidth fabrics. The following HPC use cases can benefit from AI capabilities.

  • Financial analysis like risk and fraud detection, manufacturing, and logistics.
  • Astrophysics and astronomy.
  • Climate Science and meteorology.
  • Earth Sciences.
  • Computer-aided design (CAD), computational fluid dynamics (CFD), and computer-aided engineering (CAE).
  • Scientific visualization and simulation.

How HPC can help build better AI applications

  • Massive Parallel Computing speeds up calculations significantly, allowing the processing of large datasets in less time.
  • More storage and memory make it easy to process large volumes of data, thereby increasing the accuracy of AI models.
  • Graphical Processing Units (GPUs) can be used to process AI algorithms more effectively.
  • HPC as a service can be accessed on the cloud; hence, upfront costs can be reduced.

Integration of AI and HPC

The integration of HPC and AI, however, requires a few changes to workload tools and management. Following are a few ways High-Performance Computing is evolving to address the challenges associated with integrating AI and HPC.

Programming Languages

HPC programs are generally written in languages like C, C++, and Fortran, and the extensions and libraries of these languages support the processes of HPC. AI, on the other hand, relies on python and Julia.

For HPC and AI to use the same infrastructure, the software and interface should be compatible. In most cases, AI frameworks and languages are overlaid on existing software to allow both sets of programmers to continue using their current tool without migrating to a different language.

Virtualization and Containers

Containers allow the developers to adapt their infrastructure to the changing work needs and enable them to deploy the same consistently. Containers allow the Julia and Python applications to be more scalable.

With containers, teams can create HPC configurations that are quick and easy to deploy without time-consuming configuration.

Increased Memory

Big data plays a significant role in AI, and the data sets are ever-increasing. The collection and processing of these datasets require a large amount of memory to maintain the efficiency and speed that HPC can provide. HPC systems tackle this problem with technologies that support a large amount of persistent and ephemeral RAM.

HPC use cases/applications


HPC can manage and scale large and complex datasets and is useful for healthcare computing operations.

  • Researchers at the University of Texas scanned huge amounts of data for correlation between the cancer patient’s genome and the composition of their tumors, which the university uses for further cancer research. The university’s high-powered cluster is even used for drug development and diagnosis.
  •  The Rady Children’s Institute, in 2018, set the Guinness World Record for the fastest genome sequencing in mere 19.5 hours, using an end-to-end HPC tool called DRAGEN. 


To boost a machine’s performance, engineers first build and test a rather expensive prototype. To work around this issue, massive computer simulations mimic real-world variables like wind, heat, gravity, and even chaos.

  • Joris Poort and Adam McKenzie used HPC to optimize the weight of the 787 Dreamliner. They shaved almost 200 pounds off the aircraft, saving Boeing more than $200 million.
  • Using Lawrence Livermore Laboratory‘s supercomputer, researchers created a device that optimizes the fuel use of trucks and saves up to $5000 per truck per year.
  • The complex Machine Learning algorithms that run Autonomous Vehicles are generally trained on HPC technology.


  • Solar flares can break up radio communications and even disrupt GPS navigation. Researchers at NASA used HPC to train a deep-learning algorithm to predict solar flares based on the photos of the sun from an orbiting observatory.
  • Simulia, which is an HPC-powered simulation software designed by Dassault Systèmes, uses fluid dynamics to simulate the conditions of aircraft flights.

Urban Planning

  • The city of Santiago, Chile, is infamous for smog, and its people have to deal with asthma, cancer, and other lung-related issues. A model was built on the University of Iowa’s HPC cluster, capable of forecasting smog levels 48 hours in advance so that necessary precautions could be taken.
  • The supercomputer at the National University of Defense Technology, Tianjin, China can optimize construction projects. It can identify the ideal building materials, manage their transportation to the construction site, and even ensure that the crew uses the power grid efficiently.
  • The engineering firm RWDI uses HPC for water and energy modeling, checking the structural soundness of buildings, and other technological assessments.

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I am a Civil Engineering Graduate (2022) from Jamia Millia Islamia, New Delhi, and I have a keen interest in Data Science, especially Neural Networks and their application in various areas.

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