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

What is High Performance Computing (HPC)?

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

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

A key tenet of HPC is the ability to run code massively concurrently to take advantage of 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 capacity is around 100,000 cores.

Supercomputers are one of the most well-known types of HPC solutions. A supercomputer consists of thousands of computing nodes that work together to perform one or more tasks. It’s like connecting thousands of personal computers 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 the associated storage.

How does HPC work?

A standard computer solves problems by dividing the workload into a series of tasks and executing them one after the other on the same processor. Conversely, a High Performance Computing system leverages massively 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 many high-speed servers networked together with a centralized scheduler that manages parallel workloads. The Computers, called nodes, use high-performance multi-core CPUs or, more likely, Graphics Processing Units (GPUs), which are well suited for intensive mathematical calculations, graphics tasks and machine learning models. A single HPC cluster can have over 100,000 such nodes.

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High Performance Components

The other components in an HPC cluster such as 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 data sets and produce faster results at the same level of accuracy. Implementing HPC and AI requires similar architectures – results are achieved by processing large data sets that typically grow in size using high compute and storage capacity, large bandwidth, and high-bandwidth fabrics. The following HPC use cases can benefit from AI capabilities.

  • Financial analysis such as 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

  • Massively Parallel Computing significantly speeds up the calculations, allowing large data sets to be processed in less time.
  • More storage and memory makes it easy to process large amounts of data, which increases the accuracy of AI models.
  • Graphics Processing Units (GPUs) can be used to process AI algorithms more efficiently.
  • HPC can be accessed as a service on the cloud; therefore, initial costs can be reduced.

Integration of AI and HPC

However, the integration of HPC and AI requires some changes in tools and workload management. Here are some ways High Performance Computing is emerging to address the challenges of integrating AI and HPC.

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Programming Languages

HPC programs are generally written in languages ​​such as C, C++, and Fortran, and the extensions and libraries of these languages ​​support HPC processes. On the other hand, AI depends 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 ​​were overlaid on existing software to allow both sets of programmers to continue using their current tool without switching to another language.

Virtualization and Containers

Containers allow developers to adapt their infrastructure to the changing work requirements and enable them to deploy the same thing consistently. Containers allow 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 datasets are constantly growing. Collecting and processing these data sets requires a large amount of memory to maintain the efficiency and speed that the HPC can provide. HPC systems address this problem with technologies that support large amounts of persistent and short-term RAM.

HPC use cases/applications

Health care

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

  • Researchers at the University of Texas scanned huge amounts of data for a 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 19.5 hours, using an end-to-end HPC tool called DRAGEN.


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

  • Joris Poort and Adam McKenzie used HPC to optimize the weight of the 787 Dreamliner. They saved nearly 200 pounds from the aircraft, saving Boeing more than $200 million.
  • Using a Lawrence Livermore Laboratory supercomputer, researchers created a device that maximizes truck fuel usage and saves up to $5000 per truck per year.
  • HPC technology is generally trained on the complex Machine Learning algorithms that run Autonomous Vehicles.
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  • Solar flares can disrupt radio communications and even interfere with GPS navigation. Researchers at NASA used HPC to train a deep learning algorithm to predict solar flares based on photos of the sun from an orbiting observatory.
  • Simulia, an HPC-powered simulation software designed by Dassault Systèmes, uses fluid dynamics to simulate aircraft flight conditions.

Urban Planning

  • The city of Santiago, Chile, is famous for its 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, which can predict smog levels 48 hours in advance so that the 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 transport to the construction site, and even ensure that the crew uses the power grid efficiently.
  • Engineering firm RWDI uses HPC for water and energy modeling, to check the structural soundness of buildings, and other technology assessments.

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I am a Civil Engineering Graduate (2022) from Jamia Millia Islamia, New Delhi, and I am very interested in Data Science, especially Neural Networks and their application in various fields.


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