An Introduction to HPC for Big Data

High-Performance Computing (HPC) has technically been around since the 1970s when the first supercomputers were created. Since then, HPC systems have advanced significantly. These systems have also become much more accessible and can even be created in-house

Many organizations are taking advantage of the increased accessibility of HPC systems for computationally intensive workloads. For example, processing and analyzing terabytes or petabytes of big data. In this article, you’ll learn what HPC is and how to use it with big data.

What Is HPC?

HPC is computing performed with aggregated resources, such as clusters of servers or workstations. In HPC, devices, known as nodes, work together to perform parallel processing. Parallel processing uses clustered processors to supply multiple parts of a single task.

Unlike traditional computers, which only have one Central Processing Unit (CPU), each HPC node has two or more CPUs, and each CPU has multiple cores. Cores are the actual processors within a CPU. Each HPC node also has its own memory and storage. HPC systems are typically made of between 16 and 64 nodes.

In HPC systems, each node often uses a combination of CPUs and Graphical Processing Units (GPUs). GPUs serve as co-processors, designed for specific tasks. GPU specialization enables HPC systems to more quickly process portions of an application or workload.

HPC use cases include:

  • Research labs—used for medical research, meteorological predictions, interpreting astronomical data, or training Machine Learning (ML) algorithms.
  • Media and entertainment companies—used to render special effects, edit films, or live stream events.
  • Oil and gas companies—used to identify sources of oil or gas and increase well productivity.
  • Financial services—used to automate trading, track stock trends, or identify fraud.

HPC is primarily accomplished with Linux systems although Windows systems can also be used. However, HPC systems can be hosted on-premise or in the cloud. All major cloud vendors offer services for managing and orchestrating these workloads, such as Azure’s hybrid HPC integration.

3 Benefits of HPC

1. Cost

HPC systems are designed to provide an affordable and functional alternative to supercomputers. Most organizations cannot afford the high price that supercomputers entail, some of which cost up to $20 million. HPC systems can provide some semblance of the speed and compute power at a fraction of the cost of supercomputers.

2. Resources

HPC systems enable you to process significantly larger amounts of data than you could on a traditional system. HPC provides vastly more memory and storage than any traditional system. It also enables you to distribute your available resources across workloads, maximizing resource efficiency. 

Greater resources enable you to perform multiple processes in the same system. For example, data preparation and analysis. This multipurpose use eliminates the need to transfer data between systems, saving considerable time and bandwidth.

3. Cloud-Related

When used in the cloud, HPC can be achieved with the added benefits of increased scalability and availability. Cloud implementations can also help significantly reduce your costs since you no longer need to manage or maintain your infrastructure.

HPC and Big Data

For big data to be meaningful, you must process and analyze it. While you can use traditional computers for these tasks, doing so would create an exponential backlog. The only way to successfully manage the amount of data coming in is with higher performance and speed.

HPC systems can help you run your workloads in a functional amount of time. Some HPC systems can even provide real-time analyses and processing. This speed ensures that your analyses are timely, hopefully making results more relevant. 

When you can run your workloads more efficiently, you gain the ability to process significantly more complex data. You also gain the ability to easily work with a wider range of data sources. These sources are more easily available because you do not need to worry as much about extraction or transformation time. In combination, more complex and broader data can help you create better predictive models make more effective decisions.

There is a wide range of use cases for HPC and big data, including:

  • Automation of big data analyses and visualization—HPC can automate a greater number of processes simultaneously. HPC enables you to use complex scripting to automate analyses with less concern for code optimization. It also enables the creation of rich visualizations, which can take substantial processing power to generate.
  • Training machine learning models—HPC can train on problems that were previously too complex to be feasible. HPC speeds processing time and enables you to train models using a much higher number of iterations. More iterations in training produce more reliable models, particularly for deep learning.
  • Generation of simulated data—HPC can generate realistic, highly-detailed datasets. These sets are useful for training ML and AI models or in place of personally identifiable information. Generated sets can be created to maintain the statistical significance of the base set with anonymized information.
  • Processing IoT data streams—HPC can summarize and categorize data in real-time. This upfront processing makes data more intelligible to human users and speeds higher-level analysis. Real-time processing provides faster detection and enables faster response for IoT devices.
  • Business intelligence analyses—HPC can perform complex correlations necessary for analyses. Since HPC systems enable you to process and analyze data in one system, you can gain results faster. Faster results enable you to make more competitive decisions and possibly beat others to innovation.


Big data is growing exponentially. By 2025, it’s estimated that 463 exabytes will be created every day. Processing, analyzing, and applying this much data demands significant resources, more than traditional computers can provide. For this reason, HPC and big data make an obvious pair; one that is gaining attention and driving innovation.

Tao is a passionate software engineer who works in a leading big data analysis company in Silicon Valley. Previously Tao has worked in big IT companies such as IBM and Cisco. Tao has a MS degree in Computer Science from University of McGill and many years of experience as a teaching assistant for various computer science classes.

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