High-Performance Computing

What is High-Performance Computing?

High-performance computing (HPC) describes the aggregation of the computing power of multiple stand-alone computers to perform complex operations and process large volumes of data at high speeds. When you combine the computing power of many individual computers, you get significantly higher performance than you’d derive from one workstation. Industries like the hard sciences, engineering, and commerce/business use high-performance computing power to solve complex, data-heavy problems.

With the evolution of artificial intelligence (AI), the Internet of things (IoT), and 3D imaging, the amount of data to compute is also rising exponentially. Reports show that the data generated globally has grown from about 33 zettabytes in 2018 to a projected 175 zettabytes by 2025 (1 zettabyte = 1 trillion gigabytes). This growth in data has created the need for much faster and efficient computing to cater to large data volumes. HPC offers a solution to this.

How HPC Works

There are three key constituents needed for HPC to work: computer servers, network, and storage. Servers connect in a cluster through the network to run programs and algorithms. The cluster also connects to the storage component for storing the output of these programs.

HPC can involve dozens to thousands computing nodes connected in the cluster to perform many complex tasks in parallel and within a short time. You can add or remove nodes from the cluster using automation tools, like Kubernetes, to improve efficiency and reduce cost.

All components of an HPC need to work well for efficient and smooth operation. For example, the cluster has to be able to process data at very high speeds. The storage component needs to collect and supply data to and from the servers just as quickly as the servers process it. And, the network needs to accommodate high-speed data movement between nodes of the cluster and the storage component.

Benefits of HPC

HPC has become a very integral part of research and innovation. Engineering, data science, process design, and research use it to solve complex problems and manipulate large chunks of data quickly and cost-effectively.

Improves Speed

A typical stand-alone computer system has a relatively low CPU capacity. Generally, this allows a computer to run only one model at a time, slowing down the whole process as a result. Using advanced technology with recent CPUs, GPUs, and RDMAs, HPC enables you to run multiple models or simulations simultaneously and performs calculations much more efficiently than before.

Reduces Costs

Improved speed translates into reduced time and money costs. Given that cloud-based HPC solutions are available, everyone can take advantage of them without owning the physical infrastructure. You pay for only what you use and scale as needed.

Supports Research and Innovation

The efficiency that HPC solutions provide facilitates groundbreaking research and helps to drive innovation in many industries. It reduces time to completion and allows researchers to quickly implement and scale their ideas into valuable products for a more significant number of people. Also, HPC enables scientists and engineers to focus on solving scientific problems instead of managing computer systems.

Where is HPC Useful?

You can apply HPC to solve computational problems that consist of simple independent tasks that can run in parallel with little or no communication between them, and whose results have little or no dependencies. Some examples include risk simulations, 3D video rendering, and some forms of password cracking.

You can also apply HPC to tightly coupled workloads where it’s possible to divide a significant problem into smaller interdependent tasks and coordinate them continuously. These workloads require a lot of computational power to ensure that none of the nodes fail during the computing process. This is because the failure of one node leads to the loss of the entire calculation or computation.


High-performance computing is very useful when simulating real-world scenarios using a computer. Using HPC drastically reduces the time and costs associated with repeated physical testing. Some simulation examples include crash testing for automobiles, autonomous driving, and airflow simulations over an airplane's wings. HPC allows you to perform several simulations until you achieve a satisfactory result before you do an actual physical test.

Health and Biotech

High-performance computing is particularly impactful in health services, where thousands or millions of tests and simulations are used to create the best formula. HPC facilitates the research and discovery of new drugs and vaccines, which enables scientists to invent novel treatments for diseases more quickly.

Oil and Gas

You can use HPC to test reservoir models and carry out spatial analyses to predict the location of valuable resources. Logistics and transportation also benefit from using HPC to calculate logistics planning for the physical transfer of materials. You can also use it for seismic processing and fluid flow simulations.


Media files used in the entertainment industry are usually large and require a lot of computing power to process. High-performance computing makes it possible to render special effects, create animations, and transcode large, complex media files.


The e-commerce space benefits from high-performance computing too. HPC can be used to analyze customer data, from which businesses can improve product recommendations and target ads. The insights gleaned using HPC ultimately lead to better customer service.

You can also use HPC to improve marketing efforts by personalizing website content. HPC can process demographics and other user data within seconds, allowing you to display different web content to different users.


In fintech, HPC helps to detect fraud. It supports financial modeling and high-frequency trading. You can also perform risk analysis and risk management faster and much better with HPC.

High-performance computing with Apache Ignite and GridGain

The GridGain in-memory computing platform supports distributed computing APIs for high-performance computing use cases. The compute APIs distribute resource-intensive tasks over a cluster of server nodes. This provides support for HPC and massively parallel processing. It also enables fast and precise decision-making using real-time analytics. The GridGain in-memory computing platform operates on the open-source Apache Ignite project, which uses a technique known as co-located processing.

Co-located processing enables you to do calculations on local data sets of cluster nodes. It avoids the shuffling of records over the network and removes the impact of network latency on application performance. This co-located technique works to improve the performance of calculations and processes that deal with large chunks of data and require intensive computing power.

GridGain and Apache Ignite Compute APIs divide tasks into jobs and send the jobs to specific nodes. Local CPUs execute these jobs and aggregate them to derive the result of the overall query. GridGain processes these jobs in parallel, resulting in a tremendous performance leap. In addition to this, GridGain allows you to scale up the cluster by using more powerful servers or scale out by adding more server nodes. Simultaneously scaling up and scaling out gives users the ability to create immensely powerful results with HPC.


In recent years with the development of AI, 3D imaging, and the prevalence of the Internet of things, the amount of data businesses are processing has grown exponentially. This growth in data quantity has introduced a need for more efficient and effective computing practices that cater to processing large quantities of data. This is where HPC comes in.

HPC makes life easier by coupling, simplifying, and solving computational problems, aiding research and development in science, health, and engineering, among other sectors. HPC is also useful in non-STEM industries including finance, retail, hospitality, media and entertainment, and coordination and transportation.

As leaders in the advancement of in-memory computing, GridGain and Apache Ignite allow you to navigate processing large volumes of data. Benefits of Apache Ignite’s computational APIs include broadcasting or executing tasks on specific nodes in a distributed cluster, and load-balancing to avoid overusing some nodes. Additionally, it supports fault-tolerant computations, eliminating the need to restart a calculation if something goes wrong.

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