High-performance computing (HPC) is the practice of using parallel data processing to improve computing performance and perform complex calculations. HPC achieves these goals by aggregating computing power, so even advanced applications can run efficiently, reliably and quickly as per user needs and expectations. It thus delivers much higher power and better performance than traditional computers, workstations and servers.
In the modern world, groundbreaking discoveries and inventions can only happen with technology, data and advanced computing. As cutting-edge technologies like artificial intelligence (AI), machine learning (ML) and IoT evolve, they require huge amounts of data. They also need high-performance computing because HPC systems can perform quadrillions of calculations per second, compared to regular laptops or desktops that can perform at most 3 billion calculations per second (with a 3 GHz processor).
HPC is specifically needed for these reasons:
HPC helps overcome numerous computational barriers that conventional PCs and processors typically face. The benefits of HPC are many and include the following.
HPC is mainly about lightning-fast processing, which means HPC systems can perform massive amounts of calculations very quickly. In comparison, regular processors and computing systems would take longer -- days, weeks or even months -- to perform these same calculations.
HPC systems typically use the latest CPUs and GPUs, as well as low-latency networking fabrics and block storage devices, to improve processing speeds and computing performance.
Because an HPC system can process faster, applications can run faster and yield answers quickly, saving time or money. Moreover, many such systems are available in "pay as you go" modes and can scale up or down as needed, further improving their cost-effectiveness.
Many modern-day applications require a lot of physical testing before they can be released for public or commercial use. Self-driven vehicles are one example. Application researchers, developers and testers can create powerful simulations using HPC systems, thus minimizing or even eliminating the need for expensive or repeated physical tests.
Most HPC systems have three main components or resources:
In an HPC architecture, multiple servers -- generally hundreds or thousands -- form a network or cluster. Each server is a node; and in each cluster, the nodes work in parallel to boost processing speeds and ensure HPC. Clusters are often created and removed automatically in the cloud to save time and reduce costs.
Several software programs and algorithms run simultaneously on the cluster to support multiple HPC applications. Further, the cluster is networked to the storage components to capture and store the output of these programs.
It's important that these various components operate at similar speeds or performance levels. If they can't keep pace with each other, HPC cannot happen and the entire system will fail. For instance, the servers must ingest and process data efficiently from the storage components, while these components should be able to feed data quickly to servers to support HPC. Similarly, networking components should support seamless high-speed data transportation between the other components.
HPC systems can run different types of workloads. Two popular types are parallel and tightly coupled workloads.
In parallel workloads, computational problems are divided into small, independent tasks that can run in parallel at very high speeds. Often, these workloads don't communicate with each other. Examples of such workloads include risk simulations, logistics simulations, contextual search and molecular modeling.
When workloads are divided into smaller tasks and communicate continuously with each other as they perform their processing, they are said to be tightly coupled. This usually happens with workloads across different nodes in a cluster. Some common examples of tightly coupled workloads are automobile collision emulations, geospatial simulations, weather forecast modeling and traffic management.
Both kinds of workloads require high processing speeds and accurate output, for which HPC is required.
HPC is used in many real-life scenarios to solve complex problems in science, business and engineering. Some academic institutions also use HPC systems. Some government agencies, particularly the military, rely on HPC for complex applications. As the demand for processing power and speed grows for real-world applications, HPC will likely interest businesses of all sizes, particularly for transaction processing, and data warehouses.
HPC systems are also used in many other industries, including but not limited to the following:
A supercomputer is one of the best-known examples of HPC, in which one large computer is made up of many computers and processors that work together to achieve parallel processing and high performance.
Although HPC has progressed massively in recent decades, some barriers remain that hinder widespread HPC adoption:
Many of these barriers exist due to the on-premises deployment of HPC systems. Cloud technologies that are specifically architected for HPC workloads -- and offer extensive capacity and a "pay as you go" option -- could be a feasible solution to these challenges.
See a comprehensive guide to HPC in the data center, how HPC services bring computational power to more organizations and ways to maximize HPC applications' performance. Also, explore how to implement GPUs for high-performance computing and learn about high-performance computing as a service.
28 Dec 2021