High Performance Computing (HPC)

High performance computing is used to handle large volumes of data and complex computing tasks in parallel. Typical areas of application include economics, science, simulations, and business intelligence. But which HPC methods are there and how do they work?

High Performance Computing, or HPC for short, is not so much a clearly defined technology, but rather a set of procedures that use or make available the performance and memory capacity of ordinary computers. There are no fixed criteria for HPC, as HPC changes with the times and adapts to new computing technologies. In general, it can be said that HPC solutions are used for complex computing operations with very large amounts of data or for the analysis, calculation, and simulation of systems and models.

On the one hand, HPC processes can be used on individual, very powerful computers. More often, however, HPC is found in the form of HPC nodes in supercomputers, known as HPC clusters. Supercomputers are capable of parallel, high-performance computing with multiple aggregated resources. Early HPC supercomputers were developed by current Intel partner Cray. Today, supercomputers are much more powerful, since complex hardware and software architectures are linked via nodes and performance capabilities are combined.

When data volumes overwhelm the performance of conventional computers, HPC environments are called for. As a form of distributed computing, HPC uses the aggregated performance of coupled computers within a system or the aggregated performance of hardware and software environments and servers. Modern HPC clusters and architectures for high-performance computing are composed of CPUs, work and data memories, accelerators, and HPC fabrics. Applications, measurements, calculations, and simulations on a large scale can be distributed to parallel processes thanks to HPC. The task is distributed via special computing software.

Two main approaches are found in High Performance Computing applications:

1. Scale up: HPC technologies use a complex architecture of hardware and software to distribute tasks across available resources. The distribution to parallel computing processes takes place within a system or server. When scaling up, the performance potential is high, but is partial to the system’s limitations.
2. Scale out: In scale-out architectures, individual computers, server systems, and storage capacities are connected to form nodes and HPC clusters using clustering.

In theory, a system’s individual coupled computers can suffice for scale-up HPC requirements. In practice, however, the scale-up approach hardly proves efficient for very large applications. Only the combination of computing units and server systems accumulates required capacities and scales the required performance as needed. The compilation, distribution, or separation of HPC clusters is usually done via a single server system with merged computing units or via a HPC provider’s automated cloud computing.

In contrast to local or supra-regional standalone systems that run HPC applications via a server, HPC via cloud computing offers significantly more capacity and scalability. HPC providers provide an IT environment consisting of servers and computer systems that can be booked on demand. Access is flexible and fast. In addition, the cloud services offered by HPC providers are almost unlimitedly scalable and guarantee a reliable cloud infrastructure for HPC processes. The on-premises model with individual systems, consisting of one or more servers and complex IT infrastructure, offers more independence but is dependent on higher investments and upgrades.

Just like the fluid definition of HPC, the application of HPC can be found almost everywhere where complex computing processes take place. HPC can be used locally on-premises, via the cloud, or even as a hybrid model. Industries that rely on or regularly use HPC include:

• Genomics: For DNA sequencing, lineage studies, and drug analysis
• Medicine: Drug research, vaccine production, therapy research
• Industrial sector: Simulations and models, e.g. artificial intelligence, machine learning, autonomous driving, or process optimisation
• Aerospace: Simulations on aerodynamics
• Finance: In the context of financial technology to perform risk analysis, fraud detection, business analysis, or financial modelling
• Entertainment: Special effects, animation, transfer of files
• Meteorology and climatology: Weather forecasting, climate models, disaster forecasts, and warnings
• Particle physics: Calculations and simulations of quantum mechanics/physics
• Quantum chemistry: Quantum chemical calculations

HPC has long been more than a reliable tool for solving complex tasks and problems in the sciences. Today, companies and institutions from a wide variety of fields also rely on powerful HPC technology.

The advantages of HPC include:

• Cost savings: HPC from the cloud allows larger and complex workloads to be processed even by smaller companies. Booking HPC services via HPC providers ensures transparent cost control.
• Greater performance, faster: Complex and time-consuming tasks can be completed faster with more computing capacity thanks to HPC architectures consisting of CPUs, server systems, and technologies such as Remote Direct Memory Access.
• Process optimisation: Models and simulations can be used to make physical tests and trial phases more efficient, prevent failures and defects, for example in the industrial sector or in financial technology, and optimise process flows through intelligent automation.
• Knowledge gain: In research, HPC enables the evaluation of enormous amounts of data and promotes innovation, forecasting, and knowledge.