High-Performance Computing (HPC)

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What Does High-Performance Computing (HPC) Mean?

High-performance computing (HPC) is the use of supercomputers and parallel processing techniques to solve complex computational problems that are too large or too complex to be solved by traditional computing methods.


HPC systems have the ability to deliver sustained performance through the concurrent use of computing resources. They are used in a wide range of fields, including science, engineering, medicine, finance and meteorology.

The terms high-performance computing and supercomputing are sometimes used interchangeably.

Techopedia Explains High-Performance Computing (HPC)

High performance computing systems consist of a large number of processors that work together in parallel to perform computations. They require large amounts of memory and fast communication links between processors organized in different topologies.

HPC Use Cases

HPC systems are used for a number of different use cases, including:

  • Running digital twins and other large-scale simulations.
  • Modeling complex weather patterns.
  • Performing data-intensive scientific research and discovery.
  • Running large-scale data analytics and artificial intelligence (AI) workloads.
  • Optimizing the design of complex systems, such as aircraft engines and wind turbines.
  • Simulating the behavior of molecules in chemical reactions.

In general, the choice of topology for each use case depends on the the number of nodes required for high performance, the level of parallelism required and the need for high-speed interconnects.

HPC Topologies

There are several topologies that are commonly used for high performance computing, including:

  1. Clusters: This topology connects multiple independent computers (nodes) together to work as a single system. Clusters can be used to increase the computational power and parallel processing capability of an HPC system.
  2. Grid computing: This topology connects multiple independent clusters together to work as a single system. Grid computing allows for the sharing of resources and data across multiple locations.
  3. Hybrid: This topology combines elements of both cluster and grid computing to create a more powerful and flexible HPC system. In a hybrid topology, nodes within a cluster can also be connected to other clusters and resources through a grid.
  4. Fat-tree: This topology is a non-blocking, hierarchical network that connects switches in a tree-like structure. Fat-tree is used by supercomputers to provide high bandwidth and low latency.
  5. Torus: This topology is a ring-based topology that has multiple connections between nodes. This topology can be used in supercomputing to provide high-speed communication between nodes, which in turn, helps to improve performance.
  6. Dragonfly: This topology is a non-blocking hierarchical network topology. It organizes switches and hosts in clusters and interconnects the clusters using a global network. This topology is used in HPC because of its scalability and low-latency communication.

Future of High-Performance Computing

The future of high performance computing (HPC) is expected to involve several key developments and trends, including:

Continued growth in computational power and performance.
As technology continues to advance, HPC systems will become increasingly powerful, allowing for the simulation and analysis of larger and more complex problems. This will be achieved by using advanced processors that have faster memory, more storage and more efficient cooling.

Increased use of artificial intelligence and machine learning.
AI and machine learning are expected to play a major role in HPC in the future. Machine learning models will be able to take advantage of the vast amount of data generated by HPC simulations and experiments to automatically extract insights and make predictions.

Increased use of cloud and edge computing.
Cloud-based HPC systems will enable organizations to access HPC resources on-demand and scale resources up or down as needed. Edge computing will be important because it provides localized computational power and acts as an intermediary between devices and cloud infrastructure.

Increased use of software and hardware accelerators.
Software and hardware accelerators will play an important role in HPC, for example, graphics processing units (GPUs) and field-programmable gate arrays (FPGAs) are already being used in HPC systems to accelerate specific types of computations.

Increased use of open-source software.
Open-source software allows for more collaboration and innovation across the HPC community, so it will continue to grow in importance.

Greater focus on energy efficiency.
As HPC systems continue to get larger and more powerful, energy efficiency will become increasingly important.

Greater use of quantum computing in HPC.
Research on quantum computing is accelerating at a rapid pace, and it is expected that quantum computing will start to play a significant role in HPC in the near future — especially in areas such as machine learning and simulation.


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Margaret jest nagradzaną technical writerką, nauczycielką i wykładowczynią. Jest znana z tego, że potrafi w prostych słowach pzybliżyć złożone pojęcia techniczne słuchaczom ze świata biznesu. Od dwudziestu lat jej definicje pojęć z dziedziny IT są publikowane przez Que w encyklopedii terminów technologicznych, a także cytowane w artykułach ukazujących się w New York Times, w magazynie Time, USA Today, ZDNet, a także w magazynach PC i Discovery. Margaret dołączyła do zespołu Techopedii w roku 2011. Margaret lubi pomagać znaleźć wspólny język specjalistom ze świata biznesu i IT. W swojej pracy, jak sama mówi, buduje mosty między tymi dwiema domenami, w ten…