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Today’s networks support extensive workloads in complex enterprise IT environment. With high-performance computing (HPC) and AI/deep learning applications, organizations are under pressure to meet increasing demands for faster compute cycles, more data transfer rates and excellent connectivity.
In addition, strict security measures require higher levels of encryption. And, as users rely on these systems to do more, they expect them to work seamlessly and efficiently. With increased complexity comes increased demand for bandwidth and throughput, which requires a network infrastructure that can keep up with today’s workloads. That’s why high-performance computing focuses on improving all aspects of system architecture and efficiency.
Also see: What is Software-Defined Networking
What is high-performance computing?
High-performance computing (HPC) is a term used to describe computer systems capable of performing complex calculations at exceptionally high speeds. HPC systems are often used for scientific research, engineering simulations and modeling, and data analytics.
The term high performance refers to both speed and efficiency. HPC systems are designed for tasks that require large amounts of computational power so that they can perform these tasks more quickly than other types of computers. They also consume less energy than traditional computers, making them better suited for use in remote locations or environments with limited access to electricity.
What is HPC in networking?
High-Performance Computing (HPC) in networking refers to a network infrastructure that can support high bandwidth, low latency and many concurrent connections. The idea behind HPC is to provide better performance and scalability for applications such as video streaming, online gaming, and content delivery networks. There are several ways of achieving HPC in networking, including software-defined storage solutions and virtualization technologies.
10 Networking trends in high-performance computing
You don’t have to look far to see how important networking is as an infrastructure component. With public and private clouds, software-defined networking (SDN), network function virtualization (NFV), and software-defined everything on the rise, it’s clear that networking is critical in high-performance computing (HPC) architectures.
1) Offloading
This trend is about moving processing workloads from servers to other devices with specialized hardware designed for specific computations, such as graphics processing units (GPUs) or field-programmable gate arrays (FPGAs). By offloading specific workloads to these types of hardware, you can speed up your applications while lowering your total cost of ownership (TCO) because you don’t need to buy as many servers.
2) Virtualization
As businesses become increasingly dependent on their IT infrastructure, they need a way to ensure availability even when physical systems fail. One way to do so is through virtualization, which allows multiple operating systems—and therefore multiple applications—to run simultaneously on one physical server. While virtualization isn’t a new technology, it has matured over time and now offers greater flexibility than ever before.
3) Accelerators
An accelerator is a hardware device used to speed up an application or process beyond what would be possible using only CPUs alone. Examples include GPUs, FPGAs, DSPs (digital signal processing) and ASICs (application-specific integrated circuits). These technologies work differently but accomplish similar goals: They enable companies to get more done in less time using less power and fewer system resources.
Also see: Best IoT Platforms for Device Management
4) Data storage access
Today’s data centers rely heavily on flash memory to improve performance and increase efficiency. Flash memory provides faster read/write speeds than traditional hard disk drives (HDDs), making it well suited for applications that require fast data retrievals, such as databases and high-performance computing clusters.
However, flash memory doesn’t last forever; its cells wear out after being written thousands of times, meaning eventually, they must be replaced. To address this issue, vendors have begun to develop a variety of new storage media, including phase-change memory (PCM), magnetoresistive RAM (MRAM) and resistive RAM (ReRAM).
5) Software-defined networks
SDN is a broad term encompassing several approaches to managing IT resources. But at the heart of most SDN strategies is separating applications and services from the underlying hardware and then automating how those resources are provisioned, configured and managed. This approach enables IT administrators to more easily add and remove resources from their networks and lets them more easily customize the network to meet changing business requirements.
6) Automation
With automation, IT professionals can use software to manage large network resources without human intervention. This trend will help organizations reduce costs by reducing labor expenses and increasing network reliability by eliminating human error from common tasks like configuring a new switch or router. In addition, automation can help IT departments more quickly scale their networks as business needs change, enabling them to provide better service with fewer resources.
The future of networking will be driven by automation, which can help IT departments more quickly scale their networks as business needs change, enabling them to provide better service with fewer resources. Automation also plays a key role in the other trends, such as virtualization and SDN. Automation can help IT departments more quickly scale their networks as business needs change, enabling them to provide better service with fewer resources.
Also see: Best Cloud Networking Solutions
7) Artificial intelligence (AI) and machine learning (ML)
Artificial intelligence (AI) and machine learning (ML) in HPC are becoming increasingly important. Both are related to the concept of automated decision-making, although they differ in terms of their implementation. AI is a broader category that encompasses various techniques for programming computers to behave intelligently. ML, on the other hand, is a subset of AI that focuses on developing systems capable of learning from experience and adapting to new situations.
8) Industry networking standards
Because HPC is a data-intensive industry with non-trivial bandwidth and low latency requirements, it relies on industry-standard data communications interfaces such as InfiniBand (IB) and Ethernet. This will allow researchers to use high-performance computing to mix and match hardware components that best meet their needs. Additionally, many systems have multiple types of ports on them to allow users to customize their systems according to project needs.
9) Edge computing
Edge computing refers to placing data-processing resources as close as possible to the source of data. This strategy can help speed up applications and reduce network congestion by reducing the time it takes for data to travel back and forth between remote servers and end-users.
Edge computing also improves security by keeping sensitive information on corporate networks instead of sending it over public connections.
10) Cloud
HPC workloads are increasingly running in the cloud. Cloud computing is a popular choice for HPC workloads, particularly for organizations that need to scale up or down as business needs change. Cloud providers typically offer a pay-as-you-go pricing structure, making it easy to scale up or down as needed. Additionally, cloud providers offers a wide range of tools, which creates more potential synergy with HPC solutions.
