High-Performance Computing (HPC) Technology Trends

 Supercomputers and parallel processing methods are used in High-Performance Computing Theme to quickly complete time-consuming tasks or multiple tasks at once. Edge computing and artificial intelligence (AI) are two examples of technologies that have the potential to expand HPC's capabilities and deliver high-performing processing power to a variety of industries.

GlobalData identified the key technology trends that are affecting the high-performance computing theme. These trends are listed below.

AI AI is at the center of technological disruption because devices used by individuals and businesses collect a lot of data. Without data analytics and artificial intelligence, the vast amounts of data produced each day are useless. High-performance machines are in high demand as AI adoption in businesses grows. The need to compute large amounts of data for AI workloads is largely to blame for the renewed interest in HPC.

Since AI workloads are powered by HPC, the relationship between AI and HPC is symbiotic, but AI can identify improvements in HPC data centers. AI, for instance, has the ability to optimize HVAC systems, thereby lowering electricity costs and increasing efficiency. AI systems can also predict when equipment is about to fail, check that systems are still correctly configured, and monitor the health of servers, storage, and networking gear.

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In addition, AI can be utilized for data protection, screening and analyzing both incoming and outgoing data, detecting malware, and implementing behavioral analytics.

Compared to tensor processing units (TPUs), gaming was the first application for graphics processing units (GPUs), which revolutionized high-resolution video games. GPUs are now being used in additional contexts, including HPC. GPUs are used in everything from machine learning to self-driving cars for data-intensive work. Due to their focus on data computation, they have proven to be superior chips for processing HPC workloads.

Nvidia is the industry leader in the production of GPUs, so the rise of GPUs has made the company a key player in HPC. However, Google's TPUs are beginning to challenge GPU dominance. TPUs are application-specific integrated circuits (ASICs) that facilitate the acceleration of AI algorithms and calculations.

They were made specifically for neural network machine learning by Google, which also developed its TensorFlow software. GPUs will continue to play a major role in high-performance computing (HPC) for the time being. However, Nvidia cannot afford to rest on its laurels if it wants to remain relevant in HPC due to Google's in-house chip development.

Flexibility The range of processing options offered by FPGAs, ASICs, GPUs, and traditional central processing units (CPUs) is constantly expanding. Because workloads can change a lot, it's important to be flexible and provide different computing for different use cases.

Beyond processing capabilities, HPC players increasingly allow customization of their offerings. Customers of Huawei can choose from three distinct HPC architectures, whereas customers of IBM can customize data storage, and customers of HPE are charged in accordance with flexible consumption models.

Customers have the option of having their HPC data center installed at the edge, in the cloud, or on-premise. In a single package, some vendors provide a combination of solutions for various workloads.

The rise of HPC as a service (HPCaaS) is linked to the rise of the cloud as an HPC solution. Many vendors have switched from selling equipment to providing HPCaaS. Although traditional HPC vendors are also offering HPCaaS, HPCaaS can be a compelling option for end-users because it puts intense data processing and workloads that require high-performance within reach of businesses that lack the necessary capital to hire skilled staff and invest in hardware. As a result, the trend toward HPCaaS is benefiting cloud players like Amazon Web Services (AWS), Google, and Alibaba. HPCaaS provides HPC capabilities to businesses that cannot afford to build their own HPC infrastructure and knowledge. However, using HPCaaS rather than developing HPC internally introduces all of the limitations of HPC deployed in the cloud.

On-premise data centers were the birthplace of hybrid solutions for HPC; however, cloud computing began to transform HPC in the second half of the 2010s. The edge is a brand-new HPC deployment platform that has just become available. As the market for high-performance solutions grows, vendors have begun offering hybrid options. Cloud capabilities are typically included in a hybrid HPC solution to complement an existing on-premises data center.

Some of the shortcomings of the public cloud can be overcome by utilizing both on-premise and private cloud hosting. These shortcomings include issues with performance and optimization brought on by the diversity and complexity of numerous industry-specific, data-intensive HPC workloads. In contrast, hybrid solutions offer the flexibility of the cloud while also being customizable and typically scalable.

Providers like Dell and HPE will gain from moving toward hybrid models. If advancements in the cloud make it possible to address its flaws, players like Microsoft and AWS will be in a better position.

One major trend in HPC is democratization flexibility, HPCaaS, and the emergence of hybrid solutions: democratisation. This trend is related to increasing end-user access to HPC and placing the technology within their reach.

In the past, research, academia, and the military were the only uses for supercomputers. After that, HPC expanded into banking, oil and gas, and stock trading. The automotive, aerospace, and even food processing industries are just a few of the many industries that use HPC. The reach of HPC will be further extended by placing it at the edge.

A computing system's ability to perform a billion calculations per second is known as exascale computing, and its performance is measured in exaFLOPS rather than FLOPS. At the earliest, the first exascale computer is anticipated in 2022.

Exascale computing is not a new type of computing like quantum computing; rather, it refers to the next level of processing power that can be achieved using technology that is already in place. However, Exascale High Performance Computing (HPC) will undoubtedly bring a number of enhancements to advanced simulation and modeling that will address issues such as the prediction of natural disasters and the advancement of scientific discoveries, especially in the medical field.

Improvements in microarchitecture While Exascale computing is a step forward in HPC's overall processing capacity, performance enhancements increasingly come from smaller design innovations that may not attract as much attention but are nonetheless significant.

Improvements in security, eco-friendliness, space management, faster interconnections, higher computing densities, and scalable storage are all examples of progress at the microarchitectural level. Over the next few years, HPC will continue to see advancements like these.