The global exascale computing market was valued at USD 1.30 billion in 2023 and grew at a CAGR of 23% from 2024 to 2033. The market is expected to reach USD 10.30 billion by 2033. The rising volume of data will drive the growth of the global exascale computing market.

Market Introduction:

Exascale computing defined as computing system capable of running at least one exaflop or 10¹⁸ flops, where flop stands for floating point operations or FLOPS. This is an unbelievable quantum jump in computing capability is a major advance beyond present Peta Scale systems that process data at a rate of 10¹⁵ FLOPS. Exascale computing plays a crucial role of solving different scientific, engineering, and data up intensive problems. Exascale computing is expected to provide substantial improvements in the capacity to address various kinds of grand challenges in areas of science and engineering such as climate, chemistry, biology, and astronomy. Exascale systems will help researchers get through high-resolution modelling and simulation and big data analysis, faster, thus enabling speedy innovation. Exascale computing can be considered as a qualitative leap in the global scale of high-performance computing capabilities that will have a significant impact on research and industries by offering the unlimited computational power.

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Recent Development

• Installed in June 2023, the Aurora supercomputer is designed to tackle some of the most challenging scientific problems in the world. Right now, Aurora is the world's second-fastest supercomputer. Compared to earlier generations of supercomputers, Aurora opens new levels of precision, speed, and power with its recent attainment of exascale performance. This development will greatly benefit scientific research in fields including green energy, cancer research, and climate modelling.

Market Dynamics:

Drivers

The growing high-intensity computational needs – The main applications for exascale computing are defined by the rising computational requirements from the accelerated pace of data generation. The amount of data produced is increasing at a rate which is nearly doubling approximately every two years. Furthermore, the influx of AI and ML technologies has enhanced the need for better computational capabilities. A need for training free algorithms faster in AI has prompted the demand for exascale systems. With more and more companies adopting data-oriented strategies, the capacity to process big volumes of information in real-time becomes vital in order to achieve competitiveness and optimization of performances. Therefore, the growing high-intensity computational needs given the ever-increasing data generation will contribute to the global exascale computing market’s growth.

Restraints:

The high costs of exascale computing – Creating exascale systems would need the best of hardware that can provide robust performance. This is followed by processors, and memory systems, alongside niche tech like Graphic Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs), which are costly. In addition, the kind of sophisticated cooling systems, high power outlet connection mechanisms, appropriate data centres infrastructure etc adds to the costs. Other challenges are associated with software development costs. Designing fast software and adaption of the exascale systems is not an easy task as it requires lots of research and development. This includes the production of new algorithms and the programming of new models. Such specific development of software increases costs. Further, maintenance cost, electricity consumption, cooling cost has to be included in the total expenses at the operational level. therefore, the high costs of exascale computing will hamper the market’s growth.

• The UK government has rejected £1.3 billion ($1.66 billion) in financing for AI and technology initiatives that the previous government had declared. A few of the projects affected are the AI Research Resource (AIRR) and the exascale supercomputer that the University of Edinburgh planned to build.

Opportunities:

The proliferation of AI and ML technologies worldwide – AI and ML are on the forefront driving the demand for exascale computing since these models need immense computational capabilities in their training and deployment processes. Growing demand for Artificial Intelligence and machine learning solutions across sectors such as healthcare and finance, automotive and entertainment industries has increased the demands for more capable computing systems to support strongly complex algorithms and data sets. Today’s AI models especially deep learning networks can be largely trained on big data that can be anything to terabytes or even petabytes large. This intensive training procedure prescribes doing millions, if not billions, of calculations, and this is why traditional computing platforms are unsuitable for this purpose. Exascale can accommodate such incredible workloads thus allowing researchers or organizations to create more complex AI models with increased precision and effectiveness. Therefore, the improvements in AI and ML contribute to the increasing demand for exascale computing worldwide.

Regional segmentation analysis:

The regions analyzed for the market include North America, Europe, South America, Asia Pacific, the Middle East, and Africa. North America emerged as the most significant global exascale computing market, with a 38% market revenue share in 2023.

North America is expected to dominate the exascale computing market, mainly due to its strong cluster of research centres, a highly advanced technological foundation, and progressive government funding. High-performance computing (HPC) continues to be an area where the United States has made outstanding contributions that have served as models for the rest of the world. The high concentration of prestigious universities and national laboratories has laid a firm research and development groundwork in the area of the exascale computing. These institutions contain arguably the most potent computational Cluster in the world by today’s standards making it possible for the institutions to do research in different areas such as climate science, material science and biomedical research among others. Moreover, the government and specific funding programs augment the regional market’s growth. The public private partnership and university has played a role in enabling of innovation for both the hardware and software required for exascale computing.

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• The limit for calculating the number of atoms in a molecular dynamics’ simulation has been raised by the most powerful supercomputer in the world. This simulation is 1,000 times faster and larger than any prior simulation of its sort. A group of academics lead by University of Melbourne associate professor Giuseppe Barca broke new boundaries in the realm of exascale computing. The group ran a molecular dynamics simulation on Frontier, the most potent supercomputer in the world, which was 1,000 times faster and larger than any prior state-of-the-art simulation of the same kind. Researchers from the Department of Energy's Oak Ridge National Laboratory at the University of Melbourne carried out the simulation.

Component Segment Analysis

The component segment is divided into hardware and software. The hardware segment dominated the market, with a market share of around 57% in 2023. Exascale computing requires advanced, superior special hardware components (processors, memory architectures and specific accelerators). CPUs are primary components in these systems. The importance of proper hardware is felt much more because of the complexity of applications in fields such as artificial intelligence, machine learning, and scientific applications. Since these applications require more computation, the need to meet application requirements force evolution of processors and memory associated with these applications. Furthermore, the decennial improvements made in the domain of the computer hardware affect the global structure and organization of computing systems.

Deployment type Segment Analysis

The deployment type segment is divided into on-premises and cloud-based solutions. The on-premises segment dominated the market, with a market share of around 55% in 2023. The on-premises deployment model currently holds the majority of market share in the exascale computing domain, mostly owing to the fact that it offers organizations the tools for managing, customizing, and implementing computing resources for large computational workloads. On-premises model is used where the organization wants to have full control over its key applications and data, this is common in research facilities and government agents. This model enables them to choose both the hardware and the software according to the need of the project they undertake hence enable them to have the best computational environment. Further, on-premises systems can help achieve better response time, and utilization of bandwidth. The on-premises deployment model will remain popular in the future because performance, security, and control of computational resources remain high priorities for organizations in the exascale marketplace and on-premises deployment model enables this.

• The exascale supercomputer is Google's latest technological marvel. With a processing speed of one trillion operations per second, it is poised to dramatically change the way we see computing today.

Application Segment Analysis

The application segment is divided into scientific research, artificial intelligence and machine learning, financial services, manufacturing, healthcare, and government and defence. The scientific research segment dominated the market, with a market share of around 34% in 2023. Scientists and engineers in fields like physics, climate, biology, and material science need more modelling and analysis capabilities than conventional architectures can provide. Exascale computing allows researchers to model complex processes in detail. Exascale computing is vital for climate science applications, genomics and drug discovery among others. Furthermore, scientific research work may require the results coming from different institutions and even different countries and therefore entails sharing and analysing huge datasets. Exascale computing underpins such collaborations by offering algorithms and effective tools for the analysis of the data.

• With the NVIDIA Blackwell platform, Supermicro, Inc.—a Complete IT Solution Provider for AI, Cloud, Storage, and 5G/Edge—is quickening the industry's shift to liquid-cooled data centers and delivering a new paradigm of energy efficiency for the quickly increasing energy demand of new AI infrastructures. With the NVIDIA GB200 NVL72 platform powering exascale computing in a single rack, Supermicro has begun sampling its industry-leading end-to-end liquid-cooling solutions to a small group of customers in preparation for full-scale production in late Q4. Furthermore, production-ready NVIDIA HGX B200 8-GPU systems are the recently unveiled Supermicro X14 and H14 4U liquid-cooled systems and 10U air-cooled systems.

End user Segment Analysis

The end user segment is divided into academic and research institutions, government organizations, and private sector enterprises. The academic and research institutions segment dominated the market, with a market share of around 43% in 2023. The largest consumer of exascale computing services is the academic and research sector as it is the major controller of scientific development and innovation throughout various fields. These organizations are in the vanguard of scientific work that calls for major computational resources, including advanced computational astrophysics, intricate biological modelling, and, particularly, large-scale data analysis. Moreover, academic institutions relying on exascale systems for large scale/hybrid projects, developments, and multidisciplinary partnerships with government agencies and private industries. It should be noted that collaborations of this type can result in the imposition of major advances in such fields as the climate, renewable energy, and material science. Accompanying these partnerships are exascale computing resources which help the researchers solve big questions that demand vast computing capacity and fresh ideas.

Some of the Key Market Players:

• Amazon Web Services (AWS)
• AMD (Advanced Micro Devices)
• Atos
• Cray (a Hewlett Packard Enterprise company)
• Dell Technologies
• Fujitsu
• Google
• Huawei
• IBM
• Intel
• Lenovo
• Microsoft
• NEC Corporation
• NVIDIA
• Penguin Computing

Report Description: