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.
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 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.
This study forecasts revenue at global, regional, and country levels from 2020 to 2033. The Brainy Insights has segmented the global exascale computing market based on below mentioned segments:
Global Exascale Computing Market by Component:
Global Exascale Computing Market by Deployment Type:
Global Exascale Computing Market by Application:
Global Exascale Computing Market by End User:
Global Exascale Computing Market by Region:
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