Exascale computing is a milestone in computational capability, enabling unprecedented processing power that can handle extremely large datasets and complex computational problems. This level of performance is crucial for advanced AI applications, including deep learning, simulations of complex systems, and the analysis of vast amounts of data in real time. The advent of exascale computing opens new horizons for AI research and development, facilitating more sophisticated and accurate models, faster processing times, and the ability to tackle previously intractable problems in science, medicine, and national security.
Historical overview: The concept of exascale computing has been pursued since the early 2000s, with the term becoming more commonly used in the computing and research communities as advancements in technology began to make such a goal feasible. Significant investment and research efforts have been aimed at achieving exascale computing capabilities, with the expectation that the first exascale systems would come online in the 2020s.
Key contributors: The development towards exascale computing has been a global effort, with significant contributions from governmental research organizations, universities, and private corporations worldwide. Major projects and collaborations in the United States (e.g., the Department of Energy’s Exascale Computing Project), Europe, China, and Japan have been instrumental in driving the research, development, and deployment of exascale computing technologies.