Scientific computing in an AI world.
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| Title: | Scientific computing in an AI world. |
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| Authors: | Dongarra, Jack (AUTHOR), Reed, Daniel (AUTHOR), Gannon, Dennis (AUTHOR) |
| Source: | Science. 6/18/2026, Vol. 392 Issue 6804, p1244-1247. 4p. |
| Subjects: | Scientific computing, Artificial intelligence, Economic opportunities, Cloud computing, Digital technology, High performance computing, Scientific discoveries, Ecosystems |
| Abstract: | The center of gravity in advanced computing has transitioned away from traditional scientific and engineering high-performance computing (HPC), with the locus of influence shifted to hyperscale service providers ("hyperscalers" that operate massive, highly scalable cloud computing infrastructure) and consumer smartphone companies (1), but now driven by artificial intelligence (AI). Consequently, scientific and technical computing is increasingly a specialized, policy-driven niche riding atop infrastructure optimized for other, much larger markets. The challenge for scientific computing is to adapt to this rapidly changing world. We suggest maxims that define the present and future of scientific computing and propose a "moonshot" to build a new foundation that would benefit both scientific computing and AI. We must look beyond the narrow, but important, design of next-generation computing systems to how an integrated ecosystem of new, nascent, and still-to-be developed technologies enables scientific discovery, economic opportunities, public health, and global security. [ABSTRACT FROM AUTHOR] |
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| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | The center of gravity in advanced computing has transitioned away from traditional scientific and engineering high-performance computing (HPC), with the locus of influence shifted to hyperscale service providers ("hyperscalers" that operate massive, highly scalable cloud computing infrastructure) and consumer smartphone companies (1), but now driven by artificial intelligence (AI). Consequently, scientific and technical computing is increasingly a specialized, policy-driven niche riding atop infrastructure optimized for other, much larger markets. The challenge for scientific computing is to adapt to this rapidly changing world. We suggest maxims that define the present and future of scientific computing and propose a "moonshot" to build a new foundation that would benefit both scientific computing and AI. We must look beyond the narrow, but important, design of next-generation computing systems to how an integrated ecosystem of new, nascent, and still-to-be developed technologies enables scientific discovery, economic opportunities, public health, and global security. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00368075 |
| DOI: | 10.1126/science.aef4214 |
