And a growing market focus on improving the performance of machine learning is shifting the locus of hardware innovation. Likewise, continued increases in transistor density have become more technically challenging, and the rising cost of semiconductor fabrication facilities has shifted market economics.Ĭoncurrently, the “hyperscalers”-the largest of the cloud service providers-have begun designing their own processors and accelerators, which are not available for purchase. As a result, ever larger chip core counts, along with data parallel accelerators (GPUs), have been needed to increase hardware performance, with associated pressure on application developers to increase parallelism and directly manage memory features. For example, energy dissipation limits on clock operating frequencies have led to only small increases in single-thread performance. Many of these traditional approaches are nearing either physical or practical economic limits. Increasing the number of cores per chip, compute density, vector processing units, and memory capacity, as well as scaling through parallel processing, adding high-bandwidth memory (HBM), and widening interfaces to improve communications bandwidth-along with building larger systems-have been the primary mechanisms used to increase system performance over the past two to three generations of supercomputers.

The use of commodity central processing units (CPUs), graphics processing units (GPUs), memory, and storage technologies dominates the computer system structures used today to support high-performance computing (HPC). All of these changes, together with shifting mission needs, have profound implications for how NNSA continues to co-design, configure, and deploy future computing systems. Not only are the underlying semiconductor ecosystem and computing hardware and computer system architectures shifting rapidly, so too is the software, along with rapid changes in computing business models and economics. The broad computing environment in which NNSA has acquired and deployed increasingly powerful computing systems as part of the ASC program is now in great flux. To meet mission needs, NNSA must partner in new and strategic ways to both meet its computing needs and respond to evolving geopolitical circumstances. Simply put, NNSA no longer has the same capability to drive the future of advanced computing as it did in the past. These shifts in the semiconductor ecosystem and associated market forces, together with rising costs and global competition, are indicative of the challenges now facing NNSA.

Moreover, technological shifts owing to the end of Dennard scaling and the slowing of Moore’s law have raised questions about the technical and economic viability of continued reductions in transistor sizes and associated growth in computing performance, all at a time when the locus of semiconductor design is now being driven by artificial intelligence (AI) and cloud-computing ( Box 2-1) workload needs, rather than predominantly by technical computing. Meanwhile, there is credible evidence that China was the first country to deploy exascale computing systems, targeting China’s own national security interests. Concurrently, fabrication of leading-edge semiconductors has shifted to offshore foundries such as the Taiwan Semiconductor Manufacturing Company (TSMC), with both supply-chain issues and potential national security implications, particularly for systems that must be deployed in secure environments. Via the Advanced Simulation and Computing (ASC) program, the National Nuclear Security Administration (NNSA) spearheaded the global development of terascale and then petascale computing, and its collaboration with the Department of Energy’s (DOE’s) Office of Science in the Exascale Computing Project (ECP) has led to the first U.S. Disruptions to the Computing Technology Ecosystem for Stockpile Stewardship

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