# Survey of HPC in US Research Institutions

arXiv: https://arxiv.org/abs/2506.19019 · HTML: https://mlsanity.com/paper/2506.19019/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 81 references that are in the map.
- First posted on arXiv 23 Jun 2025.

## Builds on (most cited first, 3 of 3)

- [TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings](https://mlsanity.com/paper/2304.01433/) (2023)
- [MISO: Exploiting Multi-Instance GPU Capability on Multi-Tenant Systems for Machine Learning](https://mlsanity.com/paper/2207.11428/) (2022)
- [HLAT: High-quality Large Language Model Pre-trained on AWS Trainium](https://mlsanity.com/paper/2404.10630/) (2024)

## Abstract (arXiv)

The rapid growth of AI, data-intensive science, and digital twin technologies has driven an unprecedented demand for high-performance computing (HPC) across the research ecosystem. While national laboratories and industrial hyperscalers have invested heavily in exascale and GPU-centric architectures, university-operated HPC systems remain comparatively under-resourced. This survey presents a comprehensive assessment of the HPC landscape across U.S. universities, benchmarking their capabilities against Department of Energy (DOE) leadership-class systems and industrial AI infrastructures. We examine over 50 premier research institutions, analyzing compute capacity, architectural design, governance models, and energy efficiency. Our findings reveal that university clusters, though vital for academic research, exhibit significantly lower growth trajectories (CAGR ≈ 18%) than their national (≈ 43%) and industrial (≈ 78%) counterparts. The increasing skew toward GPU-dense AI workloads has widened the capability gap, highlighting the need for federated computing, idle-GPU harvesting, and cost-sharing models. We also identify emerging paradigms, such as decentralized reinforcement learning, as promising opportunities for democratizing AI training within campus environments. Ultimately, this work provides actionable insights for academic leaders, funding agencies, and technology partners to ensure more equitable and sustainable HPC access in support of national research priorities.