papers

Publications (13)

cs.DC2025

Aurora: Architecting Argonne's First Exascale Supercomputer for Accelerated Scientific Discovery

William E. Allcock, Benjamin S. Allen, James Anchell +106

Aurora is Argonne National Laboratory's pioneering Exascale supercomputer, designed to accelerate scientific discovery with cutting-edge architectural innovations. Key new technolo…

hep-lat2022

Lattice QCD and Particle Physics

Andreas S. Kronfeld, Tanmoy Bhattacharya, Thomas Blum +79

Contribution from the USQCD Collaboration to the Proceedings of the US Community Study on the Future of Particle Physics (Snowmass 2021).

cs.PF2023

A Comprehensive Performance Study of Large Language Models on Novel AI Accelerators

Murali Emani, Sam Foreman, Varuni Sastry +6

Artificial intelligence (AI) methods have become critical in scientific applications to help accelerate scientific discovery. Large language models (LLMs) are being considered as a…

hep-lat2021

Deep Learning Hamiltonian Monte Carlo

Sam Foreman, Xiao-Yong Jin, James C. Osborn

We generalize the Hamiltonian Monte Carlo algorithm with a stack of neural network layers and evaluate its ability to sample from different topologies in a two dimensional lattice…

cs.IR2025

HiPerRAG: High-Performance Retrieval Augmented Generation for Scientific Insights

Ozan Gokdemir, Carlo Siebenschuh, Alexander Brace +21

The volume of scientific literature is growing exponentially, leading to underutilized discoveries, duplicated efforts, and limited cross-disciplinary collaboration. Retrieval Augm…

cs.LG2026

Extending P: Spectral Conditions for Feature Learning Across Optimizers

Akshita Gupta, Marieme Ngom, Sam Foreman +1

Several variations of adaptive first-order and second-order optimization methods have been proposed to accelerate and scale the training of large language models. The performance o…