activity
20172026
most citedAI and ML Accelerator Survey and Trends

96 citations · 248 across the 29 of their papers we have counts for

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24 papers · 1 filter

cs.DC2026

Interactive and Urgent HPC: State of the Research

Albert Reuther, William Arndt, Johannes Blaschke +10

When we think of how we use smartphones, e-commerce, collaboration platforms, LLMs, etc., most of our interactions with computers are interactive and often urgent. Similar trends o…

cs.DC20252 cited

Lincoln AI Computing Survey (LAICS) and Trends

Albert Reuther, Peter Michaleas, Michael Jones +2

In the past year, generative AI (GenAI) models have received a tremendous amount of attention, which in turn has increased attention to computing systems for training and inference…

cs.DC2025

Scaling Performance of Large Language Model Pretraining

Alexander Interrante-Grant, Carla Varela-Rosa, Suhaas Narayan +2

Large language models (LLMs) show best-in-class performance across a wide range of natural language processing applications. Training these models is an extremely computationally e…

cs.DC2025

Easy Acceleration with Distributed Arrays

Jeremy Kepner, Chansup Byun, LaToya Anderson +20

High level programming languages and GPU accelerators are powerful enablers for a wide range of applications. Achieving scalable vertical (within a compute node), horizontal (acros…

cs.DC20241 cited

GPU Sharing with Triples Mode

Chansup Byun, Albert Reuther, LaToya Anderson +19

There is a tremendous amount of interest in AI/ML technologies due to the proliferation of generative AI applications such as ChatGPT. This trend has significantly increased demand…

cs.DC2024

Supercomputer 3D Digital Twin for User Focused Real-Time Monitoring

William Bergeron, Matthew Hubbell, Daniel Mojica +17

Real-time supercomputing performance analysis is a critical aspect of evaluating and optimizing computational systems in a dynamic user environment. The operation of supercomputers…