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20162025
most citedAI and ML Accelerator Survey and Trends

96 citations · 274 across the 34 of their papers we have counts for

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26 papers · 1 filter

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

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…

cs.DC2024

HPC with Enhanced User Separation

Andrew Prout, Albert Reuther, Michael Houle +19

HPC systems used for research run a wide variety of software and workflows. This software is often written or modified by users to meet the needs of their research projects, and ra…

cs.DC20243 cited

LLM Inference Serving: Survey of Recent Advances and Opportunities

Baolin Li, Yankai Jiang, Vijay Gadepally +1

This survey offers a comprehensive overview of recent advancements in Large Language Model (LLM) serving systems, focusing on research since the year 2023. We specifically examine…