AI-Enabling Workloads on Large-Scale GPU-Accelerated System: Characterization, Opportunities, and Implications-Reference-Cited by-同舟云学术

AI-Enabling Workloads on Large-Scale GPU-Accelerated System: Characterization, Opportunities, and Implications

Published:2022-04 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA)
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Author:

Li Baolin¹,Arora Rohin¹,Samsi Siddharth²,Patel Tirthak¹,Arcand William²,Bestor David²,Byun Chansup²,Roy Rohan Basu¹,Bergeron Bill²,Holodnak John²,Houle Michael²,Hubbell Matthew²,Jones Michael²,Kepner Jeremy²,Klein Anna²,Michaleas Peter²,McDonald Joseph²,Milechin Lauren²,Mullen Julie²,Prout Andrew²,Price Benjamin²,Reuther Albert²,Rosa Antonio²,Weiss Matthew²,Yee Charles²,Edelman Daniel²,Vanterpool Allan³,Cheng Anson³,Gadepally Vijay²,Tiwari Devesh¹

Affiliation:

1. Northeastern University

2. MIT Lincoln Laboratory

3. US Air Force

Funder

Air Force Research Laboratory

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9773179/9773180/09773216.pdf?arnumber=9773216

Reference61 articles.

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5. QBETS

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