Toward a Holistic Performance Evaluation of Large Language Models Across Diverse AI Accelerators-Reference-Cited by-同舟云学术

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Toward a Holistic Performance Evaluation of Large Language Models Across Diverse AI Accelerators

Published:2024-05-27 Issue: Volume:1 Page:1-10
ISSN:
Container-title:2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)
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Author:

Emani Murali¹,Foreman Sam¹,Sastry Varuni¹,Xie Zhen²,Raskar Siddhisanket¹,Arnold William¹,Thakur Rajeev¹,Vishwanath Venkatram¹,Papka Michael E.¹,Shanmugavelu Sanjif³,Gandhi Darshan⁴,Zhao Hengyu⁴,Ma Dun⁴,Ranganath Kiran⁴,Weisner Rick⁴,Chen Jiunn-yeu⁵,Yang Yuting⁵,Vassilieva Natalia⁶,Zhang Bin C.⁶,Howland Sylvia⁶,Tsyplikhin Alexander⁷

Affiliation:

1. Argonne National Laboratory,Lemont,IL,USA,60439

2. State University of New York,Binghamton,NY,USA,13092

3. Groq Inc., Mountain View,CA,USA,94041

4. SambaNova Systems Inc.,Palo Alto,CA,USA,94303

5. Intel Habana,Santa Clara,CA,USA,95054

6. Graphcore Inc.,Palo Alto,CA,USA,94301

7. Cerebras Systems,Sunnyvale,CA,USA,95085

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10596331/10596332/10596441.pdf?arnumber=10596441

Reference22 articles.

1. Highly accurate protein structure prediction with AlphaFold

2. CosmoFlow: Using Deep Learning to Learn the Universe at Scale

3. Cosmic background removal with deep neural networks in SBND;Acciarri;arXiv preprint,2020

4. GenSLMs: Genome-scale language models reveal SARS-Co V-=-2evolutionary dynamics;Zvyagin;bioRxiv,2022

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