On the Rise of AMD Matrix Cores: Performance, Power Efficiency, and Programmability-Reference-Cited by-同舟云学术

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On the Rise of AMD Matrix Cores: Performance, Power Efficiency, and Programmability

Published:2024-05-05 Issue: Volume:37 Page:132-143
ISSN:
Container-title:2024 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)
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Author:

Schieffer Gabin¹,De Medeiros Daniel Araújo¹,Faj Jennifer¹,Marathe Aniruddha²,Peng Ivy¹

Affiliation:

1. KTH Royal Institute of Technology,Stockholm,Sweden

2. Lawrence Livermore National Laboratory,Livermore,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10589923/10590014/10590025.pdf?arnumber=10590025

Reference33 articles.

1. Nvidia tensor core programmability, performance & precision;Markidis

2. Harnessing GPU Tensor Cores for Fast FP16 Arithmetic to Speed up Mixed-Precision Iterative Refinement Solvers

3. GPU Tensor Cores for Fast Arithmetic Reductions

4. Accelerating reduction and scan using tensor core units

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