Modeling and Characterizing Shared and Local Memories of the Ampere GPUs
Author:
Affiliation:
1. New Mexico State University, USA
2. New Mexico State University, USA and Qualcomm, USA
3. Los Alamos National Laboratory, USA
4. New Mexico State University, USA and Los Alamos National Laboratory, USA
Funder
Los Alamos National Laboratory is managed by Triad National Security, LLC, for the National Nuclear Security Administration of the U.S. DOE under contract 89233218CNA000001. This work is partially supported by Triad National Security, LLC subcontract \#581326.
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3631882.3631891
Reference20 articles.
1. DeepBench. https://svail.github.io/DeepBench/.
2. Demystifying the Nvidia Ampere Architecture through Microbenchmarking and Instruction-level Analysis
3. Fast, accurate, and scalable memory modeling of GPGPUs using reuse profiles
4. Hybrid, scalable, trace-driven performance modeling of GPGPUs
5. Yehia Arafa Abdel-Hameed A. Badawy Gopinath Chennupati Nandakishore Santhi and Stephan Eidenbenz. PPT-GPU Tool. https://github.com/lanl/PPT
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