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2020 – today
- 2024
[c3]Grant Wilkins, Sheng Di, Jon C. Calhoun, Zilinghan Li, Kibaek Kim, Robert Underwood, Richard Mortier, Franck Cappello:
FedSZ: Leveraging Error-Bounded Lossy Compression for Federated Learning Communications. ICDCS 2024: 577-588- [c2]Grant Wilkins, Sheng Di, Jon C. Calhoun, Zilinghan Li, Kibaek Kim, Robert Underwood, Richard Mortier, Franck Cappello:
FedSZ: Leveraging Error-Bounded Lossy Compression for Federated Learning Communications. IPDPS (Workshops) 2024: 1187-1188 - [i4]Sheng Di, Jinyang Liu, Kai Zhao, Xin Liang, Robert Underwood, Zhaorui Zhang, Milan Shah, Yafan Huang, Jiajun Huang, Xiaodong Yu, Congrong Ren, Hanqi Guo, Grant Wilkins, Dingwen Tao, Jiannan Tian, Sian Jin, Zizhe Jian, Daoce Wang, Md Hasanur Rahman, Boyuan Zhang, Jon C. Calhoun, Guanpeng Li, Kazutomo Yoshii, Khalid Ayedh Alharthi, Franck Cappello:
A Survey on Error-Bounded Lossy Compression for Scientific Datasets. CoRR abs/2404.02840 (2024) - [i3]Grant Wilkins, Srinivasan Keshav, Richard Mortier:
Hybrid Heterogeneous Clusters Can Lower the Energy Consumption of LLM Inference Workloads. CoRR abs/2407.00010 (2024) - [i2]Grant Wilkins, Srinivasan Keshav, Richard Mortier:
Offline Energy-Optimal LLM Serving: Workload-Based Energy Models for LLM Inference on Heterogeneous Systems. CoRR abs/2407.04014 (2024) - 2023
- [i1]Grant Wilkins, Sheng Di, Jon C. Calhoun, Kibaek Kim, Robert Underwood, Richard Mortier, Franck Cappello:
Efficient Communication in Federated Learning Using Floating-Point Lossy Compression. CoRR abs/2312.13461 (2023) - 2022
- [c1]Grant Wilkins, Jon C. Calhoun:
Modeling Power Consumption of Lossy Compressed I/O for Exascale HPC Systems. IPDPS Workshops 2022: 1118-1126
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