Modeling Power Consumption of Lossy Compressed I/O for Exascale HPC Systems. - Grant Wilkins; Jon C. Calhoun (IPDPS Workshops, 2022)

Efficient Communication in Federated Learning Using Floating-Point Lossy Compression. - Grant Wilkins; Sheng Di; Jon C. Calhoun; Kibaek Kim; Robert Underwood; Richard Mortier; Franck Cappello (CoRR, 2023)

Hybrid Heterogeneous Clusters Can Lower the Energy Consumption of LLM Inference Workloads. - Grant Wilkins; Srinivasan Keshav; Richard Mortier (e-Energy, 2024)

FedSZ: Leveraging Error-Bounded Lossy Compression for Federated Learning Communications. - Grant Wilkins; Sheng Di; Jon C. Calhoun; Zilinghan Li; Kibaek Kim; Robert Underwood; Richard Mortier; Franck Cappello (ICDCS, 2024)

A Survey on Error-Bounded Lossy Compression for Scientific Datasets. - Sheng Di; Jinyang Liu 003; Kai Zhao 008; Xin Liang 001; Robert Underwood; Zhaorui Zhang; Milan Shah; Yafan Huang; Jiajun Huang 001; Xiaodong Yu 001; Congrong Ren; Hanqi Guo 001; et al. (CoRR, 2024)

Offline Energy-Optimal LLM Serving: Workload-Based Energy Models for LLM Inference on Heterogeneous Systems. - Grant Wilkins; Srinivasan Keshav; Richard Mortier (CoRR, 2024)

To Compress or Not to Compress: Energy Trade-Offs and Benefits of Lossy Compressed I/O. - Grant Wilkins; Sheng Di; Jon C. Calhoun; Robert Underwood; Franck Cappello (IPDPS, 2025)

A Survey on Error-Bounded Lossy Compression for Scientific Datasets. - Sheng Di; Jinyang Liu 003; Kai Zhao 008; Xin Liang 001; Robert Underwood; Zhaorui Zhang; Milan Shah; Yafan Huang; Jiajun Huang 001; Xiaodong Yu 001; Congrong Ren; Hanqi Guo 001; et al. (ACM Comput. Surv., 2025)