COMET: A Novel Memory-Efficient Deep Learning Training Framework by
Using Error-Bounded Lossy Compression

COMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compression

18 November 2021

Chengming Zhang

Dingwen Tao

Papers citing "COMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compression"

8 / 8 papers shown

Title
Enhancing Lossy Compression Through Cross-Field Information for Scientific Applications Youyuan Liu Wenqi Jia Taolue Yang Miao Yin Sian Jin 36 1 0 26 Sep 2024
MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring Qian Gong Jieyang Chen Ben Whitney Xin Liang Viktor Reshniak ... Ana Gainaru N. Podhorszki Rick Archibald Sanjay Ranka S. Klasky 25 19 0 11 Jan 2024
ROAM: memory-efficient large DNN training via optimized operator ordering and memory layout Huiyao Shu Ang Wang Ziji Shi Hanyu Zhao Yong Li Lu Lu OffRL 18 2 0 30 Oct 2023
FZ-GPU: A Fast and High-Ratio Lossy Compressor for Scientific Computing Applications on GPUs Bo Zhang Jiannan Tian Sheng Di Xiaodong Yu Yunhe Feng Xin Liang Dingwen Tao Franck Cappello 8 20 0 25 Apr 2023
GPULZ: Optimizing LZSS Lossless Compression for Multi-byte Data on Modern GPUs Bo Zhang Jiannan Tian Sheng Di Xiaodong Yu M. Swany Dingwen Tao Franck Cappello 13 12 0 14 Apr 2023
SOLAR: A Highly Optimized Data Loading Framework for Distributed Training of CNN-based Scientific Surrogates Baixi Sun Xiaodong Yu Chengming Zhang Jiannan Tian Sian Jin K. Iskra Tao Zhou Tekin Bicer Pete Beckman Dingwen Tao 11 1 0 01 Nov 2022
Accelerating Parallel Write via Deeply Integrating Predictive Lossy Compression with HDF5 Sian Jin Dingwen Tao Houjun Tang Sheng Di S. Byna Z. Lukić Franck Cappello 20 18 0 29 Jun 2022
AutoMC: Automated Model Compression based on Domain Knowledge and Progressive search strategy Chunnan Wang Hongzhi Wang Xiangyu Shi 11 0 0 24 Jan 2022