98 citations · 215 across the 26 of their papers we have counts for
4 papers · 1 filter
A Novel Memory-Efficient Deep Learning Training Framework via Error-Bounded Lossy Compression
Sian Jin, Guanpeng Li, Shuaiwen Leon Song +1
Deep neural networks (DNNs) are becoming increasingly deeper, wider, and non-linear due to the growing demands on prediction accuracy and analysis quality. When training a DNN mode…
ClickTrain: Efficient and Accurate End-to-End Deep Learning Training via Fine-Grained Architecture-Preserving Pruning
Chengming Zhang, Geng Yuan, Wei Niu +8
Convolutional neural networks (CNNs) are becoming increasingly deeper, wider, and non-linear because of the growing demand on prediction accuracy and analysis quality. The wide and…
cuSZ: An Efficient GPU-Based Error-Bounded Lossy Compression Framework for Scientific Data
Jiannan Tian, Sheng Di, Kai Zhao +8
Error-bounded lossy compression is a state-of-the-art data reduction technique for HPC applications because it not only significantly reduces storage overhead but also can retain h…
Understanding GPU-Based Lossy Compression for Extreme-Scale Cosmological Simulations
Sian Jin, Pascal Grosset, Christopher M. Biwer +4
To help understand our universe better, researchers and scientists currently run extreme-scale cosmology simulations on leadership supercomputers. However, such simulations can gen…