1 code implementation • 14 Apr 2023 • Chengming Zhang, Shaden Smith, Baixi Sun, Jiannan Tian, Jonathan Soifer, Xiaodong Yu, Shuaiwen Leon Song, Yuxiong He, Dingwen Tao
Collaborative filtering (CF) has been proven to be one of the most effective techniques for recommendation.
no code implementations • 1 Nov 2022 • Baixi Sun, Xiaodong Yu, Chengming Zhang, Jiannan Tian, Sian Jin, Kamil Iskra, Tao Zhou, Tekin Bicer, Pete Beckman, Dingwen Tao
Our evaluation with three scientific surrogates and 32 GPUs illustrates that SOLAR can achieve up to 24. 4X speedup over PyTorch Data Loader and 3. 52X speedup over state-of-the-art data loaders.
no code implementations • 28 Jun 2022 • Chengming Zhang, Tong Geng, Anqi Guo, Jiannan Tian, Martin Herbordt, Ang Li, Dingwen Tao
Graph Neural Networks (GNNs) have drawn tremendous attention due to their unique capability to extend Machine Learning (ML) approaches to applications broadly-defined as having unstructured data, especially graphs.
no code implementations • 20 Nov 2020 • Chengming Zhang, Geng Yuan, Wei Niu, Jiannan Tian, Sian Jin, Donglin Zhuang, Zhe Jiang, Yanzhi Wang, Bin Ren, Shuaiwen Leon Song, Dingwen Tao
Moreover, compared with the state-of-the-art pruning-during-training approach, ClickTrain provides significant improvements both accuracy and compression ratio on the tested CNN models and datasets, under similar limited training time.
2 code implementations • 19 Jul 2020 • Jiannan Tian, Sheng Di, Kai Zhao, Cody Rivera, Megan Hickman Fulp, Robert Underwood, Sian Jin, Xin Liang, Jon Calhoun, Dingwen Tao, Franck Cappello
To the best of our knowledge, cuSZ is the first error-bounded lossy compressor on GPUs for scientific data.
Distributed, Parallel, and Cluster Computing
1 code implementation • 26 Jan 2019 • Sian Jin, Sheng Di, Xin Liang, Jiannan Tian, Dingwen Tao, Franck Cappello
In this paper, we propose DeepSZ: an accuracy-loss bounded neural network compression framework, which involves four key steps: network pruning, error bound assessment, optimization for error bound configuration, and compressed model generation, featuring a high compression ratio and low encoding time.