Search Results for author: Chengming Zhang

Found 7 papers, 1 papers with code

SOLAR: A Highly Optimized Data Loading Framework for Distributed Training of CNN-based Scientific Surrogates

no code implementations1 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.

H-GCN: A Graph Convolutional Network Accelerator on Versal ACAP Architecture

no code implementations28 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.

BIG-bench Machine Learning

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

1 code implementation18 Nov 2021 Sian Jin, Chengming Zhang, Xintong Jiang, Yunhe Feng, Hui Guan, Guanpeng Li, Shuaiwen Leon Song, Dingwen Tao

In this paper, we propose a novel memory-efficient CNN training framework (called COMET) that leverages error-bounded lossy compression to significantly reduce the memory requirement for training, to allow training larger models or to accelerate training.

Data Compression

ClickTrain: Efficient and Accurate End-to-End Deep Learning Training via Fine-Grained Architecture-Preserving Pruning

no code implementations20 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.

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