Search Results for author: Dingwen Tao

Found 13 papers, 5 papers with code

EmojiCloud: a Tool for Emoji Cloud Visualization

no code implementations NAACL (Emoji) 2022 Yunhe Feng, Cheng Guo, Bingbing Wen, Peng Sun, Yufei Yue, Dingwen Tao

This paper proposes EmojiCloud, an open-source Python-based emoji cloud visualization tool, to generate a quick and straightforward understanding of emojis from the perspective of frequency and importance.

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

Exploring Autoencoder-based Error-bounded Compression for Scientific Data

no code implementations25 May 2021 Jinyang Liu, Sheng Di, Kai Zhao, Sian Jin, Dingwen Tao, Xin Liang, Zizhong Chen, Franck Cappello

(1) We provide an in-depth investigation of the characteristics of various autoencoder models and develop an error-bounded autoencoder-based framework in terms of the SZ model.

Image 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.

A Novel Memory-Efficient Deep Learning Training Framework via Error-Bounded Lossy Compression

no code implementations18 Nov 2020 Sian Jin, Guanpeng Li, Shuaiwen Leon Song, Dingwen Tao

In this paper, we propose a novel memory-driven high performance DNN training framework that leverages error-bounded lossy compression to significantly reduce the memory requirement for training in order to allow training larger networks.

cuSZ: An Efficient GPU-Based Error-Bounded Lossy Compression Framework for Scientific Data

2 code implementations19 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

ISM2: Optimizing Irregular-Shaped Matrix-Matrix Multiplication on GPUs

2 code implementations9 Feb 2020 Cody Rivera, Jieyang Chen, Nan Xiong, Shuaiwen Leon Song, Dingwen Tao

Many works have been done on optimizing linear algebra operations on GPUs with regular-shaped input.

Distributed, Parallel, and Cluster Computing

DeepSZ: A Novel Framework to Compress Deep Neural Networks by Using Error-Bounded Lossy Compression

1 code implementation26 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.

 Ranked #1 on Neural Network Compression on ImageNet (using extra training data)

Network Pruning Neural Network Compression

Significantly Improving Lossy Compression for Scientific Data Sets Based on Multidimensional Prediction and Error-Controlled Quantization

no code implementations12 Jun 2017 Dingwen Tao, Sheng Di, Zizhong Chen, Franck Cappello

One serious challenge is that the data prediction has to be performed based on the preceding decompressed values during the compression in order to guarantee the error bounds, which may degrade the prediction accuracy in turn.

Information Theory Information Theory

Z-checker: A Framework for Assessing Lossy Compression of Scientific Data

1 code implementation12 Jun 2017 Dingwen Tao, Sheng Di, Hanqi Guo, Zizhong Chen, Franck Cappello

However, lossy compressor developers and users are missing a tool to explore the features of scientific datasets and understand the data alteration after compression in a systematic and reliable way.

Other Computer Science Instrumentation and Methods for Astrophysics Computational Engineering, Finance, and Science

Cannot find the paper you are looking for? You can Submit a new open access paper.