top of page
Sian Jin

Assistant Professor

Department of Computer & Information Sciences

Temple University

Office: SERC 314

Email: sian.jin@temple.edu CV: download

Openings:

I am looking for outstanding Ph.D. students who are interested in the area of HPC, machine learning, data redcution, and computing systems. Details can be found here (English | Chinese).

BIOGRAPHY

Sian Jin (靳思安) is an Assistant Professor at Temple University's Department of Computer and Information Sciences. He received his Ph.D. in Computer Engineering from Indiana University in 2023, under the supervision of Prof. Dingwen Tao. He received his bachelor degree in physics from Beijing Normal University in 2018. His research interest falls in High-performance computing (HPC) data reduction & lossy compression for improving the performance for scientific data analytics & management, as well as for large-scale machine learning & deep learning. Within the past five years, he has published over 20 papers in top conferences and journals, including SC, PPoPP, VLDB, ICDE, EuroSys, HPDC, and ICS.

RESEARCH

Interests include but not limited to:

  • High-Performance Computing

  • Scientific data analytics & management

  • Data reduction & lossy compression

  • Large-scale machine learning & deep learning

  • Parallel file system & I/O system

SELECTED PROJECTS

Data reduction for scientific applications:

Accelerating machine learning via lossy compression:

AWARDS & HONORS

  • Student Grant of The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC), 2022

  • Sixth cohort of the Distinguished Graduate Research Program (DGRP), 2022

  • Technical Program Committee (TPC) for IEEE High Performance Computing and Communications (HPCC), 2021

  • Reviewer for IEEE Transactions on Parallel and Distributed Systems (TPDS), 2021

  • Technical Program Committee (TPC) for IEEE Technical Program Committee (TPC) for IEEE High Performance Computing and Communications (HPCC), 2020

  • Mahmoud M. Dillsi Family Graduate Fellowship $1000, 2020

  • Outstanding Graduate Researcher, The University of Alabama, 2020

  • Student Travel Grant $1,000, ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC), 2019

  • Graduate Conference Funding $1,000, The University of Alabama, 2019

  • Graduate Research Funding $600, The University of Alabama, 2019

  • National University Student Innovation Program, 2017-2018

  • Scholarships of Beijing Normal University, 2015-2016

  • National Olympiad in Information (Advanced Group), First-Class Prize, 2012

  • The 12th China Adolescent Robotics Competition, First-Class Prize, 2012

SELECTED PUBLICATIONS

EuroSys'24

Sian Jin, Sheng Di, Frédéric Vivien, Daoce Wang, Yves Robert, Dingwen Tao, and Franck Cappello.``Concealing Compression-accelerated I/O for HPC Applications through In Situ Task Scheduling.'' Proceedings of the Nineteenth European Conference on Computer Systems, Athens, Greece, April 22-25, 2024.

SIGMOD'24

Jinyang Liu, Sheng Di, Kai Zhao, Xin Liang, Sian Jin, Zizhe Jian, Jiajun Huang, Shixun Wu, Zizhong Chen, and Franck Cappello. ``High-performance Effective Scientific Error-bounded Lossy Compression with Auto-tuned Multi-component Interpolation.'' The ACM Special Interest Group on Management of Data, Santiago, Chile, June 9–-15, 2024.

BigData'23

Jinyang Liu, Sheng Di, Sian Jin, Kai Zhao, Xin Liang, Zizhong Chen, and Franck Cappello. ``Scientific Error-bounded Lossy Compression with Super-resolution Neural Networks.'' 2023 IEEE International Conference on Big Data, Sorrento, Italy, December 15–-18, 2023.

SC'23

Daoce Wang, Jesus Pulido, Pascal Grosset, Jiannan Tian, Sian Jin, Houjun Tang, Jean Sexton, Sheng Di, Zarija Lukić, Kai Zhao, Bo Fang, Franck Cappello, James Ahrens, and Dingwen Tao. ``AMRIC: A Novel In Situ Lossy Compression Framework for Efficient I/O in Adaptive Mesh Refinement Applications.'' The International Conference for High Performance Computing, Networking, Storage, and Analysis, Denver, Colorado, USA, November 12-17, 2023.

TPDS'23

Haoyu Jin, Donglei Wu, Shuyu Zhang, Xiangyu Zou, Sian Jin, Dingwen Tao, Qing Liao, and Wen Xia
Design of a Quantization-based DNN Delta Compression Framework for Model Snapshots and Federated Learning
IEEE Transactions on Parallel and Distributed Systems, Volume 23

SC'22

Sian Jin, Dingwen Tao, Houjun Tang, Sheng Di, Suren Byna, Zarija Lukic, and Franck CappelloAccelerating Parallel Write via Deeply Integrating Predictive Lossy Compression with HDF5The International Conference for High Performance Computing, Networking, Storage, and Analysis, Dallas, Texas, USA, Nov. 13-18, 2022 [paper]

VLDB'22

Sian Jin, Chengming Zhang, Xintong Jiang, Yunhe Feng, Hui Guan, Guanpeng Li, Shuaiwen Leon Song, and Dingwen TaoCOMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy CompressionACM International Conference on Very Large Data Bases, Sydney, Australia, Sep. 5–9, 2022 [paper]

ICDE'22

Sian Jin, Di Sheng, Jiannan Tian, Suren Byna, Dingwen Tao, and Franck CappelloSignificantly Improving Prediction-Based Lossy Compression Via Ratio-Quality ModelingIEEE International Conference on Data Engineering, Worldwide online event, May 9–12, 2022 [paper]

TPDS'22

Yuanjian Liu, Sheng Di, Kai Zhao, Sian Jin, Cheng Wang, Kyle Chard, Dingwen Tao, Ian Foster, and Franck Cappello
Optimizing Error-Bounded Lossy Compression for Scientific Data with Diverse Constraints
IEEE Transactions on Parallel and Distributed Systems, Volume 22 [paper]

ICS'22

Chengming Zhang, Sian Jin, Tong Geng, Jiannan Tian, Ang Li, and Dingwen Tao
CEAZ: Accelerating Parallel I/O via Hardware-Algorithm Co-Designed Adaptive Lossy Compression
ACM International Conference on Supercomputing, Worldwide online event, June 27–30, 2022 [paper]

HPDC'22

Daoce Wang, Jesus Pulido, Pascal Grosset, Sian Jin, Jiannan Tian, James Ahrens, and Dingwen Tao
Optimizing Error-Bounded Lossy Compression for Three Dimensional Adaptive Mesh Refinement Simulations
ACM International Symposium on High-Performance Parallel and Distributed Computing, Minneapolis, Minnesota, USA, June 27–July 1, 2022 [paper]

HPDC'21

Sian Jin, Jesus Pulido, Pascal Grosset, Jiannan Tian, Dingwen Tao, and James AhrensAdaptive Configuration of In Situ Lossy Compression for Cosmology Simulations via Fine-Grained Rate-Quality ModelingACM International Symposium on High-Performance Parallel and Distributed Computing, Worldwide online event, June 21–25, 2021 [paper]

PPoPP'21

Sian Jin, Guanpeng Li, Shuaiwen Leon Song, and Dingwen TaoPOSTER: A Novel Memory-Efficient Deep Learning Training Framework via Error-Bounded Lossy CompressionACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. Worldwide online event, Feb. 27–Mar. 3, 2021 [paper]

HiPC'21

Yuanjian Liu, Sheng Di, Kai Zhao, Sian Jin, Cheng Wang, Kyle Chard, Dingwen Tao, Ian Foster, and Franck Cappello
Optimizing Multi-Range based Error-Bounded Lossy Compression for Scientific Datasets
The IEEE International Conference On High Performance Computing, Data, & Analytics, Bengaluru, India, Dec. 17–18, 2021

Cluster'21

Bo Fang, Daoce Wang, Sian Jin, Quincey Koziol, Zhao Zhang, Qiang Guan, Suren Byna, Sriram Krishnamoorthy, Dingwen Tao
Characterizing Impacts of Storage Faults on HPC Applications: A Methodology and Insights
The IEEE Cluster Conference, Worldwide online event, September 7–10, 2021 [paper]

Cluster'21

Jinyang Liu, Sheng Di, Kai Zhao, Sian Jin, Dingwen Tao, Xin Liang, Zizhong Chen, Franck Cappello
Exploring Autoencoder-Based Error-Bounded Compression for Scientific Data
The IEEE Cluster Conference, Worldwide online event, September 7–10, 2021

Cluster'21

Jiannan Tian, Sheng Di, Xiaodong Yu, Cody Rivera, Kai Zhao, Sian Jin, Yunhe Feng, Xin Liang, Dingwen Tao, Franck Cappello
cuSZ (x): Optimizing Error-Bounded Lossy Compression for Scientific Data on GPUs
The IEEE Cluster Conference, Worldwide online event, September 7–10, 2021 [paper]

ICS'21

Chengming Zhang, Geng Yuan, Wei Niu, Jiannan Tian, Sian Jin, Donglin Zhuang, Zhe Jiang, Yanzhi Wang, Bin Ren, Shuaiwen Leon Song, and Dingwen Tao
ClickTrain: Efficient and Accurate End-to-End Deep Learning Training via Fine-Grained Architecture-Preserving Pruning
ACM International Conference on Supercomputing, Worldwide online event, June 14–17, 2021 [paper]

IPDPS'20

Sian Jin, Pascal Grosset, Christopher M. Biwer, Jesus Pulido, Jiannan Tian, Dingwen Tao, and James AhrensUnderstanding GPU-Based Lossy Compression for Extreme-Scale Cosmological SimulationsIEEE International Parallel & Distributed Processing Symposium, New Orleans, Louisiana, USA, May 18–22, 2020 [paper]

PACT'20

Jiannan Tian, Sheng Di, Kai Zhao, Cody Rivera, Megan Hickman Fulp, Robert Underwood, Sian Jin, and others
cuSZ: An Efficient GPU-Based Error-Bounded Lossy Compression Framework for Scientific Data
ACM International Conference on Parallel Architectures and Compilation Techniques. Virtual, October 2–7, 2020 [paper]

ICPP'20

Zhenbo Hu, Xiangyu Zou, Wen Xia, Sian Jin, Dingwen Tao, Yang Liu, Weizhe Zhang, and Zheng Zhang
Delta-DNN: Efficiently Compressing Deep Neural Networks via Exploiting Floats
The 49th International Conference on Parallel Processing. Edmonton, Canada, August 17–20, 2020 [paper]

PPoPP'20

Jiannan Tian, Sheng Di, Chengming Zhang, Xin Liang, Sian Jin, Dazhao Cheng, Dingwen Tao, and Franck Cappello
WAVESZ: A Hardware-Algorithm Co-Design of Efficient Lossy Compression for Scientific Data
ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. San Diego, California, USA, February 22–26, 2020 [paper]

HPDC'19

Sian Jin, Sheng Di, Xin Liang, Jiannan Tian, Dingwen Tao, and Franck CappelloDeepSZ: A Novel Framework to Compress Deep Neural Networks by Using Error-Bounded Lossy CompressionACM International Symposium on High-Performance Parallel and Distributed Computing, Phoenix, Arizona, USA, June 24–28, 2019 [paper]

SOFTWARE

  • Foresight: A Compression Benchmark Suite for Visualization and Analysis of Simulation Data

  • DeepSZ: Lossy Compression Framework for Deep Neural Networks

  • cuSZ: A GPU Accelerated Error-Bounded Lossy Compressor for Scientific Data

  • CEAZ: An Implementation of SZ Lossy Compression in Vivado HLS for Xilinx FPGAs

Enjoy some of my recent photography!

Final.jpg

Solar eclipse, 2024

bottom of page