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This page presents the publications about SZ from the core group as well as publications presenting other extensions, optimizations and implementations of SZ. If we missed publications, please contact us.

2016

  • [IPDPS’16] Sheng Di and Franck Cappello. “Fast error-bounded lossy HPC data compression with SZ.” 2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 730-739. IEEE, 2016.

2017

  • [IPDPS’17] Dingwen Tao, Sheng Di, Zizhong Chen, and Franck Cappello. “Significantly improving lossy compression for scientific data sets based on multidimensional prediction and error-controlled quantization.” 2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 1129-1139. IEEE, 2017.
  • [BigData’17] Dingwen Tao, Sheng Di, Zizhong Chen, and Franck Cappello. “In-Depth Exploration of Single-Snapshot Lossy Compression Techniques for N-Body Simulations.” 2017 IEEE International Conference on Big Data (BigData), Boston, MA, USA, December 11 - 14, 2017.
  • [DRBSD-1] Dingwen Tao, Sheng Di, Zizhong Chen, and Franck Capello. “Exploration of Pattern-Matching Techniques for Lossy Compression on Cosmology Simulation Data Sets.” The 1st International Workshop on Data Reduction for Big Scientific Data (DRBSD-11) in Conjunction with ISC’17, Frankfurt, Germany, June 22, 2017.
  • [TPDS] Sheng Di and Franck Cappello. “Optimization of Error-Bounded Lossy Compression for Hard-to-Compress HPC Data.” IEEE Transactions on Parallel and Distributed System 29, no. 1 (2017): 129-143.

2018

  • [HPDC’18] Dingwen Tao, Sheng Di, Xin Liang, Zizhong Chen, and Franck Cappello. “Improving performance of iterative methods by lossy checkponting.” The 27th International Symposium on High-Performance Parallel and Distributed Computing (HPDC), pp. 52-65. 2018.
  • [BigData’18] Xin Liang, Sheng Di, Dingwen Tao, Sihuan Li, Shaomeng Li, Hanqi Guo, Zizhong Chen, and Franck Cappello. “Error-controlled lossy compression optimized for high compression ratios of scientific datasets.” 2018 IEEE International Conference on Big Data (BigData), pp. 438-447. IEEE, 2018.
  • [BigData’18] Sihuan Li, Sheng Di, Xin Liang, Zizhong Chen, Franck Cappello. “Optimizing Lossy Compression with Adjacent Snapshots for N-body Simulation.” 2018 IEEE International Conference on Big Data (BigData), pp. 428-437. IEEE, 2018.
  • [CLUSTER’18] Ali Murat Gok, Sheng Di, Yuri Alexeev, Dingwen Tao, Vladimir Mironov, Xin Liang, and Franck Cappello. “Pastri: Error-bounded lossy compression for two-electron integrals in quantum chemistry.” 2018 IEEE international conference on cluster computing (CLUSTER), pp. 1-11. IEEE, 2018. Best Paper Award
  • [CLUSTER’18] Xin Liang, Sheng Di, Dingwen Tao, Zizhong Chen, and Franck Cappello. “An efficient transformation scheme for lossy data compression with point-wise relative error bound.” 2018 IEEE International Conference on Cluster Computing (CLUSTER), pp. 179-189. IEEE, 2018. Best Area Paper Award
  • [CLUSTER’18] Dingwen Tao, Sheng Di, Xin Liang, Zizhong Chen, and Franck Cappello. “Fixed-psnr lossy compression for scientific data.” 2018 IEEE International Conference on Cluster Computing (CLUSTER), pp. 314-318. IEEE, 2018.
  • [PMES’18] Xin-Chuan Wu, Sheng Di, Franck Cappello, Hal Finkel, Yuri Alexeev, Frederic T. Chong. “Memory-Efficient Quantum Circuit Simulation by Using Lossy Data Compression.” The 3rd International Workshop on Post-Moore Era Supercomputing (PMES), in conjunction with the International Conference for High Performance computing, Networking, Storage and Analysis (SC).
  • [TPDS] Sheng Di, Dingwen Tao, Xin Liang, and Franck Cappello. “Efficient lossy compression for scientific data based on pointwise relative error bound.” IEEE Transactions on Parallel and Distributed Systems 30, no. 2 (2018): 331-345.
  • [DRBSD-4] Xin Liang, Sheng Di, Dingwen Tao, Sihuan Li, Zizhong Chen, Franck Cappello. “Improving In-situ Lossy Compression with Spatio-Temporal Decimation based on SZ Model.” The 4th International Workshop on Data Reduction for Big Scientific Data (DRBSD-4), in conjunction with the International Conference for High Performance computing, Networking, Storage and Analysis (SC).
  • [DRBSD-4] Xin-Chuan Wu, Sheng Di, Franck Cappello, Hal Finkel, Yuri Alexeev, Frederic T. Chong. “Amplitude-Aware Lossy Compression for Quantum Circuit Simulation.” The 4th International Workshop on Data Reduction for Big Scientific Data (DRBSD-4), in conjunction with the International Conference for High Performance computing, Networking, Storage and Analysis (SC).

2019

  • [HPDC’19] Sian Jin, Sheng Di, Xin Liang, Jiannan Tian, Dingwen Tao, and Franck Cappello. “Deepsz: A novel framework to compress deep neural networks by using error-bounded lossy compression.” The 28th International Symposium on High-Performance Parallel and Distributed Computing (HPDC), pp. 159-170. 2019. https://github.com/szcompressor/deepsz
  • [MSST’19] Xiangyu Zou, Tao Lu, Wen Xia, Xuan Wang, Weizhe Zhang, Sheng Di, Dingwen Tao, and Franck Cappello. “Accelerating relative-error bounded lossy compression for hpc datasets with precomputation-based mechanisms.” The 35th Symposium on Mass Storage Systems and Technologies (MSST), pp. 65-78. IEEE, 2019.
  • [FCCM’19] Xiong, Qingqing, Rushi Patel, Chen Yang, Tong Geng, Anthony Skjellum, and Martin C. Herbordt. “Ghostsz: A transparent fpga-accelerated lossy compression framework.” The 27th IEEE Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), pp. 258-266. IEEE, 2019.
  • [CLUSTER’19] Xin Liang, Sheng Di, Dingwen Tao, Sihuan Li, Bogdan Nicolae, Zizhong Chen, and Franck Cappello. “Improving performance of data dumping with lossy compression for scientific simulation.” In 2019 IEEE International Conference on Cluster Computing (CLUSTER), pp. 1-11. IEEE, 2019.
  • [CLUSTER’19] Pavlo Triantafyllides, Tasmia Reza, and Jon C. Calhoun. “Analyzing the Impact of Lossy Compressor Variability on Checkpointing Scientific Simulations.” 2019 IEEE International Conference on Cluster Computing (CLUSTER), pp. 1-5. IEEE, 2019.
  • [SC’19] Xin Liang, Sheng Di, Sihuan Li, Dingwen Tao, Bogdan Nicolae, Zizhong Chen, and Franck Cappello. “Significantly improving lossy compression quality based on an optimized hybrid prediction model.” The International Conference for High Performance Computing, Networking, Storage and Analysis (SC), pp. 1-26. 2019.
  • [SC’19] Xin-Chuan Wu, Sheng Di, Emma Maitreyee Dasgupta, Franck Cappello, Hal Finkel, Yuri Alexeev, and Frederic T. Chong. “Full-state quantum circuit simulation by using data compression.” The International Conference for High Performance Computing, Networking, Storage and Analysis (SC), pp. 1-24. 2019.
  • [HPCC’19] Zou, Xiangyu, Tao Lu, Sheng Di, Dingwen Tao, Wen Xia, Xuan Wang, Weizhe Zhang, and Qing Liao. “Accelerating Lossy Compression on HPC datasets via Partitioning Computation for Parallel Processing.” 2019 IEEE 21st International Conference on High Performance Computing and Communications(HPCC), pp. 1791-1797. IEEE, 2019.
  • [TPDS] Dingwen Tao, Sheng Di, Xin Liang, Zizhong Chen, and Franck Cappello. “Optimizing lossy compression rate-distortion from automatic online selection between sz and zfp.” IEEE Transactions on Parallel and Distributed Systems 30, no. 8 (2019): 1857-1871.
  • [IJHPCA] Franck Cappello, Sheng Di, Sihuan Li, Xin Liang, Ali Murat Gok, Dingwen Tao, Chun Hong Yoon, Xin-Chuan Wu, Yuri Alexeev, and Frederic T. Chong. “Use cases of lossy compression for floating-point data in scientific data sets.” The International Journal of High Performance Computing Applications 33, no. 6 (2019): 1201-1220.
  • [DRBSD-5] Tasmia Reza, Kristopher Keipert, Sheng Di, Xin Liang, Jon C. Calhoun, Franck Cappello. “Analyzing the Performance and Accuracy of LossyCheckpointing on Sub-iteration of NWChem.” The 5th International Workshop on Data Reduction for Big Scientific Data (DRBSD-5), in conjunction with the International Conference for High Performance computing, Networking, Storage and Analysis (SC).

2020

  • [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.” The 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, pp. 74-88. 2020. https://github.com/szcompressor/SZ_HLS
  • [HPDC’20] Kai Zhao, Sheng Di, Xin Liang, Sihuan Li, Dingwen Tao, Zizhong Chen, and Franck Cappello. “Significantly improving lossy compression for hpc datasets with second-order prediction and parameter optimization.” The 29th International Symposium on High-Performance Parallel and Distributed Computing (HPDC), pp. 89-100. 2020. https://github.com/szcompressor/SZauto
  • [PacificVis’20] Xin Liang, Hanqi Guo, Sheng Di, Franck Cappello, Mukund Raj, Chunhui Liu, Kenji Ono, Zizhong Chen, and Tom Peterka. “Toward Feature-Preserving 2D and 3D Vector Field Compression.” The 13th IEEE Pacific Visualization Symposium (PacificVis), pp. 81-90. 2020. https://github.com/szcompressor/cpSZ
  • [IPDPS’20] Robert Underwood, Sheng Di, Jon Calhoun, and Franck Cappello. “FRaZ: A Generic High-Fidelity Fixed-Ratio Lossy Compression Framework for Scientific Floating-point Data.” 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 567-577. IEEE, 2020.
  • [IPDPS’20] Sian Jin, Pascal Grosset, Christopher M Biwer, Jesus Pulido, Jiannan Tian, Dingwen Tao, and James Ahrens. “Understanding GPU-Based Lossy Compression for Extreme-Scale Cosmological Simulations.” 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 105-115. IEEE, 2020.
  • [PACT’20] Jiannan Tian, Sheng Di, Kai Zhao, Cody Rivera, Megan Hickman, Robert Underwood, Sian Jin, Xin Liang, Jon Calhoun, Dingwen Tao, and Franck Cappello. “cuSZ: An Efficient GPU Based Error-Bounded Lossy Compression Framework for Scientific Data.” The 29th International Conference on Parallel Architectures and Compilation Techniques (PACT), 2020. https://github.com/szcompressor/cuSZ
  • [SMC’20] Franck Cappello, Sheng Di, Ali M. Gok. “Fulfilling the Promises of Lossy Compression forScientific Applications.” Smoky Mountain Computational Science and Engineering Conference (SMC), 2020.
  • [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 Similarity.” The 49th International Conference on Parallel Processing (ICPP), Article 40, 1–12. ACM, 2020.
  • [CLUSTER’20] Sihuan Li, Sheng Di, Kai Zhao, Xin Liang, Zizhong Chen, and Franck Cappello. “Towards End-to-end SDC Detection for HPC Applications Equipped with Lossy Compression.” 2020 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2019.
  • [TPDS] Xiangyu Zou, Tao Lu, Wen Xia, Xuan Wang, Weizhe Zhang, Haijun Zhang, Sheng Di, Dingwen Tao, and Franck Cappello. “Performance Optimization for Relative-Error-Bounded Lossy Compression on Scientific Data.” IEEE Transactions on Parallel and Distributed Systems 31, no. 7 (2020): 1665-1680.
  • [BioRxiv] Chandak, Shubham, Kedar Tatwawadi, Srivatsan Sridhar, and Tsachy Weissman. “Impact of lossy compression of nanopore raw signal data on basecall and consensus accuracy.” BioRxiv (2020).
  • [Neurocomputing] Azar, Joseph, Abdallah Makhoul, Raphaël Couturier, and Jacques Demerjian. “Robust IoT Time Series Classification with Data Compression and Deep Learning.” Neurocomputing (2020).
  • [TACO] Eldstål-Ahrens, Albin, and Ioannis Sourdis. “MemSZ: Squeezing Memory Traffic with Lossy Compression.” ACM Transactions on Architecture and Code Optimization (TACO) 17, no. 4 (2020): 1-25.

2021

  • [IPDPS’21] Jiannan Tian, Cody Rivera, Sheng Di, Jieyang Chen, Xin Liang, Dingwen Tao, and Franck Cappello. “Revisiting Huffman Coding: Toward Extreme Performance on Modern GPU Architectures.” 2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 881-891, 2021.
  • [ICDE’21] Kai Zhao, Sheng Di, Maxim Dmitriev, Thierry-Laurent D. Tonellot, Zizhong Chen, and Franck Cappello. “Optimizing Error-Bounded Lossy Compression for Scientific Data by Dynamic Spline Interpolation.” 2021 IEEE International Conference on Data Engineering (ICDE), Chania, Crete, Greece, pp. 1643-1654, 2021.
  • [PPoPP’21] Sian Jin, Guanpeng Li, Shuaiwen Leon Song, Dingwen Tao. “A Novel Memory-Efficient Deep Learning Training Framework via Error-Bounded Lossy Compression.” The 26th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP), pp. 485-487, 2021.
  • [HPDC’21] Sian Jin, Jesus Pulido, Pascal Grosset, Jiannan Tian, Dingwen Tao, and James Ahrens. “Adaptive Configuration of In Situ Lossy Compression for Cosmology Simulations via Fine-Grained Rate-Quality Modeling.” The 30th ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC), pp. 45-56. 2021.
  • [HPDC’21] Dakota Fulp, Alexandra Poulos, Robert Underwood, and Jon C. Calhoun. “ARC: An Automated Approach to Resiliency for Lossy Compressed Data via Error Correcting Codes.” The 30th International Symposium on High-Performance Parallel and Distributed Computing (HPDC), pp. 57–68. 2021.
  • [SC’21] Sihuan Li, Sheng Di, Kai Zhao, Xin Liang, Zizhong Chen, and Franck Cappello. “Resilient Error-bounded Lossy compressor for Data Transfer.” The 33rd ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis (SC), St. Louis, Missouri, USA, pp. 1-14, 2021.
  • [CLUSTER’21] Jiannan Tian, Sheng Di, Xiaodong Yu, Cody Rivera, Kai Zhao, Sian Jin, Yunhe Feng, Xin Liang, Dingwen Tao, Franck Cappello. “Optimizing Error-Bounded Lossy Compression for Scientific Data on GPUs.” 2021 IEEE International Conference on Cluster Computing (CLUSTER), pp. 283-203, 2021.
  • [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.” In 2021 IEEE International Conference on Cluster Computing (CLUSTER), pp. 294-306, 2021.
  • [CLUSTER’21] Xiaodong Yu, Sheng Di, Ali Murat Gok, Dingwen Tao, Franck Cappello. “cuZ-Checker: A GPU-Based Ultra-Fast Assessment System for Lossy Compressions.” 2021 IEEE International Conference on Cluster Computing (Cluster), pp. 307-319, 2021.
  • [HPEC’21] Ruiwen Shan, Sheng Di, Jon C. Calhoun, Franck Cappello. “Towards Combining Error-bounded Lossy Compression and Cryptography for Scientific Data.” in 2021 IEEE High Performance Extreme Computing (HPEC), pp. 1-7, 2021.
  • [HiPC’21] Yuanjian Liu, Sheng Di, Kai Zhao, Sian Jin, Cheng Wang, Kyle Chard, Ian Foster, Dingwen Tao, Frank Cappello. “Optimizing Multi-Range based Error-Bounded Lossy Compression for Scientific Datasets.” 2021 IEEE 28th International Conference on High Performance Computing, Data, and Analytics (HiPC), 2021.
  • [DRBSD-7] David Krasowska, Julie Bessac, Robert Underwood, Jon C. Calhone, Sheng Di, Franck Cappello. “Exploring Lossy Compressibility through Statistical Correlations of Scientific Datasets.” The 7th International Workshop on Data Reduction for Big Scientific Data (DRBSD-7), in conjunction with IEEE/ACM 29th The International Conference for High Performance computing, Networking, Storage and Analysis (SC), 2021.
  • [DRBSD-7] Yuanjian Liu, Sheng Di, Kai Zhao, Sian Jin, Cheng Wang, Kyle Chard, Dingwen Tao, Ian Foster, Franck Cappello. “Understanding Effectiveness of Multi-error-bounded Lossy Compression for Preserving Ranges of Interest in Scientific Analysis.” The 7th International Workshop on Data Reduction for Big Scientific Data (DRBSD-7), in conjunction with IEEE/ACM 29th The International Conference for High Performance computing, Networking, Storage and Analysis (SC), 2021.
  • [DRBSD-7] Robert Underwood, Victoriana Malvoso, Jon C. Calhone, Sheng Di, Franck Cappello. “Productive and Performant Generic Lossy Data Compression with LibPressio.” The 7th International Workshop on Data Reduction for Big Scientific Data (DRBSD-7), in conjunction with IEEE/ACM 29th The International Conference for High Performance computing, Networking, Storage and Analysis (SC), 2021.

2022

  • [VLDB’22] Sian Jin, Chengming Zhang, Jiannan Tian, Yunhe Feng, Hui Guan, Guanpeng Li, Shuaiwen Leon Song, Dingwen Tao. “COMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compression.” The 48th International Conference on Very Large Data Bases (VLDB), Australia, September 5-9, 2022.
  • [ICDE’22] Sian Jin, Sheng Di, Suren Byna, Dingwen Tao, Franck Cappello. “Significantly Improving Prediction-Based Lossy Compression via Ratio-Quality Modelin.” 2022 IEEE International Conference on Data Engineering (ICDE), 2022.
  • [ICDE’22] Kai Zhao, Sheng Di, Danny Perez, Xin Liang, Zizhong Chen, Franck Cappello. “MDZ: An Efficient Error-bounded Lossy Compressor for Molecular Dynamics Simulations of Materials.” 2022 IEEE International Conference on Data Engineering (ICDE), 2022.
  • [IPDPS’22] Cody Rivera, Sheng Di, Xiaoding Yu, Jiannan Tian, Dingwen Tao, and Franck Cappello. “Optimizing Huffman Decoding for Error-Bounded Lossy Compression on GPUs.” 2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2022.
  • [ICS’22] Chengming Zhang, Sian Jin, Tong Geng, Jiannan Tian, Ang Li, Dingwen Tao. “CEAZ: Accelerating Parallel I/O via Hardware-Algorithm Co-Designed Adaptive Lossy Compression.” The 36th ACM International Conference on Supercomputing (ICS), 2022.
  • [HPDC’22] Xiaodong Yu, Sheng Di, Kai Zhao, Jiannan Tian, Dingwen Tao, Xin Liang, Franck Cappello. “Ultra-fast Error-bounded Lossy Compression for Scientific Dataset.” The 31st ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC), 2022.
  • [HPDC’22] Daoce Wang, Jesus Pulido, Pascal Grosset, Jiannan Tian, James Ahrens, Dingwen Tao. “Optimizing Error-Bounded Lossy Compression for Three Dimensional Adaptive Mesh Refinement Simulations.” The 31st ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC), 2022.
  • [SC’22] Sian Jin, Dingwen Tao, Houjun Tang, Sheng Di, Suren Byna, Zarija Lukic, and Franck Cappello. “Accelerating Parallel Write via Deeply Integrating Predictive Lossy Compression with HDF5.” The International Conference for High Performance Computing, Networking, Storage and Analysis (SC), 2022.
  • [TPDS] Robert Underwood, Jon C. Calhoun, Sheng Di, Amy Apon, Franck Cappello. “OptZConfig: Efficient Parallel Optimization of Lossy Compression Configuration.” IEEE Transactions on Distributed and Computer Systems (TPDS), 2022.
  • [TPDS] 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 (TPDS), 2022.

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