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Sian Jin
Sian Jin
在 temple.edu 的电子邮件经过验证 - 首页
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Cusz: An efficient gpu-based error-bounded lossy compression framework for scientific data
J Tian, S Di, K Zhao, C Rivera, MH Fulp, R Underwood, S Jin, X Liang, ...
Proceedings of the ACM International Conference on Parallel Architectures …, 2020
702020
DeepSZ: A novel framework to compress deep neural networks by using error-bounded lossy compression
S Jin, S Di, X Liang, J Tian, D Tao, F Cappello
Proceedings of the 28th international symposium on high-performance parallel …, 2019
652019
Understanding GPU-based lossy compression for extreme-scale cosmological simulations
S Jin, P Grosset, CM Biwer, J Pulido, J Tian, D Tao, J Ahrens
2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS …, 2020
382020
Exploring autoencoder-based error-bounded compression for scientific data
J Liu, S Di, K Zhao, S Jin, D Tao, X Liang, Z Chen, F Cappello
2021 IEEE International Conference on Cluster Computing (CLUSTER), 294-306, 2021
312021
Wavesz: A hardware-algorithm co-design of efficient lossy compression for scientific data
J Tian, S Di, C Zhang, X Liang, S Jin, D Cheng, D Tao, F Cappello
Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of …, 2020
252020
Optimizing error-bounded lossy compression for scientific data on gpus
J Tian, S Di, X Yu, C Rivera, K Zhao, S Jin, Y Feng, X Liang, D Tao, ...
2021 IEEE International Conference on Cluster Computing (CLUSTER), 283-293, 2021
222021
Improving prediction-based lossy compression dramatically via ratio-quality modeling
S Jin, S Di, J Tian, S Byna, D Tao, F Cappello
2022 IEEE 38th International Conference on Data Engineering (ICDE), 2494-2507, 2022
202022
Pascal Grosset, Christopher M Biwer, Jesus Pulido, Jiannan Tian, Dingwen Tao, and James Ahrens. 2020. Understanding GPU-Based Lossy Compression for Extreme-Scale Cosmological …
S Jin
arXiv preprint arXiv:2004.00224, 2020
202020
Clicktrain: Efficient and accurate end-to-end deep learning training via fine-grained architecture-preserving pruning
C Zhang, G Yuan, W Niu, J Tian, S Jin, D Zhuang, Z Jiang, Y Wang, B Ren, ...
Proceedings of the ACM international conference on supercomputing, 266-278, 2021
172021
Comet: a novel memory-efficient deep learning training framework by using error-bounded lossy compression
S Jin, C Zhang, X Jiang, Y Feng, H Guan, G Li, SL Song, D Tao
arXiv preprint arXiv:2111.09562, 2021
162021
Delta-DNN: Efficiently compressing deep neural networks via exploiting floats similarity
Z Hu, X Zou, W Xia, S Jin, D Tao, Y Liu, W Zhang, Z Zhang
Proceedings of the 49th International Conference on Parallel Processing, 1-12, 2020
122020
Accelerating parallel write via deeply integrating predictive lossy compression with HDF5
S Jin, D Tao, H Tang, S Di, S Byna, Z Lukic, F Cappello
SC22: International Conference for High Performance Computing, Networking …, 2022
112022
Adaptive configuration of in situ lossy compression for cosmology simulations via fine-grained rate-quality modeling
S Jin, J Pulido, P Grosset, J Tian, D Tao, J Ahrens
Proceedings of the 30th International Symposium on High-Performance Parallel …, 2021
112021
A novel memory-efficient deep learning training framework via error-bounded lossy compression
S Jin, G Li, SL Song, D Tao
Proceedings of the 26th ACM SIGPLAN Symposium on Principles and Practice of …, 2021
112021
Pascal Grosset, Jiannan Tian, Dingwen Tao, and James Ahrens. 2021. Adaptive configuration of in situ lossy compression for cosmology simulations via fine-grained rate-quality …
S Jin, J Pulido
arXiv preprint arXiv:2104.00178, 2021
102021
Concealing compression-accelerated i/o for hpc applications through in situ task scheduling
S Jin, S Di, F Vivien, D Wang, Y Robert, D Tao, F Cappello
EuroSys 2024, 2024
62024
Ceaz: accelerating parallel i/o via hardware-algorithm co-designed adaptive lossy compression
C Zhang, S Jin, T Geng, J Tian, A Li, D Tao
Proceedings of the 36th ACM International Conference on Supercomputing, 1-13, 2022
62022
Amric: A novel in situ lossy compression framework for efficient i/o in adaptive mesh refinement applications
D Wang, J Pulido, P Grosset, J Tian, S Jin, H Tang, J Sexton, S Di, K Zhao, ...
Proceedings of the International Conference for High Performance Computing …, 2023
52023
Design of a quantization-based dnn delta compression framework for model snapshots and federated learning
H Jin, D Wu, S Zhang, X Zou, S Jin, D Tao, Q Liao, W Xia
IEEE Transactions on Parallel and Distributed Systems 34 (3), 923-937, 2023
52023
Optimizing Error-Bounded Lossy Compression for Scientific Data With Diverse Constraints
Y Liu, S Di, K Zhao, S Jin, C Wang, K Chard, D Tao, I Foster, F Cappello
IEEE Transactions on Parallel and Distributed Systems 33 (12), 4440-4457, 2022
52022
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