Seuraa
Sian Jin
Sian Jin
Vahvistettu sähköpostiosoite verkkotunnuksessa temple.edu - Kotisivu
Nimike
Viittaukset
Viittaukset
Vuosi
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
692020
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
642019
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
262021
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
232020
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
202021
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
192022
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
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
162021
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
122021
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
102022
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
102021
Pascal Grosset, Christopher M Biwer, Jesus Pulido, Jiannan Tian, Dingwen Tao, and James Ahrens. Understanding gpu-based lossy compression for extreme-scale cosmological simulations
S Jin
2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS …, 2020
102020
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
2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS …, 2020
92020
Characterizing Impacts of Storage Faults on HPC Applications: A Methodology and Insights
B Fang, D Wang, S Jin, Q Koziol, Z Zhang, Q Guan, S Byna, ...
2021 IEEE International Conference on Cluster Computing (CLUSTER), 409-420, 2021
72021
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
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
Proceedings of the 30th ACM International Symposium on High-Performance …, 2021
62021
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
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Artikkelit 1–20