| Automated performance modeling of HPC applications using machine learning J Sun, G Sun, S Zhan, J Zhang, Y Chen IEEE Transactions on Computers 69 (5), 749-763, 2020 | 33 | 2020 |
| Adasam: Boosting sharpness-aware minimization with adaptive learning rate and momentum for training deep neural networks H Sun, L Shen, Q Zhong, L Ding, S Chen, J Sun, J Li, G Sun, D Tao Neural Networks 169, 506-519, 2024 | 23 | 2024 |
| Lossy compression of communication traces using recurrent neural networks J Sun, T Yan, H Sun, H Lin, G Sun IEEE Transactions on Parallel and Distributed Systems 33 (11), 3106-3116, 2021 | 9 | 2021 |
| Performance analysis of graph neural network frameworks J Wu, J Sun, H Sun, G Sun 2021 IEEE International Symposium on Performance Analysis of Systems and …, 2021 | 9 | 2021 |
| Latency-aware automatic CNN channel pruning with GPU runtime analysis J Liu, J Sun, Z Xu, G Sun BenchCouncil Transactions on Benchmarks, Standards and Evaluations 1 (1), 100009, 2021 | 8 | 2021 |
| SPLZ: An efficient algorithm for single source shortest path problem using compression method J Sun, G Sun GeoInformatica 20, 1-18, 2016 | 8 | 2016 |
| An efficient channel-level pruning for CNNs without fine-tuning Z Xu, J Sun, Y Liu, G Sun 2021 International Joint Conference on Neural Networks (IJCNN), 1-8, 2021 | 6 | 2021 |
| Automated performance modeling based on runtime feature detection and machine learning J Sun, S Zhan, G Sun, Y Chen 2017 IEEE International Symposium on Parallel and Distributed Processing …, 2017 | 6 | 2017 |
| An active learning method for empirical modeling in performance tuning J Zhang, J Sun, W Zhou, G Sun 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS …, 2020 | 5 | 2020 |
| Using small-scale history data to predict large-scale performance of hpc application W Zhou, J Zhang, J Sun, G Sun 2020 IEEE International Parallel and Distributed Processing Symposium …, 2020 | 4 | 2020 |
| Constructing skeleton for parallel applications with machine learning methods Z Zhang, J Sun, J Zhang, Y Qin, G Sun Workshop Proceedings of the 48th International Conference on Parallel …, 2019 | 4 | 2019 |
| GPU Occupancy Prediction of Deep Learning Models Using Graph Neural Network H Mei, H Qu, J Sun, Y Gao, H Lin, G Sun 2023 IEEE International Conference on Cluster Computing (CLUSTER), 318-329, 2023 | 1 | 2023 |
| FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data H Sun, L Shen, S Chen, J Sun, J Li, G Sun, D Tao arXiv preprint arXiv:2309.09719, 2023 | 1 | 2023 |
| Accelerating GNN inference by soft channel pruning W Zhang, J Sun, G Sun 2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms …, 2022 | 1 | 2022 |
| Performance characterization and optimization of pruning patterns for sparse DNN inference Y Liu, J Sun, J Liu, G Sun BenchCouncil Transactions on Benchmarks, Standards and Evaluations 2 (4), 100090, 2022 | 1 | 2022 |
| Structured Pruning for Large Language Models Using Coupled Components Elimination and Minor Fine-tuning H Zhang, XS XiaolongShi, J Sun, G Sun Findings of the Association for Computational Linguistics: NAACL 2024, 1-12, 2024 | | 2024 |
| AG-SpTRSV: An Automatic Framework to Optimize Sparse Triangular Solve on GPUs Z Hu, J Sun, Z Li, G Sun ACM Transactions on Architecture and Code Optimization, 2024 | | 2024 |
| Physically plausible and conservative solutions to Navier-Stokes equations using Physics-Informed CNNs J Li, L Zhou, J Sun, G Sun | | 2024 |
| Automated HPC Workload Generation Combining Statistical Modeling and Autoregressive Analysis Z Zhou, J Sun, G Sun International Symposium on Benchmarking, Measuring and Optimization, 153-170, 2023 | | 2023 |
| EC-SpMM: Efficient Compilation of SpMM Kernel on GPUs J Lin, H Zhang, X Shi, J Sun, X Yu, J Yao, G Sun Proceedings of the 52nd International Conference on Parallel Processing, 21-30, 2023 | | 2023 |
