PyTorch: An Imperative Style, High-Performance Deep Learning Library A Paszke, S Gross, F Massa, A Lerer, J Bradbury, G Chanan, T Killeen, ... Advances in neural information processing systems, 8026-8037, 2019 | 50475 | 2019 |
Advances in Neural Information Processing Systems 32 A Paszke, S Gross, F Massa, A Lerer, J Bradbury, G Chanan, T Killeen, ... Curran Associates, Inc, 8024-8035, 2019 | 1576 | 2019 |
Onnx: Open neural network exchange J Bai, L Fang, K Zhang GitHub repository, https://github.com/onnx/onnx/, 2017 | 454 | 2017 |
Pytorch: An imperative style, high-performance deep learning library, 2019 A Paszke, S Gross, F Massa, A Lerer, J Bradbury, G Chanan, T Killeen, ... arXiv preprint arXiv:1912.01703 10, 1912 | 251 | 1912 |
Yak: A {High-Performance}{Big-Data-Friendly} Garbage Collector K Nguyen, L Fang, G Xu, B Demsky, S Lu, S Alamian, O Mutlu 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI …, 2016 | 146 | 2016 |
Facade: A compiler and runtime for (almost) object-bounded big data applications K Nguyen, K Wang, Y Bu, L Fang, J Hu, G Xu ACM SIGARCH Computer Architecture News 43 (1), 675-690, 2015 | 125 | 2015 |
Interruptible tasks: Treating memory pressure as interrupts for highly scalable data-parallel programs L Fang, K Nguyen, G Xu, B Demsky, S Lu Proceedings of the 25th Symposium on Operating Systems Principles, 394-409, 2015 | 70 | 2015 |
Pytorch: An imperative style, high-performance deep learning library. arXiv 2019 A Paszke, S Gross, F Massa, A Lerer, J Bradbury, G Chanan, T Killeen, ... arXiv preprint arXiv:1912.01703, 2019 | 65* | 2019 |
Skyway: Connecting managed heaps in distributed big data systems K Nguyen, L Fang, C Navasca, G Xu, B Demsky, S Lu ACM SIGPLAN Notices 53 (2), 56-69, 2018 | 60 | 2018 |
Advances in Neural Information Processing Systems 32 ed H A Paszke, S Gross, F Massa, A Lerer, J Bradbury, G Chanan, T Killeen, ... Wallach et al 8024, 2019 | 57 | 2019 |
Pytorch: An imperative style, high-performance deep learning library B Steiner, Z DeVito, S Chintala, S Gross, A Paske, F Massa, A Lerer, ... | 55* | 2019 |
APIExample: An effective web search based usage example recommendation system for Java APIs L Wang, L Fang, L Wang, G Li, B Xie, F Yang 2011 26th IEEE/ACM International Conference on Automated Software …, 2011 | 51 | 2011 |
Towards automatic tagging for web services L Fang, L Wang, M Li, J Zhao, Y Zou, L Shao 2012 IEEE 19th International Conference on Web Services, 528-535, 2012 | 45 | 2012 |
Perfblower: Quickly detecting memory-related performance problems via amplification L Fang, L Dou, G Xu 29th European Conference on Object-Oriented Programming (ECOOP 2015), 2015 | 31 | 2015 |
Speculative region-based memory management for big data systems K Nguyen, L Fang, G Xu, B Demsky Proceedings of the 8th workshop on programming languages and operating …, 2015 | 14 | 2015 |
Low-overhead and fully automated statistical debugging with abstraction refinement Z Zuo, L Fang, SC Khoo, G Xu, S Lu Proceedings of the 2016 ACM SIGPLAN International Conference on Object …, 2016 | 11 | 2016 |
An exploratory study of API usage examples on the web L Wang, Y Zou, L Fang, B Xie, F Yang 2012 19th Asia-Pacific Software Engineering Conference 1, 396-405, 2012 | 10 | 2012 |
Understanding and combating memory bloat in managed data-intensive systems K Nguyen, K Wang, Y Bu, L Fang, G Xu ACM Transactions on Software Engineering and Methodology (TOSEM) 26 (4), 1-41, 2018 | 9 | 2018 |
Toward More Efficient Statistical Debugging with Abstraction Refinement Z Zuo, X Niu, S Zhang, L Fang, SC Khoo, S Lu, C Sun, GH Xu ACM Transactions on Software Engineering and Methodology 32 (2), 1-38, 2023 | 1 | 2023 |
Detecting and Fixing Memory-Related Performance Problems in Managed Languages L Fang University of California, Irvine, 2017 | | 2017 |