Publications

[Euro-Par'23] Zhen Xie, Siddhisanket Raskar, Murali Emani, and Venkatram Vishwanath, "TrainBF: High-Performance DNN Training Engine using BFloat16 on AI Accelerators." 29th International European Conference on Parallel and Distributed Computing, 2023. Paper
[ExHET'23] Gaurav Verma, Siddhisanket Raskar, Zhen Xie, Abid M. Malik, Murali Emani, and Barbara Chapman, "Transfer Learning Across Heterogeneous Features For Efficient Tensor Program Generation." 2nd International Workshop on Extreme Heterogeneity Solutions, 2023. Paper
[PPoPP'23] Zhen Xie, Jie Liu, Jiajia Li, and Dong Li, "Merchandiser: Data Placement on Heterogeneous Memory for Task-Parallel HPC Applications with Load-Balance Awareness." 27th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming (PPoPP), 2023. Paper
[Gordon Bell'22] Maxim Zvyagin, Alexander Brace, Kyle Hippe, Yuntian Deng, Bin Zhang, Cindy Orozco Bohorquez, Austin Clyde, Bharat Kale, Danilo Perez-Rivera, Heng Ma, Carla M. Mann, Michael Irvin, J. Gregory Pauloski, Logan Ward, Valerie Hayot-Sasson, Murali Emani, Sam Foreman, Zhen Xie, Diangen Lin, Maulik Shukla, Weili Nie, Josh Romero, Christian Dallago, Arash Vahdat, Chaowei Xiao, Thomas Gibbs, Ian Foster, James J. Davis, Michael E. Papka, Thomas Brettin, Rick Stevens, Anima Anandkumar, Venkatram Vishwanath, Arvind Ramanathan, "GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics." Winner of the ACM Gordon Bell Special Prize for HPC-based Covid-19 research, 2022. Paper
[PMBS'22] Murali Emani, Zhen Xie, Sid Raskar, Varuni Sastry, William Arnold, Bruce Wilson, Rajeev Thakur, Venkatram Vishwanath, Michael E Papka, Cindy Orozco Bohorquez, Rick Weisner, Karen Li, Yongning Sheng, Yun Du, Jian Zhang, Alexander Tsyplikhin, Gurdaman Khaira, Jeremy Fowers, Ramakrishnan Sivakumar, Victoria Godsoe, Adrian Macias, Chetan Tekur, Matthew Boyd, "A Comprehensive Evaluation of Novel AI Accelerators for Deep Learning Workloads." 13th IEEE International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS) at SC, 2022. Paper
[IPDPSW'22] Zhen Xie, Siddhisanket Raskar, and Murali Emani, "Throughput-oriented and Accuracy-aware DNN Training with BFloat16 on GPU." at ScaDL workshop at IPDPS, 2022. Paper
[TACO] Bang Di, Daokun Hu, Zhen Xie, Jianhua Sun, Hao Chen, Jinkui Ren, Dong Li, "TLB-pilot: Mitigating TLB Contention Attack on GPUs with Microarchitecture-Aware Scheduling." ACM Transactions on Architecture and Code Optimization (TACO), 2021. Paper
[SEC'21] Jie Liu, Jiawen Liu, Zhen Xie, and Dong Li, "Flame: A Self-Adaptive Auto-Labeling System for Heterogeneous Mobile Processors." ACM/IEEE Symposium on Edge Computing (SEC), 2021. Paper
[TPDS] Zhen Xie, Guangming Tan, Weifeng Liu and Ninghui Sun, "A Pattern Based SpGEMM Library for Multi-core and Many-core Architectures." IEEE Transactions on Parallel and Distributed Systems (TPDS), 2021. Paper
[ICS'21] Zhen Xie, Wenqian Dong, Jie Liu, Ivy Peng, Yanbao Ma, and Dong Li. MD-HM: Memoization-based Molecular Dynamics Simulations on Big Memory System. ACM 35th International Conference on Supercomputing, 2021. (38/157=24.2%) Paper
[ICS'21] Xin He, Jiawen Liu, Zhen Xie, Hao Chen, Guoyang Chen, Weifeng Zhang, and Dong Li. Enabling Energy-Efficient DNN Training on Hybrid GPU-FPGA Accelerators. ACM 35th International Conference on Supercomputing, 2021. (38/157=24.2%) Paper
[EuroSys'21] Zhen Xie, Wenqian Dong, Jiawen Liu, Hang Liu and Dong Li. Tahoe: Tree Structure-Aware High Performance Inference Engine for Decision Tree Ensemble on GPU. ACM 16th European Conference on Computer Systems, 2021. (38/191=19.9%) Paper Slides Video
[SC'20] Wenqian Dong, Zhen Xie, Gokcen Kestor and Dong Li, Smart-PGSim: Using Neural Network to Accelerate AC-OPF Power Grid Simulation. International Conference for High Performance Computing, Networking, Storage and Analysis, 2020. (95/378=25.1%) Paper
[USENIX OpML'20] Jiawen Liu, Zhen Xie, Dimitrios Nikolopoulos, and Dong Li, "RIANN: Real-time Incremental Learning with Approximate Nearest Neighbor on Mobile Devices", USENIX Conference on Operational Machine Learning, 2020. Paper
[MLSys-W'20] Jiawen Liu, Jie Liu, Zhen Xie, and Dong Li, "Flame: A Self-Adaptive Auto-Labeling System for Heterogeneous Mobile Processors", On-Device Intelligence Workshop at Machine Learning and Systems Conference, 2020. Paper
[ICS'19] Zhen Xie, Guangming Tan, Weifeng Liu and Ninghui Sun, "IA-SpGEMM: an Input-aware Auto-tuning Framework for Parallel Sparse Matrix-Matrix Multiplication." ACM 33rd on International Conference on Supercomputing, 2019. (45/193=23.3%) Paper Slides
[SC'19] Wenqian Dong, Jie Liu, Zhen Xie, and Dong Li, "Adaptive neural network-based approximation to accelerate eulerian fluid simulation." International Conference for High Performance Computing, Networking, Storage and Analysis, 2019. (87/344=25.3%) Paper
[ICPADS'16] Zhen Xie, Zheng Cao, Zhan Wang, Dawei Zang, En Shao and Ninghui Sun, "Modeling Traffic of Big Data Platform for Large Scale Datacenter Networks," IEEE 22nd International Conference on Parallel and Distributed Systems, 2016. (123/412=29.9%) Paper
Zhen Xie, Guangming Tan and Ninghui Sun, "PRF : A Process-RAM-Feedback Performance Model to Reveal Bottlenecks and Propose Optimizations." High Technology Letters, 2019. Paper
Zhen Xie, Guangming Tan and Ninghui Sun, Revealing bottlenecks and predicting optimal performance of Sparse Matrix-Vector and Convolution using the Probability-Process-Ram model, Computer Research and Development, 2020. Paper