I am an enthusiastic computer science researcher. I am fascinated by the challenges in algorithm, system software, middleware, and architecture of High-Performance Computing (HPC), especially the ones raised during the accelerations of real-world scientific and AI applications.
I have been working for Argonne since 2019. I got my Ph.D. degree in Computer Science from Virginia Tech. I have authored more than 20 research papers published in highly-regarded conferences and journals, such as HPDC, ICS, IPDPS, CLUSTER, and JSAC. I also have served as the Technical Program Committee (TPC) member, AD/AE Committee member, or reviewer for CS conferences and transactions, including SC, TPDS, ISPASS, HotI, and HPCC.
I am moving to The Department of Computer Science at Stevens Institute of Technology in 2023Fall as a tenure-track assistant professor. I am looking for five PhD students and multiple Master's students with strong self-motivations to join my lab to work on HPC/large-scale machine learning/AI accelerators. The PhD students will be fully-funded throughout the degree study. Please drop me an email with your CV and a brief self-introduction in the text-body if you are interested.
Advisor: Prof. Danfeng (Daphne) Yao
Tentative dissertation title: Challenges, Algorithms, and Frameworks for Accelerating Security Applications on High-Performance Computing Platforms
Thesis committee: Prof. Danfeng (Daphne) Yao (Chair), Prof. Michela Becchi (NCSU), Prof. Ali Butt, Prof. Matthew Hicks, Prof. Xinming (Simon) Ou (USF)
Designing benchmark suite for next-generation AI hardware (LDRD and FAIR-SBI).
Optimizing data loading for large-scale surrogate training (FAIR-SBI).
Designing and implementing fast GPU-based lossy compressors for scientific data (VeloC/SZ).
Designing and implementing MPI collective communications with compression (Exascale MPI).
Designing and implementing multi-GPU-based ptychographic image reconstruction (RAVEN).
YAO Group, under the supervision of Prof. Danfeng (Daphne) Yao. Projects:
1. GPU-assisted Android Static Analysis for Security. Supported by ONR grant: Automatic Generation of Anti-Specifications from Exploits for Scalable Program Hardening
2. Cache Timing Side-Channel Attacks. Supported by NSF CISE CSR #1565314
Synergy Lab, under the supervision of Prof. Wu-chun Feng. Projects:
1. High-Performance Automata Processing. Supported by NSF I/UCRC IIP1266245
2. GPU-based CT Image Reconstruction. Supported by NSF CCF-1337131
Help to develop the GPU Deep learning library (MIOpen).
CS2505: Computer Organization I
CS2506: Computer Organization II
NPS Lab, under the supervision of Prof. Michela Becchi. Project:
GPU-based Automata Processing. Supported by NSF award CNS-1216756
Milan Shah, Xiaodong Yu, Sheng Di, Danylo Lykov, Yuri Alexeev, Michela Becchi, Franck Cappello, “GPU-Accelerated Error-Bounded Compression Framework for Quantum Circuit Simulations.” In 2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS), IEEE, 2023 (AR:)
Baixi Sun, Xiaodong Yu, Chengming Zhang, Jiannan Tian, Sian Jin, Kamil Iskra, Tao Zhou, Tekin Bicer, Pete Beckman, Dingwen Tao, “SOLAR: A Highly Optimized Data Loading Framework for Distributed Training of CNN-based Scientific Surrogates.” arXiv preprint arXiv:2211.00224, 2022
Xiaodong Yu, Sheng Di, Kai Zhao, Dingwen Tao, Xin Liang, Franck Cappello, “Ultra-fast Error-bounded Lossy Compression for Scientific Dataset.” In Proceedings of the 31th International Symposium on High-Performance Parallel and Distributed Computing (HPDC), ACM, 2022 (AR:21/108=19.4%)
Xiaodong Yu, Viktor Nikitin, Daniel J. Ching, Selin Aslan, Doga Gursoy, Tekin Bicer, “Scalable and accurate multi-gpu based image reconstruction of large-scale ptychography data.” In Scientific reports, 2022
Cody Rivera, Sheng Di, Jiannan Tian, Xiaodong Yu, Dingwen Tao, and Franck Cappello, “Optimizing Huffman Decoding for Error-Bounded Lossy Compression on GPUs.” In 2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS), IEEE, 2022 (AR:132/474=27.8%)
Tekin Bicer, Xiaodong Yu, Daniel J. Ching, Ryan Chard, Mathew J. Cherukara, Bogdan Nicolae, Rajkumar Kettimuthu, Ian T. Foster, "High-Performance Ptychographic Reconstruction with Federated Facilities,” In Smoky Mountains Computational Sciences and Engineering Conference (SMC), Springer, Cham, 2021
Xiaodong Yu, Sheng Di, Ali Murat Gok, Dingwen Tao, Franck Cappello, “cuZ-Checker: A GPU-Based Ultra-Fast Assessment System for Lossy Compressions.” In 2021 IEEE International Conference on Cluster Computing (CLUSTER), pp. 307-319. IEEE, 2021 (AR:48/163=29.4%)
Jiannan Tian, Sheng Di, Xiaodong Yu, Kai Zhao, Sian Jin, Yunhe Feng, Xin Liang, Dingwen Tao, Franck Cappello, “Optimizing Error-Bounded Lossy Compression for Scientific Data on GPUs.” In 2021 IEEE International Conference on Cluster Computing (CLUSTER), pp. 283-293. IEEE, 2021 (AR:48/163=29.4%)
Xiaodong Yu, Tekin Bicer, Rajkumar Kettimuthu, Ian T. Foster, “Topology-aware Optimizations for Multi-GPU Ptychographic Image Reconstruction,” In 2021 ACM International Conference on Supercomputing (ICS), June 14 - 17, 2021. Worldwide online event (AR:38/157=24.2%)
Xiaodong Yu, Fengguo Wei, Xinming Ou, Michela Becchi, Tekin Bicer, Danfeng Yao, “GPU-Based Static Data-Flow Analysis for Fast and Scalable Android App Vetting,” In 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS), May 18 pp. 274-284 (AR:110/446=24.7%) [PDF]
Xiaodong Yu, “Algorithms and Frameworks for Accelerating Security Applications on HPC Platforms,” In Virginia Tech (PhD Dissertation) [PDF]
Xiaodong Yu, Ya Xiao, Kirk Cameron, and Danfeng Yao, “Comparative Measurement of Cache Configurations’ Impacts on Cache Timing Side-Channel Attacks,” In 12th USENIX Workshop on Cyber Security Experimentation and Test (CSET) (AR:19/61=31.1%) co-located with USENIX Security '19 [PDF]
Xiaodong Yu, Hao Wang, Wu-chun Feng, Hao Gong, and Guohua Cao, “GPU-Based Iterative Medical CT Image Reconstructions,” In Journal of Signal Processing Systems (Springer) (JSPS '18) (IF:1.013)
Thomas C. H. Lux, Layne T. Watson, Tyler H. Chang, Jon Bernard, Bo Li, Xiaodong Yu, Li Xu, Godmar Back, Ali R. Butt, Kirk W. Cameron, Yili Hong, Danfeng Yao, “Novel meshes for multivariate interpolation and approximation,” In Proceedings of the ACMSE 2018 Conference (ACMSE '18), Richmond, KY, USA [PDF]
Thomas C. H. Lux, Layne T. Watson, Tyler H. Chang, Jon Bernard, Bo Li, Xiaodong Yu, Li Xu, Godmar Back, Ali R. Butt, Kirk W. Cameron, Yili Hong, Danfeng Yao, “Nonparametric Distribution Models for Predicting and Managing Computational Performance Variability” In the IEEE SoutheastCon 2018 (IEEE SoutheastCon '18), St. Petersburg, FL, USA [PDF]
Xiaodong Yu, Kaixi Hou, Hao Wang, Wu-chun Feng, “Robotomata: A Framework for Approximate Pattern Matching of Big Data on an Automata Processor,” In Proceedings of the 2017 IEEE International Conference on Big Data (IEEE BigData '17), Boston, MA, USA (AR:79/437=18.1%) [PDF]
Marziyeh Nourian, Xiang Wang, Xiaodong Yu, Wu-chun Feng, and Michela Becchi, “Demystifying Automata Processing: GPUs, FPGAs or Micron’s AP?” In Proceedings of the International Conference on Supercomputing (ICS '17). Chicago, Illinois, USA (AR:28/177=15.8%) [PDF]
Xiaodong Yu, Hao Wang, Wu-chun Feng, Hao Gong, and Guohua Cao, “An Enhanced Image Reconstruction Tool for Computed Tomography on GPUs,” In Proceedings of the ACM International Conference on Computing Frontiers (CF '17), Siena, Italy (Full paper AR:27/76=35.5%)
Xiaodong Yu, Hao Wang, Wu-chun Feng, Hao Gong, and Guohua Cao, “cuART: Fine-Grained Algebraic Reconstruction Technique for Computed Tomography Images on GPUs,” 2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid '16). Cartagena, Colombia (Short paper AR:25%)
Xiaodong Yu, Wu-chun Feng, Danfeng Yao, and Michela Becchi, “O3FA: a Finite Automata-based Pattern Matching Engine for Out-of-Order Packets,” In Proceedings of the 2016 Symposium on Architectures for Networking and Communications Systems (ANCS '16). Santa Clara, CA, USA (AR:12/58=20.7%)
Xiaodong Yu, Bill Lin and Michela Becchi, “Revisiting State Blow-up: Automatically Building Augmented-FA while Preserving Functional Equivalence,” In Selected Areas in Communications, IEEE Journal on (JSAC), vol.32, no.10, pp.1822-1833, Oct. 2014 (IF:11.42)
Xiaodong Yu, “Deep packet inspection on large datasets: algorithmic and parallelization techniques for accelerating regular expression matching on many-core processors,” In University of Missouri--Columbia (Master's Thesis) [PDF]
Xiaodong Yu and Michela Becchi, “GPU Acceleration of Regular Expression Matching for Large Datasets: Exploring the Implementation Space,” In Proceedings of the 10th ACM International Conference on Computing Frontiers (CF '13), Ischia, Italy, May 2013
Xiaodong Yu and Michela Becchi, “Exploring Different Automata Representations for Efficient Regular Expression Matching on GPU,” In Proceedings of the 18th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPOPP 2013), Shenzhen, China (poster)