My publication list in BibTex can be downloaded here.

Google Scholar Profile

Note: Underlined author names indicate students under my advising and * indicates the corresponding author.

Publications in 2025

• Dipak Acharya and Tong Shu^*. PredTOP: Latency Predictor Utilizing DAG Transformers for Distributed Deep Learning Training with Operator Parallelism. In Proc. of IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 712-724, Milano, Italy, Jun 3rd-7th 2025. (Acceptance rate: 24.7% = 105/425) [ Preprint | BibTex ]

Publications in 2024

• Dhroov Pandey, Jonah Ghebremichael, Zongqing Qi, and Tong Shu^*. A Comparative Survey: Reusing Small Pre-Trained Models for Efficient Large Model Training. In Proc. of Workshops of IEEE International Conference for High Performance Computing, Networking, Storage and Analysis (SC-W): 5th Workshop on Artificial Intelligence and Machine Learning for Scientific Applications (AI4S), pp. 56-63, Atlanta, GA, USA, Nov 18th 2024. [ Preprint | BibTex ]
• Dhroov Pandey and Tong Shu^*. AM-DGCNN: Leveraging Graph Attention Networks and Edge Attributes for Link Classification in Knowledge Graphs. In Proc. of Workshops of IEEE International Conference for High Performance Computing, Networking, Storage and Analysis (SC-W): Workshop on Machine Learning with Graphs in High-Performance Computing Environments, pp. 1037-1045, Atlanta, GA, USA, Nov 17th 2024. [ Preprint | BibTex ]
• Amirhossein Nazeri, Denys W. Godwin, Aikaterini Maria Panteleaki, Iraklis Anagnostopoulos, Michael I. Edidem, Ruopu Li, and Tong Shu^*. Exploration of TPU Architectures for the Optimized Transformer in Drainage Crossing Detection. In Proc. of IEEE International Conference on Big Data (BigData): 5th International Workshop on Big Data & AI Tools, Methods, and Use Cases for Innovative Scientific Discovery (BTSD), pp. 4178-4187, Washington DC, USA, Dec 15th 2024. [ Preprint | BibTex ]
• Kalpit M. Vadnerkar, Emmanuela Amen Eze, Rinoj Gautam, Daoru Han, Xin Liang, and Tong Shu. A Deep Learning Approach to Maximizing Electrostatic Sieve Efficiency in Regolith Beneficiation. In Proc. of IEEE International Conference on Big Data (BigData): 5th International Workshop on Big Data & AI Tools, Methods, and Use Cases for Innovative Scientific Discovery (BTSD), pp. 4248-4256, Washington DC, USA, Dec 15th 2024. [ Preprint | BibTex ]
• Niloofar Zendehdel, Adib Mosharrof, Katherine Delgado, Daoru Han, Xin Liang, and Tong Shu. Modeling Lunar Surface Charging Using Physics-Informed Neural Networks (short paper). In Proc. of IEEE International Conference on Big Data (BigData): 5th International Workshop on Big Data & AI Tools, Methods, and Use Cases for Innovative Scientific Discovery (BTSD), pp. 4269-4274, Washington DC, USA, Dec 15th 2024. [ Preprint | BibTex ]

Publications in 2023

• Turja Kundu and Tong Shu^*. HIOS: Hierarchical Inter-Operator Scheduler for Real-Time Inference of DAG-Structured Deep Learning Models on Multiple GPUs. In Proc. of IEEE International Conference on Cluster Computing (Cluster), pp. 95-106, Santa Fe, NM, USA, Oct 31st-Nov 3rd 2023. (Acceptance rate: 24.6% = 32/130, NSF student travel award $1,250) [ Preprint | BibTex ]
• Yicheng Zhang, Dhroov Pandey, Di Wu, Turja Kundu, Ruopu Li, and Tong Shu^*. Accuracy-Constrained Efficiency Optimization and GPU Profiling of CNN Inference for Detecting Drainage Crossing Locations. In Proc. of Workshops of ACM International Conference for High Performance Computing, Networking, Storage and Analysis (SC-W), pp. 1780-1788, Denver, CO, USA, Nov 12th 2023. [ Preprint | BibTex ]
• Yuke Li, Jiwon Baik, Md Marufi Rahman, Iraklis Anagnostopoulos, Ruopu Li, and Tong Shu^*. Pareto Optimization of CNN Models via Hardware-Aware Neural Architecture Search for Drainage Crossing Classification on Resource-Limited Devices. In Proc. of Workshops of ACM International Conference for High Performance Computing, Networking, Storage and Analysis (SC-W), pp. 1767-1775, Denver, CO, USA, Nov 12th 2023. [ Preprint | BibTex ]
• Yasmeen Haleem, Isabelle Wagenvoord, Qianjun Wei, Ting Xiao, Tong Shu, and Yuede Ji. Understanding Nationwide Power Outage and Restoration for Future Predication. Annual Smoky Mountains Computational Sciences and Engineering Conference Data Challenge (SMCDC), 12 pages, Knoxville, TN, USA, Aug 29th-31st 2023. ( Best Poster Award ) [ Preprint ]

Previous Publications

• Journal Articles
1. I. Foster, M. Ainsworth, J. Bessac, F. Cappello, J. Choi, S. Di, Z. Di, A. M. Gok, H. Guo, K. A. Huck, C. Kelly, S. Klasky, K. K. van Dam, X. Liang, K. Mehta, M. Parashar, T. Peterka, L. Pouchard, T. Shu, O. Tugluk, H. van Dam, L. Wan, M. Wolf, J. M. Wozniak, W. Xu, I. Yakushin, S. Yoo, and T. Munson. Online Data Analysis and Reduction: An Important Co-design Motif for Extreme-scale Computers. SAGE International Journal of High Performance Computing Applications, vol. 35, no. 6, pp. 617-635, Jun 2021. [ Preprint | BibTex ]
2. T. Shu and C. Q. Wu. Energy-efficient Mapping of Large-scale Workflows under Deadline Constraints in Big Data Computing Systems. Elsevier Future Generation Computing Systems, vol. 110, pp. 515-530, Sep 2020. [ Preprint | BibTex ]
3. J. M. Wozniak, M. Dorier, R. Ross, T. Shu, T. Kurc, L. Tang, N. Podhorszki, and M. Wolf. MPI Jobs within MPI Jobs: a Practical Way of Enabling Task-level Fault-tolerance in HPC Workflows. Elsevier Future Generation Computing Systems, vol. 101, pp. 576-589, Dec 2019. [ Preprint | BibTex ]
4. T. Shu and C. Q. Wu. Bandwidth Scheduling for Energy-efficiency in High-performance Networks. IEEE Transactions on Communications, vol. 65, no. 8, pp. 3359-3373, Aug 2017. [ Preprint | BibTex ]
5. T. Shu, M. Liu, A. Zhou, and Z. Li. A Fairness Optimization Algorithm of Variable Spectral Widths and Time-Slot Lengths in Wireless Mesh Networks. Chinese Journal of Computers, vol. 34, no. 1, pp. 65-75, Jan 2011. [ Preprint | BibTex ]
6. T. Shu, M. Liu, and Z. Li. Performance Analysis of a Network-layer Soft Vertical Handoff Scheme without Packet Reordering. Chinese Journal of Computers, vol. 33, no. 1, pp. 23-35, Jan 2010. [ Preprint | BibTex ]
• Conference Papers
1. T. Shu, Y. Guo, J. Wozniak, X. Ding, I. Foster, and T. Kurc. Bootstrapping In-Situ Workflow Auto-Tuning via Combining Performance Models of Component Applications. In Proc. of ACM/IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis (SC), article no. 28, pp. 1-15, St. Louis, MO, USA, Nov 14th-19th 2021. (Acceptance rate: 23.6% = 86/365) [ Preprint | BibTex ]
2. T. Shu and C. Q. Wu. Performance Optimization of Hadoop Workflows in Public Clouds through Adaptive Task Partitioning. In Proc. of the 36th IEEE International Conference on Computer Communications (INFOCOM), pp. 2349-2357, Atlanta, GA, USA, May 1st-4th 2017. (Acceptance rate: 20.9%) [ Preprint | BibTex ]
3. T. Shu and C. Q. Wu. Energy-efficient Dynamic Scheduling of Deadline-constrained MapReduce Workflows. In Proc. of the 13th IEEE International Conference on e-Science (eScience), pp. 393-402, Auckland, New Zealand, Oct 24th-27th 2017. (Acceptance rate: 36%) [ Preprint | BibTex ]
4. T. Shu, C. Q. Wu, and D. Yun. Advance Bandwidth Reservation for Energy Efficiency in High-performance Networks. In Proc. of the 38th IEEE Conference on Local Computer Networks (LCN), pp. 541-548, Sydney, Australia, Oct 21st-24th 2013. (Acceptance rate: 26.4%) [ Preprint | BibTex ]
5. T. Shu, M. Liu, Z. Li, and Q. Wu. Interference Pair-Based Distributed Spectrum Allocation in Wireless Mesh Networks with Frequency-Agile Radios. In Proc. of the 8th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), pp. 82-90, Salt Lake City, UT, USA, Jun 27th-30th 2011. (Acceptance rate: 27.4%) [ Preprint | BibTex ]
6. T. Shu, M. Liu, and Z. Li. Spectrum Allocation for Distributed Throughput Maximization under Secondary Interference Constraints in Wireless Mesh Networks. In Proc. of the 20th IEEE International Conference on Computer Communications and Networks (ICCCN), 6 pages, Maui, HI, USA, Jul 31st-Aug 4th 2011. (Acceptance rate: 29.6%) [ Preprint | BibTex ]
7. A. Zhou, M. Liu, T. Shu, Y. Song, and Z. Li. Exploiting the Full Potential of Multi-AP Diversity in Centralized WLANs through Back-pressure Scheduling. In Proc. of the 36th IEEE Conference on Local Computer Networks (LCN), pp. 505-513, Bonn, Germany, Oct 4th-7th 2011. (Acceptance rate: 29.3%) [ Preprint | BibTex ]
8. T. Shu, M. Liu, Z. Li, and A. Zhou. A Diagnosis-Based Soft Vertical Handoff Mechanism for TCP Performance Improvement. In Proc. of the 19th IEEE International Conference on Computer Communications and Networks (ICCCN), 6 pages, Zurich, Switzerland, Aug 2nd-5th 2010. (Acceptance rate: 33.9%) [ Preprint | BibTex ]
9. T. Shu, M. Liu, Z. Li, and A. Zhou. Joint Variable Width Spectrum Allocation and Link Scheduling for Wireless Mesh Networks. In Proc. of IEEE International Conference on Communications (ICC), 5 pages, Cape Town, South Africa, May 23rd-27th 2010. (Acceptance rate: 39.5%) [ Preprint | BibTex ]
10. T. Shu, M. Liu, and Z. Li. A Performance Evaluation Model for RSS-based Vertical Handoff Algorithms. In Proc. of the 14th IEEE Symposium on Computers and Communications (ISCC), pp. 271-276, Sousse, Tunisia, Jul 5th-8th 2009. (Acceptance rate: 35.9%) [ Preprint | BibTex ]
• Workshop Papers, Short Papers, and Posters
1. T. Shu, Y. Guo, J. Wozniak, X. Ding, I. Foster, and T. Kurc. POSTER: In-situ Workflow Auto-tuning through Combining Component Models. In Proc. of 26th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP), pp. 467-468, Virtual Event, Seoul, South Korea, Feb 27th-Mar 3rd, 2021 (Acceptance rate: 42% = (31+32)/150). [ Preprint | BibTex ]
2. J. Wozniak, P. E. Davis, T. Shu, J. Ozik, N. Collier, I. Foster, T. Brettin, and R. Stevens. Scaling Deep Learning for Cancer with Advanced Workflow Storage Integration. In Proc. of the 4th Workshop on Machine Learning in HPC Environments (MLHPC) in conjunction with ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis (SC), pp. 114-123, Dallas, TX, USA, Nov 11th-12th 2018. [ Preprint | BibTex ]
3. T. Shu and C. Q. Wu. Energy-efficient Mapping of Big Data Workflows under Deadline Constraints. In Proc. of the 11th Workshop on Workflows in Support of Large-Scale Science (WORKS) in conjunction with ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis (SC), pp. 34-43, Salt Lake City, UT, USA, Nov 14th 2016. (Acceptance rate: 29.4%) [ Preprint | BibTex ]
4. T. Shu, M. Liu, Z. Li, and K. Zheng. Network-layer Soft Vertical Handoff Schemes without Packet Reordering (short paper). In Proc. of the 34th IEEE Conference on Local Computer Networks (LCN), pp. 285-288, Zurich, Switzerland, Oct 20th-23rd 2009. (Acceptance rate: 48.6% = (59+44)/(188+24) ) [ Preprint | BibTex ]