Zhang, Y., Pandey, D., Wu, D., Kundu, T., Li, R., Shu, T. (2023). Accuracy-Constrained Efficiency Optimization and GPU Profiling of CNN Inference for Detecting Drainage Crossing Locations. 1780-1788. Denver, CO, USA: Workshops of ACM International Conference for High Performance Computing, Networking, Storage and Analysis (SC-W). https://dl.acm.org/doi/10.1145/3624062.3624260
Li, Y., Baik, J., Rahman, M., Anagnostopoulos, I., Li, R., Shu, T. (2023). Pareto Optimization of CNN Models via Hardware-Aware Neural Architecture Search for Drainage Crossing Classification on Resource-Limited Devices. 1767-1775. Denver, CO, USA: Workshops of ACM International Conference for High Performance Computing, Networking, Storage and Analysis (SC-W). https://dl.acm.org/doi/10.1145/3624062.3624258
Kundu, T., Shu, T. (2023). HIOS: Hierarchical Inter-Operator Scheduler for Real-Time Inference of DAG-Structured Deep Learning Models on Multiple GPUs. 95-106. Santa Fe, NM, USA: IEEE International Conference on Cluster Computing (Cluster). https://ieeexplore.ieee.org/document/10319967
Caldwell, J., Feng, W., Byun, M., Albert, M. V., Shu, T., Ji, Y. (2023). Exploring Power and Thermal Dynamics in the Summit Supercomputer: A Data Visualization Study. 7th Annual Smoky Mountains Computational Sciences Data Challenge (SMCDC).
Haleem, Y., Wagenvoord, I., Wei, Q., Xiao, T., Shu, T., Ji, Y. (2023). Understanding Nationwide Power Outage and Restoration for Future Prediction. 7th Annual Smoky Mountains Computational Sciences Data Challenge (SMCDC).
Shu, T., Guo, Y., Wozniak, J., Ding, X., Foster, I., Kurc, T. (2021). Bootstrapping In-Situ Workflow Auto-Tuning via Combining Performance Models of Component Applications. 28: 1-15. St. Louis, MO, USA: ACM/IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis (SC). https://dl.acm.org/doi/10.1145/3458817.3476197
Foster, I., Ainsworth, M., Bessac, J., Cappello, F., Choi, J., Di, S., Di, Z., Gok, A., Guo, H., Huck, K., Kelly, C., Klasky, S., Kleese Van Dam, K., Liang, X., Mehta, K., Parashar, M., Peterka, T., Pouchard, L., Shu, T., Tugluk, O., Van Dam, H., Wan, L., Wolf, M., Wozniak, J., Xu, W., Yakushin, I., Yoo, S., Munson, T. (2021). Online Data Analysis and Reduction: An Important Co-design Motif for Extreme-scale Computers. International Journal of Higher Performance Computing Applications. 35(6), 617-635. SAGE.
Shu, T., Wu, C. (2020). Energy-efficient Mapping of Large-scale Workflows under Deadline Constraints in Big Data Computing Systems. Future Generation Computer Systems. 110, 515-530. Elsevier. https://www.sciencedirect.com/science/article/abs/pii/S0167739X17300468
Shu, T., Guo, Y., Wozniak, J., Ding, X., Foster, I., Kurc, T. (2021). POSTER: In-situ Workflow Auto-tuning through Combining Component Models. 467–468. ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP) 2021. https://dl.acm.org/doi/10.1145/3437801.3441615