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Tong Shu

Title: Assistant Professor

Department: Computer Science and Engineering

College: College of Engineering

Curriculum Vitae

Curriculum Vitae Link

Education

  • PhD, New Jersey Institute of Technology, 2017
    Major: Computer Science
    Specialization: Parallel and Distributed Computing
    Dissertation: Performance Optimization and Energy Efficiency of Big-data Computing Workflows

Current Scheduled Teaching

CSCE 5610.001Computer System ArchitectureSpring 2025
CSCE 6940.960Individual ResearchSpring 2025
CSCE 5218.001Deep LearningFall 2024
CSCE 6940.860Individual ResearchFall 2024
Texas Education Code 51.974 (HB 2504) requires each institution of higher education to make available to the public, a syllabus for undergraduate lecture courses offered for credit by the institution.

Previous Scheduled Teaching

CSCE 4110.002AlgorithmsSpring 2024 Syllabus SPOT
CSCE 4890.760Directed StudySpring 2024
CSCE 6940.960Individual ResearchSpring 2024
CSCE 5218.001Deep LearningFall 2023 SPOT
CSCE 4890.760Directed StudyFall 2023
CSCE 6940.860Individual ResearchFall 2023
CSCE 5610.003Computer System ArchitectureSpring 2023 SPOT
CSCE 4610.001Computer Systems ArchitectureSpring 2023 Syllabus SPOT
CSCE 5934.917Directed StudySpring 2023
CSCE 6940.960Individual ResearchSpring 2023
CSCE 5218.001Deep LearningFall 2022 SPOT
Texas Education Code 51.974 (HB 2504) requires each institution of higher education to make available to the public, a syllabus for undergraduate lecture courses offered for credit by the institution.

Published Intellectual Contributions

    Conference Proceeding

  • 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

    • Journal Article

  • 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

    • Poster

  • 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

Contracts, Grants and Sponsored Research

    Grant - Research

  • Shu, T. (Principal), "Collaborative Research: CyberTraining: Pilot: Research Workforce Development for Deep Learning Systems in Advanced GPU Cyberinfrastructure," sponsored by National Science Foundation, Federal, $201262 Funded. (2022 - 2024).
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Overall
Summative Rating		 Challenge and
Engagement Index		 Response Rate

out of 5
out of 7		 %
of
students responded
  • Overall Summative Rating (median):
    This rating represents the combined responses of students to the four global summative items and is presented to provide an overall index of the class’s quality. Overall summative statements include the following (response options include a Likert scale ranging from 5 = Excellent, 3 = Good, and 1= Very poor):
    • The course as a whole was
    • The course content was
    • The instructor’s contribution to the course was
    • The instructor’s effectiveness in teaching the subject matter was
  • Challenge and Engagement Index:
    This rating combines student responses to several SPOT items relating to how academically challenging students found the course to be and how engaged they were. Challenge and Engagement Index items include the following (response options include a Likert scale ranging from 7 = Much higher, 4 = Average, and 1 = Much lower):
    • Do you expect your grade in this course to be
    • The intellectual challenge presented was
    • The amount of effort you put into this course was
    • The amount of effort to succeed in this course was
    • Your involvement in course (doing assignments, attending classes, etc.) was
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