Semester of Graduation

Fall 2025

Degree

Master of Science in Computer Science (MSCS)

Department

Division Of Computer Science and Engineering

Document Type

Thesis

Abstract

Large-scale quantum chemistry computations, such as those executed with the Tensor Algebra for Many-body Methods (TAMM) framework, require careful configuration of runtime parameters to achieve high performance and cost efficiency in high-performance computing (HPC) and cloud environments. Without effective performance analysis tools, researchers risk inefficient use of computational resources, leading to longer runtimes and higher costs.

To address this challenge, this thesis presents the design and implementation of a performance profiling and visualization toolkit for TAMM, developed as part of the DOE TEC4 project in collaboration with Pacific Northwest National Laboratory, Microsoft, and Louisiana State University. The toolkit collects detailed runtime data from TAMM-based quantum chemistry computations, enabling the analysis of tensor contraction performance, visualization of dependency graphs, and estimation of compute costs through machine learning models. A user-facing dashboard provides interactive performance views and supports optimization of configuration parameters such as tile size and node allocation.

The toolkit is evaluated using Coupled Cluster Singles and Double (CCSD) workloads on both LSU HPC systems and Microsoft Azure HPC cloud nodes. Results show that the machine learning models achieve accurate runtime prediction (Mean Absolute Percentage Error as low as 2.3% in certain test cases) and that the visual analytics effectively reveal performance bottlenecks. These capabilities make exascale-level simulations more accessible,interpretable, and resource-efficient for chemists and computational scientists.

Date

8-22-2025

Committee Chair

Gerald Baumgartner

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