Date of Award
2026
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science
Committee Chair
Joshua Booth
Committee Member
Tathagata Mukherjee
Committee Member
Summer Atkins
Research Advisor
Joshua Booth
Subject(s)
High performance computing, Machine learning, Neural networks (Computer science)
Abstract
Due to rise of machine learning workloads, high performance computing platforms have increased GPU resources. Consequently, use of surrogate models, to utilize GPU compute and speedup scientific applications, is on the rise. Most of the time, the whole program is replaced with a surrogate model. In this work a shock simulation program (LULESH) was taken and three functions of varying complexity were replaced with small sized neural network as surrogate models. The speedup and error in output of whole program was analyzed. We found that for the smallest function, trained model lead to speedup of overall program by 40%, maintained acceptable accuracy and generalized to a bigger domain size. Larger functions however, required bigger model with slower execution time and did not generalize. The implication is that small surrogate models applied to small code segments also provide good potential for optimizing performance.
Recommended Citation
Panthi, Bibek, "Analysis of surrogate models at multiple levels for neural acceleration of HPC applications" (2026). Theses. 814.
https://louis.uah.edu/uah-theses/814