Date of Award


Degree Name

Master of Science


Computational Science


Vinod Kumar

Second Advisor

William F. Spotz


Physics aware simulations, often arising in problems of fluid dynamics, aerodynamics and multi-physics areas have demanded the need for computing software that has the capability to resolve the complexities at multiple scales to analyze and visualize the effects of their interactions with the surroundings. Usually the governing dynamics of these phenomenon appear in the form of complex partial differential equations whose numerical solutions impose various constraints on computational complexity, programming time and efficient throughput. In this scenario, the need of computing software that can solve very large problems resolving the physics of these phenomenon at multiple scales is imperative. Despite traditional computing capabilities in today's hardware through massively parallel systems, optimization and tuning of legacy physics code are usually constrained to specific super-computing clusters and often fail to reproduce similar efficiencies on others. With the dawn of heterogeneous computing systems equipped with accelerators, optimized code that is portable on different systems with varying architectures is a necessity. Such code exploits the advantages of specific hardware capabilities and scales sufficiently for very large and highly nonlinear problems. In this context Sandia National Labs' Trilinos and Kokkos libraries with inherently optimized parallelism and performance portability layers provide a suitable abstraction to build APIs (application programming interfaces) that can model complex physics at multiple scales with a very high degree of fidelity while scaling on massively parallel computers and heterogeneous computing architectures requiring little to no modification of source code. This Thesis discusses the development of two such APIs that have been built to solve a range of different fluid dynamics problems and demonstrate the physics that they can simulate. At the same time the different performance metrics obtained from testing these APIs on different supercomputing platforms have been discussed.




Received from ProQuest

File Size

105 pages

File Format


Rights Holder

Ashesh Kumar Chattopadhyay

Included in

Engineering Commons