Thesis Defence: Porting a Large Eddy Simulation Computational Fluid Dynamics Solver to Graphics Processing Units

Andres Escobedo, supervised by Dr. Joshua Brinkerhoff, will defend their thesis titled “Porting a Large Eddy Simulation Computational Fluid Dynamics Solver to Graphics Processing Units” in partial fulfillment of the requirements for the degree of Master of Applied Science in Mechanical Engineering.

An abstract for Andres Escobedo’s thesis is included below.

Defences are open to all members of the campus community as well as the general public. Registration is not required for in-person defences.

Abstract

In this study, multiple approaches for accelerating a large eddy simulation (LES) solver are analyzed. The code routines that comprise the largest fraction of the execution time are offloaded to graphics processing units (GPUs). Large speedups are experienced for ported sections of code when using the optimal techniques. This study finds that care should be taken in choosing the right programming model as well as the manner in which data is transferred back and forth from the host (CPU) to the device (GPU). NVIDIA GPUs were targeted through the use of the CUDA (Compute Unified Device Architecture) API (Application Programming Interface) and the CUDA backend programming model was used for code written in the heterogeneous programming model Kokkos. Using Kokkos allowed for a much greater ease of use for the developer when using 3D arrays in PETSc (the Portable Extensible Tool for Scientific Computation). Though the entirety of the LES solver’s code was not ported, the benefits of using GPUs are discussed and an extensive outline to continue porting the code is provided. At least a 5 times speedup was experienced when using an older generation GPU when comparing ported code to code running on the CPU.