Computational Fluid Dynamics in the Exascale Era of Computation

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Fluids are everywhere in nature. Computational fluid dynamics (CFD) is the broad field of using computers to obtain solutions to the equations describing fluid flow. Performing simulations of fluid flow using CFD techniques has become an indispensable tool to study problems across a range of scientific disciplines. Simulations allow us to validate of our understanding of complex phemonena, and, in many cases, offers the only potential avenue to study problems that are intractable from a mathematical or experimental perspective. Simulations continually increase in scope, reflecting the need to connect small-scale processes with large-scale features, and the drive to create wholly consistent simulations that encompass all relevant physical processes. That simulations can increase in scale and complexity is permissible because supercomputing clusters continue to exponentially grow in computational power over time. Within the next decade, the first supercomputing clusters will come online that can compute at 10^18 floating point operations per second (an exaflop). However, there exist substantial challenges in designing parallel algorithms that can scale CFD solvers for exascale clusters. These clusters will have more computational power not by individual CPUs becoming faster, but by sheer number of CPUs, and by including a mixture of CPUs and GPUs (graphics processing units). Communication loads and memory bottlenecks will need to be overcome, and failure modes that are rare enough to ignore today will become commonplace. The primary objectives of this research program are to design parallel algorithms and multi-physics CFD solvers that enable the next generation of CFD simulations on exascale clusters. I will focus on smoothed particle hydrodynamics (SPH), a widely used mesh-free CFD method, and its applications to astrophysics. I will focus on two main areas: designing parallel algorithms for exascale clusters, and creating the multi-physics algorithms required to solve key astrophysical problems by exascale computation. The first exascale clusters will soon be reality, as the US National Strategic Computing Initiative aims to have a capable exascale cluster by 2023. The outcomes of this research program will have numerous beneficial impacts to Canadian society, as robust algorithms to utilize exascale resources will be useful in many areas, such as wind farm optimization, cancer drug discovery, and nuclear reactor design. Training of undergraduate and graduate students through my research program will yield highly-qualified personnel (HQP) with valuable skills, such as software engineering and data science, that are transferrable to a broad range of careers.