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High-Performance Computing
Published in Dale A. Anderson, John C. Tannehill, Richard H. Pletcher, Munipalli Ramakanth, Vijaya Shankar, Computational Fluid Mechanics and Heat Transfer, 2020
Dale A. Anderson, John C. Tannehill, Richard H. Pletcher, Munipalli Ramakanth, Vijaya Shankar
CFD applications using highly scalable computational methods have been deployed on very large computer clusters. The factors driving a balance between numerical methods and computational cost are gradually pointing toward exascale computing. As noted by Ashby et al. (2010), exascale computing is now seen as an enabling technology for simulation-based aerodynamic design. There seems to be a strong likelihood that this will be attained very soon. Intel introduced a teraflops chip (i9) for desktop applications in 2017, and an IBM-built supercomputer named “Summit” at Oak Ridge National Laboratory recorded a 148.6 petaflops performance in 2019. Exascale computing is also believed to be essential for transformative developments in astrophysics, biological systems, climate modeling, combustion, materials science, nuclear engineering, and aspects of national security.
Solving the Neutron Transport Equation for Microreactor Modeling Using Unstructured Meshes and Exascale Computing Architectures
Published in Nuclear Science and Engineering, 2023
William C. Dawn, Scott Palmtag
Solving the neutron transport equation is a notoriously computationally challenging problem. Therefore, the solution methodology in MEZCAL will target exascale computing architectures. Exascale computers are named for their ability to compute at a rate of 1 exaFLOPS ( floating point operations per second).4 Exascale computing architectures are unique in that they depend heavily on the use of graphics processing units (GPUs) instead of traditional CPUs for computation.5 Traditionally, numerical methods have been implemented on CPUs, which offer a wide variety of operations but are not optimized for any particular type of computation. GPUs were originally designed to efficiently perform the computations necessary for rendering computer graphics. Later, computer scientists determined that GPUs could be used to perform certain classes of more general-purpose computations.6 In the age of exascale computing, the use of GPUs has continued to grow, and more and more computations are performed in a fast and efficient manner on GPUs.