SimpleMOC - A performance abstraction for 3D MOC

The method of characteristics (MOC) is a popular method for efficiently solving two-dimensional reactor problems. Extensions to three dimensions have been attempted with mitigated success bringing into question the ability of performing efficient full core three-dimensional (3D) analysis. Although the 3D problem presents many computational difficulties, some simplifications can be made that allow for more efficient computation. In this investigation, we present SimpleMOC, a “mini-app” which mimics the computational performance of a full 3D MOC solver without involving the full physics perspective, allowing for a more straightforward analysis of the computational challenges. A variety of simplifications are implemented that are intended to increase the computational feasibility, including the formation axially-quadratic neutron sources. With the addition of the quadratic approximation to the neutron source, 3D MOC is cast as a CPU-intensive method with the potential for remarkable scalability on next generation computing architectures.United States. Dept. of Energy. Office of Nuclear Energy (Nuclear Energy University Programs Fellowship)United States. Dept. of Energy. Center for Exascale Simulation of Advanced ReactorUnited States. Dept. of Energy. Office of Advanced Scientific Computing Research (Contract DE-AC02-06CH11357

A task-based parallelism and vectorized approach to 3D Method of Characteristics (MOC) reactor simulation for high performance computing architectures

In this study we present and analyze a formulation of the 3D Method of Characteristics (MOC) technique applied to the simulation of full core nuclear reactors. Key features of the algorithm include a task-based parallelism model that allows independent MOC tracks to be assigned to threads dynamically, ensuring load balancing, and a wide vectorizable inner loop that takes advantage of modern SIMD computer architectures. The algorithm is implemented in a set of highly optimized proxy applications in order to investigate its performance characteristics on CPU, GPU, and Intel Xeon Phi architectures. Speed, power, and hardware cost efficiencies are compared. Additionally, performance bottlenecks are identified for each architecture in order to determine the prospects for continued scalability of the algorithm on next generation HPC architectures. Keywords: Method of Characteristics; Neutron transport; Reactor simulation; High performance computingUnited States. Department of Energy (Contract DE-AC02-06CH11357

Development of the random ray method of neutral particle transport for high-fidelity nuclear reactor simulation

Thesis: Ph. D. in Computational Nuclear Science and Engineering, Massachusetts Institute of Technology, Department of Nuclear Science and Engineering, 2018.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 177-188).A central goal in computational nuclear engineering is the high-fidelity simulation of a full nuclear reactor core by way of a general simulation method. General full core simulations can potentially reduce design and construction costs, increase reactor performance and safety, reduce the amount of nuclear waste generated, and allow for much more complex and novel designs. To date, however, the time to solution and memory requirements for a general full core high fidelity 3D simulation have rendered such calculations impractical, even using leadership class supercomputers. Reactor designers have instead relied on calibrated methods that are accurate only within a narrow design space, greatly limiting the exploration of innovative concepts. One numerical simulation approach, the Method of Characteristics (MOC), has the potential for fast and efficient performance on a variety of next generation computing systems, including CPU, GPU, and Intel Xeon Phi architectures. While 2D MOC has long been used in reactor design and engineering as an efficient simulation method for smaller problems, the transition to 3D has only begun recently, and to our knowledge no 3D MOC based codes are currently used in industry. The delay of the onset of full 3D MOC codes can be attributed to the impossibility of "naively" scaling current 2D codes into 3D due to prohibitively high memory requirements. To facilitate transition of MOC based methods to 3D, we have developed a fundamentally new computational algorithm. This new algorithm, known as The Random Ray Method (TRRM), can be viewed as a hybrid between the Monte Carlo (MC) and MOC methods. Its three largest advantages compared to MOC are that it can handle arbitrary 3D geometries, it offers extreme improvements in memory efficiency, and it allows for significant reductions in algorithmic complexity on some simulation problems. It also offers a much lower time to solution as compared to MC methods. In this thesis, we will introduce the TRRM algorithm and a parallel implementation of it known as the Advanced Random Ray Code (ARRC). Then, we will evaluate its capabilities using a series of benchmark problems and compare the results to traditional deterministic MOC methods. A full core simulation will be run to assess the performance characteristics of the algorithm at massive scale. We will also discuss the various methods to parallelize the algorithm, including domain decomposition, and will investigate the new method's scaling characteristics on two current supercomputers, the IBM Blue Gene/Q Mira and the Cray XC40 Theta. The results of these studies show that TRRM is capable of breakthrough performance and accuracy gains compared to existing methods which we demonstrate to enable general, full core 3D high-fidelity simulations that were previously out of reach.by John Robert Tramm.Ph. D. in Computational Nuclear Science and Engineerin