| Summary: | Thesis: Ph. D., Massachusetts Institute of Technology, Department of Nuclear Science and Engineering, 2015. === Cataloged from PDF version of thesis. === Includes bibliographical references (pages 151-158). === Monte Carlo (MC) neutral particle transport methods have long been considered the gold-standard for nuclear simulations, but high computational cost has limited their use significantly. However, as we move towards higher-fidelity nuclear reactor analyses the method has become competitive with traditional deterministic transport algorithms for the same level of accuracy, especially considering the inherent parallelism of the method and the ever-increasing concurrency of modern high performance computers. Yet before such analysis can be practical, several algorithmic challenges must be addressed, particularly in regards to the memory requirements of the method. In this thesis, a robust domain decomposition algorithm is proposed to alleviate this, along with models and analysis to support its use for full-scale reactor analysis. Algorithms were implemented in the full-physics Monte Carlo code OpenMC, and tested for a highly-detailed PWR benchmark: BEAVRS. The proposed domain decomposition implementation incorporates efficient algorithms for scalable inter-domain particle communication in a manner that is reproducible with any pseudo-random number seed. Algorithms are also proposed to scalably manage material and tally data with on-the-fly allocation during simulation, along with numerous optimizations required for scalability as the domain mesh is refined and divided among thousands of compute processes. The algorithms were tested on two supercomputers, namely the Mira Blue Gene/Q and the Titan XK7, demonstrating good performance with realistic tallies and materials requiring over a terabyte of aggregate memory. Performance models were also developed to more accurately predict the network and load imbalance penalties that arise from communicating particles between distributed compute nodes tracking different spatial domains. These were evaluated using machine properties and tallied particle movement characteristics, and empirically validated with observed timing results from the new implementation. Network penalties were shown to be almost negligible with per-process particle counts as low as 1000, and load imbalance penalties higher than a factor of four were not observed or predicted for finer domain meshes relevant to reactor analysis. Load balancing strategies were also explored, and intra-domain replication was shown to be very effective at improving parallel efficiencies without adding significant complexity to the algorithm or burden to the user. Performance of the strategy was quantified with a performance model, and shown to agree well with observed timings. Imbalances were shown to be almost completely removed for the finest domain meshes. Finally, full-core studies were carried out to demonstrate the efficacy of domain-decomposed Monte Carlo in tackling the full scope of the problem. A detailed mesh required for a robust depletion treatment was used, and good performance was demonstrated for depletion tallies with 206 nuclides. The largest runs scored six reaction rates for each nuclide in 51M regions for a total aggregate memory requirement of 1.4TB, and particle tracking rates were consistent with those observed for smaller non-domain- decomposed runs with equivalent tally complexity. These types of runs were previously not achievable with traditional Monte Carlo methods, and can be accomplished with domain decomposition with between 1.4x and 1.75x overhead with simple load balancing. === by Nicholas Edward Horelik. === Ph. D.
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