GPU Acceleration of Kernel Density Estimators in Monte Carlo Neutron Transport Simulations

Journal Article · Fri Jul 01 04:00:00 EDT 2016 · Transactions of the American Nuclear Society

OSTI ID:23042650

Burke, Timothy P.; Kiedrowski, Brian C.; Martin, William R. ^[1]; Brown, Forrest B. ^[2]

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1. Department of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, MI (United States)
2. Los Alamos National Laboratory, P.O. Box 1663, Los Alamos, NM 87545 (United States)

Kernel Density Estimators (KDEs) have recently been explored for use in Monte Carlo radiation transport simulations as an alternative to histogram tallies for capturing spatially-resolved quantities such as neutron flux and reaction rates. Histogram tallies suffer from large uncertainties when detailed spatial resolution of the quantity of interest is required; increasing the resolution of the underlying mesh increases the uncertainty in each histogram bin. KDEs obtain estimates of underlying densities on user-defined points, with the uncertainty at those points being independent of the spatial resolution desired since collisions and particle-track segments are allowed to contribute to the score at multiple tally points. Thus, KDEs show potential for obtaining estimates of a smoother spatially-resolved with reduced variance when compared to a histogram. However, this ability that enables KDEs to obtain lower uncertainties per particle history is more computationally expensive than using a traditional histogram bins on a structured mesh. While KDEs are capable of producing lower tally uncertainties compared to histogram binning for a given number of histories, their increased computational cost may make them less efficient. Even so, it is possible to reduce this additional cost by exporting the KDE tally routines onto Graphics Processing Units (GPUs). GPUs are commonly found on high-performance computing machines as a means of increasing performance without adding additional compute nodes. While it is difficult for existing Monte Carlo radiation transport codes to take advantage of GPUs without rewriting large portions of the code, it is possible to leverage GPUs through heterogeneous computing. This work uses a modified version of OpenMC and exports the KDE tally routines onto a GPU to obtain problem-dependent speedups ranging between 1.6 and 5.0 for the problem studied in this paper. While this methodology is applied to KDEs, the same ideas can be applied to other computationally expensive vectorizable portions of Monte Carlo algorithms. (authors)

• OSTI ID: 23042650
• Journal Information: Transactions of the American Nuclear Society, Journal Name: Transactions of the American Nuclear Society Vol. 115; ISSN 0003-018X
• Country of Publication: United States
• Language: English

Related Subjects

73 NUCLEAR PHYSICS AND RADIATION PHYSICS
97 MATHEMATICS AND COMPUTING
ALGORITHMS
COLLISIONS
COMPARATIVE EVALUATIONS
COMPUTERIZED SIMULATION
DENSITY
KERNELS
MONTE CARLO METHOD
NEUTRON FLUX
NEUTRON TRANSPORT
PERFORMANCE
REACTION KINETICS
SPATIAL RESOLUTION