A central goal in computational nuclear engineering is the high fidelity simulation of a full nuclear reactor core. 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 full core high fidelity deterministic 3D calculation have rendered such calculations impractical, even using leadership-class supercomputers. However, with High Performance Computing (HPC) architectures evolving rapidly towards exascale, the computational horsepower required to accomplish full 3D reactor simulations may soon be available.

One numerical simulation approach, the 3D Method of Characteristics (MOC), has the potential for fast and efficient performance on a variety of next-generation HPC systems, including CPU, GPU, and Intel Xeon Phi architectures. The task–based and highly vectorizable nature of MOC style simulations allows them to very efficiently utilize the floating-point resources of modern hardware architectures – in one test reaching 63% of peak FLOPS available on a CPU node. This is advantageous, as most high performance computing applications are only able to achieve 8 to 20% of peak FLOPS, with some neutronics algorithms, such as continuous energy Monte Carlo, often failing to exceed 1%. 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 codes can be attributed to the impossibility of “naively” scaling current 2D codes into 3D due to prohibitively high memory requirements.