AbstractsComputer Science

Over the past 20 years, parallel computing has enabled computers to grow ever larger and more powerful while scientific applications have advanced in sophistication and resolution. This trend is being challenged, however, as the power consumption for conventional parallel computing architectures has risen to unsustainable levels and memory limitations have come to dominate compute performance. Multi-core processors and heterogeneous computing platforms, such as Graphics Processing Units (GPUs), are an increasingly popular paradigm for resolving these issues. This thesis explores the applicability of shared memory parallel platforms for solving deterministic neutron transport problems. A 2D method of characteristics code - OpenMOC - has been developed with solvers for shared memory multi-core platforms as well as GPUs. The multi-threading and memory locality methodologies for the multi-core CPU and GPU solvers are presented. Parallel scaling results using OpenMP demonstrate better than ideal weak scaling and nearly perfect strong scaling on both Intel Xeon and IBM Blue Gene/Q architectures. Performance results for the 2D C5G7 benchmark demonstrate up to 50x speedup for MOC on a GPU. The lessons learned from this thesis will provide the basis for further exploration of MOC on many-core platforms and GPUs as well as design decisions for hardware vendors exploring technologies for the next generation of machines for scientific computing.