Efficient Parallel Particle Advection via Targeting Devices

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dc.contributor.advisorChilds, Hank
dc.contributor.authorBelcher, Kristi
dc.date.accessioned2020-09-24T17:19:15Z
dc.date.available2020-09-24T17:19:15Z
dc.date.issued2020-09-24
dc.description.abstractParticle advection is a fundamental operation for a wide range of flow visualization algorithms. Particle advection execution times can vary based on many factors, including the number of particles, duration of advection, and the underlying architecture. In this study, we introduce a new algorithm for parallel particle advection which improves execution time by targeting devices, i.e., adapting to use the CPU or GPU based on the current work. This algorithm is motivated by the observation that CPUs are sometimes able to better perform part of the overall computation since CPUs operate at a faster rate when the threads of a GPU can not be fully utilized. To evaluate our algorithm, we ran 162 experiments and compared our algorithm to traditional GPU-only and CPU-only approaches. Our results show that our algorithm adapts to match the performance of the faster of CPU-only and GPU-only approaches.en_US
dc.identifier.urihttps://hdl.handle.net/1794/25657
dc.language.isoen_US
dc.publisherUniversity of Oregon
dc.rightsAll Rights Reserved.
dc.subjectDevice Targetingen_US
dc.subjectGPUsen_US
dc.subjectHeterogeneous Computingen_US
dc.subjectParticle Advectionen_US
dc.subjectScientific Visualizationen_US
dc.subjectVTK-men_US
dc.titleEfficient Parallel Particle Advection via Targeting Devices
dc.typeElectronic Thesis or Dissertation
thesis.degree.disciplineDepartment of Computer and Information Science
thesis.degree.grantorUniversity of Oregon
thesis.degree.levelmasters
thesis.degree.nameM.S.

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