Effective Parallelization Strategies for Scalable, High-Performance Iterative Reconstruction
| dc.contributor.author | Gribble, Christiaan Paul | en_US |
| dc.contributor.editor | Frey, Steffen and Huang, Jian and Sadlo, Filip | en_US |
| dc.date.accessioned | 2020-05-24T13:24:38Z | |
| dc.date.available | 2020-05-24T13:24:38Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | Iterative reconstruction techniques in X-ray computed tomography converge to a result by successively refining increasingly accurate estimates. Compared to alternative approaches, iterative reconstruction imposes significant computational demand but generally leads to higher reconstruction quality and is more robust to inherently imperfect scan data. We explore several strategies for exploiting parallelism in iterative reconstruction and evaluate their scalability and performance on modern workstation-class systems. Results show that scalable, high performance iterative reconstruction is possible with careful attention to the expression of parallelism in both the projection and backprojection phases of computation. | en_US |
| dc.description.sectionheaders | Visualization | |
| dc.description.seriesinformation | Eurographics Symposium on Parallel Graphics and Visualization | |
| dc.identifier.doi | 10.2312/pgv.20201074 | |
| dc.identifier.isbn | 978-3-03868-107-6 | |
| dc.identifier.issn | 1727-348X | |
| dc.identifier.pages | 47-56 | |
| dc.identifier.uri | https://doi.org/10.2312/pgv.20201074 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/pgv20201074 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.rights | Attribution 4.0 International License | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | ] |
| dc.subject | Computing methodologies | |
| dc.subject | Parallel algorithms | |
| dc.subject | Ray tracing | |
| dc.title | Effective Parallelization Strategies for Scalable, High-Performance Iterative Reconstruction | en_US |