Hybrid MPI+OpenMP parallelization of image reconstruction in proton beam therapy on multi-core and many-core processors
| dc.contributor.author | Della-Giustina, James | |
| dc.contributor.author | Barajas, Carlos A. | |
| dc.contributor.author | Gobbert, Matthias K. | |
| dc.contributor.author | Mackin, Dennis S. | |
| dc.contributor.author | Polf, Jeremy | |
| dc.date.accessioned | 2020-07-29T17:14:32Z | |
| dc.date.available | 2020-07-29T17:14:32Z | |
| dc.description | HPC '18: Proceedings of the High Performance Computing Symposium, April 2018 | en_US |
| dc.description.abstract | The advantage of proton beam therapy is that the lethal dose of radiation is delivered by a sharp increase toward the end of the beam range, known as the Bragg peak (BP), with no dose delivered beyond. By using these characteristics of the BP, radiation dose to the tumor can be maximized, with greatly reduced radiation dose to the surrounding healthy tissue. If the secondary gamma rays that are emitted through interaction of the protons in the beam with atoms in the patient tissue could be imaged in (near) real-time during beam delivery, it could provide a means of visualizing the delivery of dose for verification of proper treatment delivery. However, such imaging requires very fast image reconstruction to be feasible. This project focuses on measuring the performance of a new parallel version of the CCI (Compton camera imaging) image reconstruction algorithm. We show two conclusions: (i) The new hybrid MPI+OpenMP parallelization of the code on the many-core Intel Xeon Phi KNL processor with 68 computational cores makes fast reconstruction times possible and thus enables the use of CCI in real time during treatment. (ii) A compute node with two of the latest multi-core Intel Skylake CPUs with 24 cores performs even better in a first comparison of both types of processors available on Stampede2 at the Texas Advanced Computing Center (TACC). | en_US |
| dc.description.sponsorship | This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI–1053575 (Towns et al. 2014). We acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing the HPC resources Stampede2. The hardware in the UMBC High Performance Computing Facility (HPCF) is supported by the U.S. National Science Foundation through the MRI program (grant nos. CNS–0821258, CNS–1228778, and OAC–1726023) and the SCREMS program (grant no. DMS–0821311), with additional substantial support from the University of Maryland, Baltimore County (UMBC). See hpcf.umbc.edu for more information on HPCF and the projects using its resources. Co-author James Della-Giustina was supported in part, by the Math Computer Inspired Scholars program, through funding from the National Science Foundation and also the Constellation STEM Scholars Program, funded by Constellation Energy. Graduate assistant and co-author Carlos Barajas was supported by UMBC as HPCF RA. For co-author Matthias Gobbert, this material is based upon work supported while serving at the National Science Foundation. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. The research reported in this publication was supported by the National Institutes of Health National Cancer Institute under award number R01CA187416. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. | en_US |
| dc.description.uri | https://dl.acm.org/doi/10.5555/3213069.3213080 | en_US |
| dc.format.extent | 11 pages | en_US |
| dc.genre | conference papers and proceedings preprints | en_US |
| dc.identifier | doi:10.13016/m2zzpq-ybhh | |
| dc.identifier.citation | James Della-Giustina, Carlos Barajas, Matthias K. Gobbert, Dennis S. Mackin, and Jerimy Polf. 2018. Hybrid MPI+OpenMP parallelization of image reconstruction in proton beam therapy on multi-core and many-core processors. In Proceedings of the High Performance Computing Symposium (HPC ’18). Society for Computer Simulation International, San Diego, CA, USA, Article 11, 1–11, https://dl.acm.org/doi/10.5555/3213069.3213080 | en_US |
| dc.identifier.uri | http://hdl.handle.net/11603/19272 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | ACM | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Mathematics Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
| dc.rights | ©2018 Society for Modeling & Simulation International (SCS) | |
| dc.subject | UMBC High Performance Computing Facility (HPCF) | |
| dc.title | Hybrid MPI+OpenMP parallelization of image reconstruction in proton beam therapy on multi-core and many-core processors | en_US |
| dc.type | Text | en_US |