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Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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Hash Hashemian: Visionary leadership
As Dr. Hashem M. “Hash” Hashemian prepares to step into his term as President of the American Nuclear Society, he is clear that he wants to make the most of this unique moment.
A groundswell in public approval of nuclear is finding a home in growing governmental support that is backed by a tailwind of technological innovation. “Now is a good time to be in nuclear,” Hashemian said, as he explained the criticality of this moment and what he hoped to accomplish as president.
Ravi Vadapalli, Pablo Yepes, Wayne Newhauser, Roger Lichti
Nuclear Technology | Volume 175 | Number 1 | July 2011 | Pages 16-21
Technical Paper | Special Issue on the 16th Biennial Topical Meeting of the Radiation Protection and Shielding Division / Radiation Transport and Protection | doi.org/10.13182/NT11-A12263
Articles are hosted by Taylor and Francis Online.
Grid computing is an emerging technology that enables computational tasks to be accomplished in a collaborative approach by using a distributed network of computers. The grid approach is especially important for computationally intensive problems that are not tractable with a single computer or even with a small cluster of computers, e.g., radiation transport calculations for cancer therapy. The objective of this work was to extend a Monte Carlo (MC) transport code used for proton radiotherapy to utilize grid computing techniques and demonstrate its promise in reducing runtime from days to minutes. As proof of concept we created the Medical Grid between Texas Tech University and Rice University. Preliminary computational experiments were carried out in the GEANT4 simulation environment for transport of 25 × 106 200 MeV protons in a prostate cancer treatment plan. The simulation speedup was approximately linear; deviations were attributed to the spectrum of parallel runtimes and communication overhead due to Medical Grid computing. The results indicate that [approximately]3 × 105 to 5 × 105 proton events with processor core would result in 65 to 83% efficiency. Extrapolation of our results indicates that about 103 processor cores of the class used here would reduce the MC simulation runtime from 18.3 days to [approximately]1 h.