Speaker
Description
GPU acceleration has been successfully utilised in particle physics for real time analysis and simulation, in this study, we investigate the potential benefits for medical physics applications by analysing performance, development effort, and availability. We selected a software developer with no high performance computing experience to parallelise and accelerate a stand-alone Monte Carlo simulation consisting of electron single coulomb scattering. Such simulations contribute to real-time dose estimation for real-time adaptive radiotherapy, a new and emerging cancer treatment that heavily relies on high performance computing. As a proof of principle, we implement a single scattering process of electrons in a homogeneous material with pencil beam at constant initial energy. We compared performance gain offered by GPU acceleration against an optimised CPU implementation and evaluated it by computing 100M histories of a 128 keV electron interacting in water. We also evaluated 1B histories to measure the scalability. The results show that when comparing the multi-core CPU implementation running with 24 cores, a speedup of 808x (100M) and 1727x (1B), which corresponds to a 320x and 648x cost-equivalent speedup. The results on both architectures were statistically equivalent.The successful implementation and measured acceleration combined with the low level of expertise needed for obtaining such speedup is a promising first step for the use of GPU acceleration in a context such as real-time adaptive radiotherapy where there are strict performance and time requirements.
Significance
This study analyzes the possibility of applying HEP methods for GPU acceleration of Monte Carlo simulation to the medical context. The results show that it is possible to achieve multiple orders of magnitude speedup compared to equivalent multicore implementations
