  We recently built an analytical source model for GPU-based MC dose engine. In
this paper, we present a sampling strategy to efficiently utilize this source
model in GPU-based dose calculation. Our source model was based on a concept of
phase-space-ring (PSR). This ring structure makes it effective to account for
beam rotational symmetry, but not suitable for dose calculations due to
rectangular jaw settings. Hence, we first convert PSR source model to its
phase-space let (PSL) representation. Then in dose calculation, different types
of sub-sources were separately sampled. Source sampling and particle transport
were iterated. So that the particles being sampled and transported
simultaneously are of same type and close in energy to alleviate GPU thread
divergence. We also present an automatic commissioning approach to adjust the
model for a good representation of a clinical linear accelerator . Weighting
factors were introduced to adjust relative weights of PSRs, determined by
solving a quadratic minimization problem with a non-negativity constraint. We
tested the efficiency gain of our model over a previous source model using PSL
files. The efficiency was improved by 1.70 ~ 4.41, due to the avoidance of long
data reading and transferring. The commissioning problem can be solved in ~20
sec. Its efficacy was tested by comparing the doses computed using the
commissioned model and the uncommissioned one, with measurements in different
open fields in a water phantom under a clinical Varian Truebeam 6MV beam. For
the depth dose curves, the average distance-to-agreement was improved from
0.04~0.28 cm to 0.04~0.12 cm for build-up region and the root-mean-square (RMS)
dose difference after build-up region was reduced from 0.32%~0.67% to
0.21%~0.48%. For lateral dose profiles, RMS difference was reduced from
0.31%~2.0% to 0.06%~0.78% at inner beam and from 0.20%~1.25% to 0.10%~0.51% at
outer beam.
