  Optimization plays a central role in modern radiation therapy, where it is
used to determine optimal treatment machine parameters in order to deliver
precise doses adapted to each patient case. In general, solving the
optimization problems that arise can present a computational bottleneck in the
treatment planning process, as they can be large in terms of both variables and
constraints. In this paper, we develop a GPU accelerated optimization solver
for radiation therapy applications, based on an interior point method (IPM)
utilizing iterative linear algebra to find search directions. The use of
iterative linear algebra makes the solver suitable for porting to GPUs, as the
core computational kernels become standard matrix-vector or vector-vector
operations. Our solver is implemented in C++20 and uses CUDA for GPU
acceleration.
  The problems we solve are from the commercial treatment planning system
RayStation, developed by RaySearch Laboratories (Stockholm, Sweden), which is
used clinically in hundreds of cancer clinics around the world. RayStation
solves (in general) nonlinear optimization problems using a sequential
quadratic programming (SQP) method, where the main computation lies in solving
quadratic programming (QP) sub-problems in each iteration. GPU acceleration for
the solution of such QP sub-problems is the focus of the interior point method
of this work. We benchmark our solver against the existing QP-solver in
RayStation and show that our GPU accelerated IPM can accelerate the aggregated
time-to-solution for all QP sub-problems in one SQP solve by 1.4 and 4.4 times,
respectively, for two real patient cases.
