  Radiation Treatment Planning (RTP) is the process of planning the appropriate
external beam radiotherapy to combat cancer in human patients. RTP is a complex
and compute-intensive task, which often takes a long time (several hours) to
compute. Reducing this time allows for higher productivity at clinics and more
sophisticated treatment planning, which can materialize in better treatments.
The state-of-the-art in medical facilities uses general-purpose processors
(CPUs) to perform many steps in the RTP process. In this paper, we explore the
use of accelerators to reduce RTP calculating time. We focus on the step that
calculates the dose using the Graphics Processing Unit (GPU), which we believe
is an excellent candidate for this computation type. Next, we create a highly
optimized implementation for a custom Sparse Matrix-Vector Multiplication
(SpMV) that operates on numerical formats unavailable in state-of-the-art SpMV
libraries (e.g., Ginkgo and cuSPARSE). We show that our implementation is
several times faster than the baseline (up-to 4x) and has a higher operational
intensity than similar (but different) versions such as Ginkgo and cuSPARSE.
