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Radiation Treatment Planning (RTP) is the process of planning the appropriateexternal beam radiotherapy to combat cancer in human patients. RTP is a complexand compute-intensive task, which often takes a long time (several hours) tocompute. Reducing this time allows for higher productivity at clinics and moresophisticated 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 theuse of accelerators to reduce RTP calculating time. We focus on the step thatcalculates the dose using the Graphics Processing Unit (GPU), which we believeis an excellent candidate for this computation type. Next, we create a highlyoptimized implementation for a custom Sparse Matrix-Vector Multiplication(SpMV) that operates on numerical formats unavailable in state-of-the-art SpMVlibraries (e.g., Ginkgo and cuSPARSE). We show that our implementation isseveral times faster than the baseline (up-to 4x) and has a higher operationalintensity than similar (but different) versions such as Ginkgo and cuSPARSE.

Accelerating Radiation Therapy Dose Calculation with Nvidia GPUs