  Data management on GPUs has become increasingly relevant due to a tremendous
rise in processing power and available GPU memory. Similar to main-memory
systems, there is a need for performant GPU-resident index structures to speed
up query processing. Unfortunately, mapping indexes efficiently to the highly
parallel and hard-to-program hardware is challenging and often fails to yield
the desired performance and flexibility. Instead of proposing yet another
hand-tailored index, we investigate whether we can exploit an indexing
mechanism that is already built into modern GPUs: The raytracing hardware
accelerator provided by NVIDIA RTX GPUs. To do so, we re-phrase the database
indexing problem as a raytracing problem, where we express the dataset to be
indexed as objects in a 3D scene, and point/range lookups as rays across the
scene. In this combination, coined RX in the following, lookups are performed
as intersection tests in hardware by dedicated raytracing cores. To analyze the
pros, cons, and usefulness of the raytracing pipeline for database indexing, we
carefully evaluate RX along fourteen dimensions and demonstrate its
competitiveness and potential in a large variety of situations.
