Using Ray Tracing cores for accelerating a KNN algorithm.
This project studies the use of ray tracing cores - a novel type of GPU hardware used for accelerating ray tracing algorithms - for accelerating a k-nearest neighbours algorithm. Final project of GPGPU - General purpose computing in GPUs at Universidad de la República (UDELAR).
The aim of this project was to do a research of the state of the art in using ray tracing cores - introduced in Nvidia Turing architecture - for general purpose computing. Specifically, we studied the use of this new hardware for accelerating the KNN algorithm. This involved the analysis of how the original problem of the K nearest neighbours can be reformulated to a ray tracing problem.
Below you can find a report about the algorithm (Explanation, experimental analysis and performance reports) and its source code. Unfortunately, the report is only available in Spanish. The appendix section contains a detailed explanation of how to use the OptiX library for building a KNN algorithm that benefits from the ray tracing cores.
This project was a great opportunity to gather experience in:
- GPU programming.
- Investigation of the state of the art in GPUs and RT cores.
- Graphic programming with OptiX.
- Algorithm analysis.