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About

About the RayON project

RayON started as a clean-room reimplementation of the Ray Tracing in One Weekend series — a personal exercise to understand the mechanics of stochastic light transport from first principles. It grew into a full-featured interactive GPU path tracer used as an educational resource in the CS302 High-Performance Computing course for the ISC bachelor programme at HES-SO Valais.

The project demonstrates:

  • How one scene description drives two GPU back-ends, a software path tracer in CUDA and a hardware-accelerated one on OptiX, without duplicating the scene logic.
  • Progressive sample accumulation and interactive camera control at real-time frame rates, with a regulator that trades resolution for frame rate while the camera moves.
  • Physically-based materials, from Lambertian and microfacet conductors to thin films, clear coats and participating media, with next event estimation and multiple importance sampling.
  • Ray-marched signed distance shapes and volumetric clouds from procedural noise or OpenVDB data.

The source code is published under the GPL v3 licence and is intended as a learning resource as much as a working renderer.

About the author

Pierre-André Mudry is head of Computer Science and Communication degree programme and a professor at the University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis) in Sion, where he teaches and conducts research in the Systems Engineering department.

His work sits at the intersection of embedded systems, digital hardware design, and applied machine learning. He has a particular interest in bridging the gap between algorithm and silicon — from high-level language design for constrained devices through to FPGA implementation and IoT deployment. Recent work has explored edge inference, non-invasive flow metering with neural networks, and anomaly detection on resource-limited hardware.

Research interests

  • Embedded systems and processor architectures
  • FPGA design and hardware/software co-design
  • Internet of Things and edge computing
  • Applied machine learning on constrained devices
  • High-performance computing and GPU programming