Details

Autor(en) / Beteiligte

• Fouladi, Sadjad
• Shacklett, Brennan
• Poms, Fait
• Arora, Arjun
• Ozdemir, Alex
• Raghavan, Deepti
• Hanrahan, Pat
• Fatahalian, Kayvon
• Winstein, Keith

Titel

R2E2: low-latency path tracing of terabyte-scale scenes using thousands of cloud CPUs

Ist Teil von

• ACM transactions on graphics, 2022-07, Vol.41 (4), p.1-12, Article 76

Ort / Verlag

New York, NY, USA: ACM

Erscheinungsjahr

2022

Link zum Volltext

Quelle

ACM Digital Library

Beschreibungen/Notizen

• In this paper we explore the viability of path tracing massive scenes using a "supercomputer" constructed on-the-fly from thousands of small, serverless cloud computing nodes. We present R2E2 (Really Elastic Ray Engine) a scene decomposition-based parallel renderer that rapidly acquires thousands of cloud CPU cores, loads scene geometry from a pre-built scene BVH into the aggregate memory of these nodes in parallel, and performs full path traced global illumination using an inter-node messaging service designed for communicating ray data. To balance ray tracing work across many nodes, R2E2 adopts a service-oriented design that statically replicates geometry and texture data from frequently traversed scene regions onto multiple nodes based on estimates of load, and dynamically assigns ray tracing work to lightly loaded nodes holding the required data. We port pbrt's ray-scene intersection components to the R2E2 architecture, and demonstrate that scenes with up to a terabyte of geometry and texture data (where as little as 1/250th of the scene can fit on any one node) can be path traced at 4K resolution, in tens of seconds using thousands of tiny serverless nodes on the AWS Lambda platform.