R2E2: low-latency path tracing of terabyte-scale scenes using thousands of cloud CPUs
R2E2: low-latency path tracing of terabyte-scale scenes using thousands of cloud CPUs
复制标题
R2E2：使用数千个云CPU对TB级场景进行低延迟路径追踪
DOI：
10.1145/3528223.3530171
复制
发表时间：
2022
影响因子：
6.2
通讯作者：
Winstein, Keith
中科院分区：
文献类型：
--
作者：
Fouladi, Sadjad;Shacklett, Brennan;Poms, Fait;Arora, Arjun;Ozdemir, Alex;Raghavan, Deepti;Hanrahan, Pat;Fatahalian, Kayvon;Winstein, Keith
在本文中，我们探索了使用由数千个小型无服务器云计算节点实时构建的“超级计算机”跟踪大规模场景的可行性。我们提出了R2E2 (real Elastic Ray Engine)，这是一个基于场景分解的并行渲染器，它可以快速获取数千个云CPU内核，从预先构建的场景BVH中并行加载场景几何图形到这些节点的聚合内存中，并使用用于通信光线数据的节点间消息传递服务执行全路径跟踪全局照明。为了平衡多个节点上的光线追踪工作，R2E2采用了面向服务的设计，基于负载估计，静态地将频繁遍历场景区域的几何和纹理数据复制到多个节点上，并动态地将光线追踪工作分配给承载所需数据的轻负载节点。我们将pbrt的光线-场景交叉组件移植到R2E2架构，并证明具有高达1tb的几何和纹理数据的场景（其中只有1/250的场景可以适合任何一个节点）可以在数十秒内使用AWS Lambda平台上的数千个小型无服务器节点以4K分辨率进行路径跟踪。
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.