DDDDRRaW: A prototype toolkit for distributed real-time rendering on commodity clusters

We describe DDDDRRaW, a prototype toolkit for distributed real-time rendering on commodity clusters. In constrast to most work on cluster computing, DDDDRRaW supports a repeated, low-latency computation, the drawing of frames which must take place on a time scale of 30-100 ms. DDDDRRaW employs image layer decomposition, a rendering-specific work partitioning algorithm described and evaluated using simulation. In this paper we address implementation issues. In particular, one important issue we explore is how to exploit the potential parallelism afforded by the multiple hardware resources of each node: the CPU, the network adapter and the video card. We evaluate DDDDRRaW´s live performance on two small workstation clusters representing different points in the technology spectrum. Our results show that DDDDRRaW effectively exploits cluster resources to improve real-time rendering performance and should scale well to moderately sized clusters