A Distributed Multi-Node GPU Accelerated Parallel Rendering Scheme for Visualization Cluster Environment

Abstract

Due to its interactive and high quality rendering abilities, GPU ray-casting volume rendering method is very popular for the post-processing of scientific and engineering computing appliances. This method however is likely suffered from memory effect, for it will cause the algorithm failure when facing the big data appliances. This problem can be solved through massively parallel approaches. But on the other hand, the complex architecture of the current massively parallel machine environment leads to the more difficulty in the implementation of algorithms with adaptability and parallel scalability. Caused by the dual complexity of computing environments and software architecture, the development difficulty of high-performance algorithms is rapidly rising from now on. In this paper, we presented a distributed multi-node GPU accelerated parallel rendering scheme for seamless coupling low-level computing environments and high-level visualization software. Experiment results show that our scheme can offer stable and efficient run-time support for our multi-GPU ray casting volume render in visualization cluster. When using 8 multi-nodes GPU to visualize 17GB scientific data in a single time-step, the interactive high quality volume rendering only needs less than one second per frame. The results are one order of magnitude faster than the traditional parallel ray casting method run on 512 processor cores.