Sort-last parallel rendering using a cluster of GPUs has been widely used as an efficient method for visualizing large-scale volume datasets. The performance of this method is constrained by load balancing when data parallelism is included. In previous works static partitioning could lead to self-balance when only task level parallelism is included. This paper presents a load balancing scheme that adapts to the characteristic of volume dataset when data parallelism is also employed. It effectively combines the hierarchical data structures (octree and BSP tree) in order to skip empty regions and distribute workload to corresponding rendering nodes. Moreover, it also exploits a 3D clustering method to determine visibility order and save the AGP bandwidths on each rendering node.
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