Abstract: Large-scale numerical simulation produces datasets with ever-growing size and complexity. In particular, unstructured meshes are encountered in many applications. Volume rendering provides a way to efficiently analyze such datasets. Recent advances in graphics hardware have enabled the implementation of efficient unstructured volume rendering algorithms on the GPU. However, GPU architecture limitations make these methods difficultly amenable to a parallel implementation, which is necessary to render very large datasets at interactive speeds and high resolutions. Many previous parallel approaches have focused on softwarebased algorithms. In this paper, we present a hybrid object-space/image-space CPU-GPU distributed parallel volume rendering method, taking advantage of the flexibility afforded by the CPU, including SIMD processing capabilities, and using GPUs to perform repetitive tasks like depth-sorting and compositing. We present the impact of the different phases on the overall rendering time as a function of node number.