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GPU Volume Voxelization: Exploration of the performance characteristics of different GPU-based implementations

Grigory Glukhov, Aleksandra Soltan
KTH Royal Institute of Technology, School of Electrical Engineering and Computer Science, SE-100 44 Stockholm, Sweden
KTH Royal Institute of Technology, 2019

@misc{glukhov2019gpu,

   title={GPU Volume Voxelization: Exploration of the performance characteristics of different GPU-based implementations},

   author={Glukhov, Grigory and Soltan, Alexandra},

   year={2019}

}

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In recent years, voxel-based modelling has seen a reintroduction to computer game development through massive graphics hardware improvements. Nevertheless, polygons continue to be the default building block of 3D objects, introducing a need for the transformation of polygon meshes into voxel-based models; this process is known as voxelization. Efficient voxelization algorithms take advantage of the flexibility and control offered by modern, programmable GPU pipelines. However, the variability in possible approaches poses the question of how different GPU-based implementations affect voxelization performance. This thesis explores the impact of GPU-based improvements by comparing four different implementations of a solid voxelization algorithm. The implementations include a naive transition from the CPU to the GPU, a non-branching execution path approach, data pre-processing, and a combination of the two previous approaches. Benchmarking experiments run on four, standard polygonal models and three graphics cards (NVIDIA and AMD) provide runtime and memory usage data for each implementation. A comparative analysis is performed on the basis of this data to determine the performance impact of the GPU-based adjustments to the voxelization algorithm implementation. Results indicate that the non-branching execution path approach yields clear improvements over the naive implementation, while data pre-processing has inconsistent performance and a large initial performance cost; the combination of the two improvements unsurprisingly leads to combined results. Therefore, the conclusive recommendation is using the non-branching execution path technique for GPU-based improvements.
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