Point Distribution Tensor Computation on Heterogeneous Systems

Big data in observational and computational sciences impose increasing challenges on data analysis. In particular, data from light detection and ranging (LIDAR) measurements are questioning conventional methods of CPU-based algorithms due to their sheer size and complexity as needed for decent accuracy. These data describing terrains are natively given as big point clouds consisting of millions of independent coordinate locations from which meaningful geometrical information content needs to be extracted. The method of computing the point distribution tensor is a very promising approach, yielding good results to classify domains in a point cloud according to local neighborhood information. However, an existing KD-Tree parallel approach, provided by the VISH visualization framework, may very well take several days to deliver meaningful results on a real-world dataset. Here we present an optimized version based on uniform grids implemented in OpenCL that is able to deliver results of equal accuracy up to 24 times faster on the same hardware. The OpenCL version is also able to benefit from a heterogeneous environment and we analyzed and compared the performance on various CPU, GPU and accelerator hardware platforms. Finally, aware of the heterogeneous computing trend, we propose two low-complexity dynamic heuristics for the scheduling of independent dataset fragments in multi-device heterogenous systems.