PISTON: A Portable Cross-Platform Framework for Data-Parallel Visualization Operators

This framework is based on linear operators defined on polygonal meshes, and furnishes a variety of processing applications, such as shape approximation and compact representation,

To further improve visualization of the relationships be- tween different gene’s expression, particularly for genes dis- played on non-adjacent parallel coordinate axes, we have

Practicability for Future Ray Tracing Systems While the previous results have shown that our approach can produce better results than current approaches based on ei- ther refitting

We further show comprehensive performance results using this pipeline with multiple datasets and demonstrate that cross-processor occlusion can improve the performance of

We propose two shared cache aware parallel isosurface algorithms, one based on marching tetrahedra, and one using a min-max tree as acceleration data structure1. We theoretically

If the sequential filter already uses a locator for its output points, it is more likely to use thread local locators in its parallel version.. The second merge algorithm is then

In the first step, input data is transformed into coefficients in the wavelet space using filter banks.. In the wavelet space, the magnitude of each co- efficient is correlated to

In this work, we bench- mark data-parallel primitives in PyTorch, and investigate its application to GPU volume rendering using two distinct DPP formulations: a parallel scan and