AG 1, AG 2, AG 3, INET, AG 4, AG 5, SWS, RG1, MMCI   Info

Public Audience

English

60 Minutes

Saarbrücken

As the computing landscape is being reshaped by the dramatic shift towards ubiquitous parallelism, and by the sheer scale of data, extracting performance from existing applications gives rise to formidable challenges. In digital geometry processing, the problem gets amplified by data irregularity (e.g. meshes) and the predominately serial nature of traditional algorithmic solutions.  As a results the gap between the high performance promise of modern hardware and the actual performance seems to grow wider.

In this talk, I will discuss the impact of data structures and problem abstraction on performance. In particular, I will outline how high performance can be gained through a lean data representation which allows channeling parallelism through linear algebra kernels regardless of the underlying granularity. I will illustrate the impact of problem abstraction on challenging and far reaching scenarios including Voronoi diagrams (VD)/centroidal Voronoi tessellations (CVT) on surface meshes, subdivision surfaces, as well as matrix assembly in finite element analysis.

Jennifer Müller

2900

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