Parallel proximity computation on heterogeneous computing systems for graphics applications

Abstract

Proximity computation is one of the most fundamental geometric operations for various applications including physically-based simulations, computer graphics, robotics, Etc. Also proximity computation is one of the most time consuming parts in various applications. There have been numerous attempts to accelerate the queries like adopting an acceleration hierarchy to cull redundant computations. Even though these methods are general and improve the performance of various proximity queries by several orders of magnitude, there are ever growing demands for further improving the performance of proximity queries, since the model complexities are also ever growing. Recently, the number of cores on a single chip has continued to increase in order to achieve a higher computing power. Also, various heterogeneous computing architectures consisting of dierent types of parallel computing resources have been introduced. However, prior acceleration techniques such as using acceleration hierarchies gave less consideration for utilizing such parallel architectures and heterogeneous computing environments. Since we are increasingly seeing more heterogeneous computing environments, it is getting more important to utilize them for proximity queries, in an ecient and robust manner. In this thesis, we employ heterogeneous parallel computing architectures to accelerate the performance of proximity computation for various applications. To eciently utilize heterogeneous computing resources, we propose parallel computing systems and algorithms for proximity computation. We start with a specic proximity query and design a novel ecient parallel algorithm based on knowledge of the query and computing resources. Then we extend our method to various proximity queries and propose a general proximity computing framework. Also we improve the utilization eciency of computing resources by designing optimization-based scheduling algorithm. With the proposed methods, an order of magnitude improvem...