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A GPU Implementation of Bulk Execution of the Dynamic Programming for the Optimal Polygon Triangulation

机译:最优多边形三角剖分的动态编程批量执行的GPU实现

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Abstract, the optimal polygon triangulation problem tor a convex polygon is an optimization problem to find a triangulation with minimum total weight. It is known that this problem can be solved using the dynamic programming technique in O(n~3) time. The main contribution of this paper is to present an efficient parallel implementation of this O(n~3)-time algorithm for a lot of instances on the GPU (Graphics Processing Unit). In our proposed GPU implementation, we focused on the computation for a lot of instances and considered programming issues of the GPU architecture such as coalesced access of the global memory, warp divergence. Our implementation solves the optimal polygon triangulation problem for 1024 convex 1024-gons in 4.77s on the XVIDIA TITAN X, while a conventional CPU implementation runs in 241.53 s. Thus, our GPU implementation attains a speedup factor of 50.6.
机译:摘要:凸多边形的最优三角剖分问题是寻找总权最小的三角剖分的优化问题。众所周知,使用O(n〜3)时间的动态编程技术可以解决此问题。本文的主要贡献在于,针对GPU(图形处理单元)上的许多实例,提出了这种O(n〜3)时间算法的高效并行实现。在我们提出的GPU实现中,我们专注于许多实例的计算,并考虑了GPU体系结构的编程问题,例如全局内存的合并访问,翘曲散度。我们的实现解决了XVIDIA TITAN X上4.77s内1024个凸1024个多边形的最优多边形三角剖分问题,而传统的CPU实现在241.53 s内运行。因此,我们的GPU实现达到了50.6的加速因子。

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