Bulk execution of the dynamic programming for the optimal polygon triangulation problem on the GPU

Abstract

Summary The bulk execution is to execute some computation for many different inputs in turn or at the same time. The main contribution of this paper is to propose a parallel processing technique for the bulk execution of the dynamic programming using the GPU (Graphics Processing Unit). Especially, we focus on the optimal polygon triangulation problem for a lot of polygons. We consider programming issues of the GPU architecture such as coalesced memory access of the global memory, warp divergence avoidance, and reduction of CUDA kernel calls. In the GPU implementation, we propose two thread assignment methods that efficiently perform the parallel execution with a lot of threads on thousands of cores in the GPU. The experimental results show that our GPU implementation on NVIDIA TITAN V attains a speed‐up factor of up to 106.05 and 26.78 over the single‐thread and 8‐thread CPU implementations on Intel Core i7‐6700K CPU, respectively.

Algorithmic advances in parallel architectures and energy‐efficient computing

“…Dynamic programming is an important algorithmic technique to find the optimal solution of a problem over an exponential number of solution candidates. Yamashita et al propose a parallel processing technique for the bulk execution of the dynamic programming on GPUs, assuming many instances of a problem. In particular, they focus on the optimal polygon triangulation problem for a lot of polygons.…”