Dynamic Configuration of CUDA Runtime Variables for CDP-Based Divide-and-Conquer Algorithms.

CUDA Dynamic Parallelism (CDP) is an extension of the GPGPU programming model proposed to better address irregular applications and recursive patterns of computation. However, processing memory demanding problems by using CDP is not straightforward, because of its particular memory organization. This work presents an algorithm to deal with such an issue. It dynamically calculates and configures the CDP runtime variables and the GPU heap on the basis of an analysis of the partial backtracking tree. The proposed algorithm was implemented for solving permutation combinatorial problems and experimented on two test-cases: N-Queens and the Asymmetric Travelling Salesman Problem. The proposed algorithm allows different CDP-based backtracking from the literature to solve memory demanding problems, adaptively with respect to the number of recursive kernel generations and the presence of dynamic allocations on GPU.