Divide and conquer skeleton on GPU

Parallelism is a suitable approach for speeding up the massive computations of applications, but parallel programming is difficult yet. Algorithmic skeleton is a parallel programming model that provides a high level of abstraction for programmers. This approach uses the pre-defined components to facilitate easier parallel programming. Divide and conquer (DC) is an appropriate parallel pattern for implementation as a skeleton. The solution of the original problem is obtained by dividing it into smaller sub-problems and solving them in parallel. Today, graphics processor unit (GPU) is an attractive computational processor for doing tasks in parallel, because it has a large number of process units. In this paper, divide and conquer skeleton on GPU has been proposed and named OC_GFV.DC_GPU is a divide and conquer skeleton that is implemented on GPU that using a consistent programming interface in C++ for easier parallel programming. Performance of this skeleton has been evaluated by mergesort and sobeledge detection. The results show that obtained speedup at this skeleton is more than 2 on GPU.