$k$-Way In-Place Merge by CPU-GPU Cooperative Processing

We propose a high-performance
$k$
-way in-place merging algorithm by CPU-GPU cooperative processing. Current merging algorithms are either
$k$
-way in-place with CPUs only or in-place for data smaller than the GPU memory size. To solve this problem, we extend these merging algorithms to be more adaptable to a wider range of applications by improving the segmentation, reordering, and inter-segment merging methods used in the current merging algorithms by using both CPUs and GPUs cooperatively. We conducted experiments to evaluate the performance of our proposed algorithm and analyzed its detailed behavior by changing parameters and data distributions. We also applied the proposed merging algorithm to the merge sort algorithm to verify its performance.