Accelerating adaptive inverse distance weighting interpolation algorithm on a graphics processing unit.

ABSTRACT: This paper focuses on designing and implementing parallel adaptive inverse distance weighting (AIDW) interpolation algorithms by using the graphics processing unit (GPU). The AIDW is an improved version of the standard IDW, which can adaptively determine the power parameter according to the data points' spatial distribution pattern and achieve more accurate predictions than those predicted by IDW. In this paper, we first present two versions of the GPU-accelerated AIDW, i.e. the naive version without profiting from the shared memory and the tiled version taking advantage of the shared memory. We also implement the naive version and the tiled version using two data layouts, structure of arrays and array of aligned structures, on both single and double precision. We then evaluate the perform

SUBMITTER: Mei G 

PROVIDER: S-EPMC5627094 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature