Optimizing Multi-Dimensional Data-Index Algorithms for Mic Architectures

A data structure for geographical partitioning called multi-dimensional data-indexing enables effective CPU-based nearest-neighbor searches. Despite not being a natural match for Many-Integrated Core Architecture (MIC) implementation, depth-first search MultiDimensional Data-Indexing can nevertheless be successful with the right engineering choices. We suggested a technique that minimizes data structure memory trace by limiting the maximum height of the DFS Multi-Dimensional Data-Indexing. With tens of thousands to tens of millions of points in the MIC kernel code, we optimize the multi-core MIC NN search. In comparison to a single-core CPU of equivalent power, it is 20–40 times quicker. NN uses the knowledge obtained from improving MIC code to find ways to rewrite CPU code. As a consequence, the initial level of CTA and engineering choices to make the Multi-Dimensional DataIndexing search algorithm on CPU and MIC simpler account for the bulk of the parallel performance in this study. Threads inside each thread warp split onto several search pathways for the second level of CTA using MultiDimensional Data-Indexing. Thread divergence removes the majority of the performance benefit of employing multiple threads per thread-block. Experiments in this article reveal that small thread block sizes produce the best results.