MPI Collective Algorithm Selection and Quadtree Encoding

We explore the applicability of the quadtree encoding method to the run-time MPI collective algorithm selection problem. Measured algorithm performance data was used to construct quadtrees with different properties. The quality and performance of generated decision functions and in-memory decision systems were evaluated. Experimental data shows that in some cases, a decision function based on a quadtree structure with a mean depth of three, incurs on average as little as a 5% performance penalty. In all cases examined, experimental data can be fully represented with a quadtree containing a maximum of six levels. Our results indicate that quadtrees may be a feasible choice for both processing of the performance data and automatic decision function generation.