Local Search Variants for Hypercube Embedding

Abstract

The hypercube embedding problem, a restricted ver- sion of the general mapping problem, is the problem of mapping a set of communicating processes to a hy- percube multiprocessor. The goal is to find a map- ping that minimizes the average length of the paths between communicating processes. Iterative improve- ment heuristics for hypercube embedding, including a local search, a Kernighan-Lin, and a simulated an- nealing, are evaluated under different options includ- ing neighborhoods (all-swaps versus cube-neighbors), initial solutions (random versus greedy), and enhance- ments on terminating conditions (flat moves and up- hill moves). By varying these options we obtain a wide range of tradeoffs between execution time and solution quality.