Parallel and distributed local search in COMET
The availability of commodity multiprocessors and high-speed networks of workstations offer significant opportunities for addressing the increasing computational requirements of optimization applications. To leverage these potential benefits, it is important, however, to make parallel and distributed processing easily accessible to a wide audience of optimization programmers. This paper addresses this challenge by proposing parallel and distributed programming abstractions that keep the distance from sequential local search algorithms as small as possible. The abstractions, including parallel loops, interruptions, thread pools, and shared objects, are compositional and cleanly separate the optimization program and the parallel instructions. They have been evaluated experimentally on a variety of applications, including warehouse location and coloring, for which they provide significant speedups.
parallel distributed local search combinatorial optimization constraint programming language