Towards generic solver of combinatorial optimization problems with autonomous agents in P2P networks
ISHPC'05/ALPS'06 Proceedings of the 6th international symposium on high-performance computing and 1st international conference on Advanced low power systems
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In this paper, we propose a new class of parallelbranch-and-bound (B&B) schemes. The main idea of the scheme is to focus on the functional parallelism instead of conventional data parallelism, and to support such a heterogeneous and irregular parallelism by using a collection of autonomous agents distributed over the network. After examining several design issues toward the implementation of a prototype of the distributed B&B system, we illustrate the result of our preliminary experiments conducted to estimate the performance of the proposed scheme. The result shows that it could cause a significant performance improvement if each agent autonomously changes its function type according to the change of the underlying environment.