Load balancing for distributed branch & bound algorithms

The authors present a new load balancing strategy and its application to distributed branch & bound algorithms and demonstrate its efficiency by solving some NP-complete problems on a network of up to 256 transputers. The parallelization of their branch & bound algorithm is fully distributed. Every processor performs the same algorithm but each on a different part of the solution tree. In this case it is necessary to distribute subproblems among the processors to achieve a well balanced workload. Their load balancing method overcomes the problem of search overhead and idle times by an appropriate load model and avoids trashing effects by a feedback control method. Using this strategy they were able to achieve a speedup of up to 237.32 on a 256 processor network for very short parallel computation times, compared to an efficient sequential algorithm