The maximum flow problem is a type of network optimization problem in the flow graph theory. Many important applications used the maximum flow problem and thus it has been studied by many researchers using different methods. Ford Fulkerson algorithm is the most popular algorithm that used to solve the maximum flow problem, but its complexity is high. In this paper, a parallel Genetic algorithm is applied to find a maximum flow in a weighted directed graph, by finding the objective function value for each augmenting path from the source to the sink simultaneously in the parallel steps in every iteration. The algorithm is implemented using Message Passing Interface (MPI) library, and results are conducted from a real distributed system IMAN1 supercomputer and were compared with a sequential version of Genetic-Maxflow. The simulation results show this parallel algorithm speedup the running time by achieving up to 50% parallel efficiency.
Abstract-The tree-hypercube (TH) interconnection network is relatively a new interconnection network, which is constructed from tree and hypercube topologies. TH is developed to support parallel algorithms for solving computation and communication intensive problems. In this paper, we propose a new parallel multiplication algorithm on TH network to present broadcast communication operation for TH using store-and-forward technique, namely, one-to-all broadcast operation which allows a message to be transmitted through the shortest path from the source node to all other nodes. The proposed algorithm is implemented and evaluated in terms of running time, efficiency and speedup with different data size using IMAN1. The experimental results show that the runtime, efficiency and the speedup of the proposed algorithm decrease as a number of processors increases for all cases of matrices size of 10001000, 20002000, and 40004000.
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