An approach to mapping parallel programs on hypercube multiprocessors

In this work, we propose a heuristic algorithm based on genetic algorithm for the task-to-processor mapping problem in the context of local-memory multiprocessors with a hypercube interconnection topology. Hyper-cube multiprocessors have offered a cost effective and feasible approach to supercomputing through parallelism at the processor level by directly connecting a large number of low-cost processors with local memory which communicate by message passing instead of shared variables. We use concepts of the graph theory (task graph precedence to represent parallel programs, graph partitioning to solve the program decomposition problem, etc.) to model the problem. This problem is NP-complete which means heuristic approaches must be adopted. We develop a heuristic algorithm based on genetic algorithms to solve it.