A FPGA Optimization Tool Based on a Multi-island Genetic Algorithm Distributed over Grid Environments

In this work we present a Grid implementation of a FPGA optimization tool. The application is based on a Distributed Genetic Algorithm (DGA). It solves the placement and routing problem into the FPGA design cycle. The Grid infrastructure is based both on gLite middleware and GridWay metascheduler. The DGA’s different islands are sent to the Working Nodes (WN), where they evolve as remote jobs. We implemented a migration system between islands based on centralizing the exchanging data on a local node. Parting from this data, the local node builds new islands and the evolution continues until the stop criterion is reached. Obtained results show us that the main benefit of the distributed model is a large reduction of the execution time. By using the distributed platform users can launch more complex tasks and increase the number of experiments comparing with sequential execution, expending less amounts of time and effort.