Towards the Design of Systolic Genetic Search

Abstract

This paper elaborates on a new, fresh parallel optimization algorithm specially engineered to run on Graphic Processing Units (GPUs). The underlying operation relates to Systolic Computation. The algorithm, called Systolic Genetic Search (SGS) is based on the synchronous circulation of solutions through a grid of processing units and tries to profit from the parallel architecture of GPUs. The proposed model has shown to outperform a random search and two genetic algorithms for solving the Knapsack Problem over a set of increasingly sized instances. Additionally, the parallel implementation of SGS on a GeForce GTX 480 graphics processing unit (GPU), obtaining a runtime reduction up to 35 times.