The cache performance and optimizations of blocked algorithms
ASPLOS IV Proceedings of the fourth international conference on Architectural support for programming languages and operating systems
Future Generation Computer Systems
Journal of Computer and System Sciences
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Membrane Computing: An Introduction
Membrane Computing: An Introduction
Complexity classes in models of cellular computing with membranes
Natural Computing: an international journal
Ant Colony Optimization
Parallel Computing Experiences with CUDA
IEEE Micro
Understanding throughput-oriented architectures
Communications of the ACM
IEEE Computational Intelligence Magazine
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Computer Methods and Programs in Biomedicine
High performance evaluation of evolutionary-mined association rules on GPUs
The Journal of Supercomputing
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We present GPU implementations of two different nature-inspired optimization methods for well-known optimization problems. Ant Colony Optimization (ACO) is a two-stage population-based method modelled on the foraging behaviour of ants, while P systems provide a high-level computational modelling framework that combines the structure and dynamic aspects of biological systems (in particular, their parallel and non-deterministic nature). Our methods focus on exploiting data parallelism and memory hierarchy to obtain GPU factor gains surpassing 20x for any of the two stages of the ACO algorithm, and 16x for P systems when compared to sequential versions running on a single-threaded high-end CPU. Additionally, we compare performance between GPU generations to validate hardware enhancements introduced by Nvidia's Fermi architecture.