The Design and Implementation of a GPU-enabled Multi-objective Tabu-search Intended for Real World and High-dimensional Applications | Zendy

Christos Tsotskas | Zendy; Timoleon Kipouros | Zendy; Mark Savill | Zendy

Open Access

The Design and Implementation of a GPU-enabled Multi-objective Tabu-search Intended for Real World and High-dimensional Applications

Author(s) -

Christos Tsotskas,

Timoleon Kipouros,

Mark Savill

Publication year - 2014

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2014.05.200

Subject(s) - computer science , cuda , heuristics , scalability , metaheuristic , tabu search , porting , curse of dimensionality , general purpose computing on graphics processing units , range (aeronautics) , graphics , parallel computing , computer engineering , distributed computing , algorithm , artificial intelligence , computer graphics (images) , materials science , software , database , composite material , programming language , operating system

Metaheuristics is a class of approximate methods based on heuristics that can effectively handle real world (usually NP-hard) problems of high-dimensionality with multiple objectives. An existing multi-objective Tabu-Search (MOTS2) has been re-designed by and ported onto Compute Unified Device Architecture (CUDA) so as to effectively deal with a scalable multi-objective problem with a range of decision variables. The high computational cost due to the problem complexity is addressed by employing Graphics Processing Units (GPUs), which alleviate the computational intensity. The main challenges of the re-implementation are the effective communication with the GPU and the transparent integration with the optimization procedures. Finally, future work is proposed towards heterogeneous applications, where improved features are accelerated by the GPUs

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