Affiliation : Department of Computer Science and Information Engineering, National Changhua University of Education
Country : Taiwan
Category : Computer Science & Information Technology
Volume, Issue, Month, Year : 6, 7, November, 2016
ABSTRACT
Graphics Processing Units (GPUs) have been emerged as powerful parallel compute platforms for various
application domains. A GPU consists of hundreds or even thousands processor cores and adopts Single
Instruction Multiple Threading (SIMT) architecture. Previously, we have proposed an approach that
optimizes the Tabu Search algorithm for solving the Permutation Flowshop Scheduling Problem (PFSP)
on a GPU by using a math function to generate all different permutations, avoiding the need of placing all
the permutations in the global memory. Based on the research result, this paper proposes another
approach that further improves the performance by avoiding duplicated computation among threads,
which is incurred when any two permutations have the same prefix. Experimental results show that the
GPU implementation of our proposed Tabu Search for PFSP runs up to 1.5 times faster than another GPU
implementation proposed by CzapiĆski and Barnes.
KEYWORDS
GPU, CUDA, Parallel algorithm, Tabu Search, Permutation Flowshop Scheduling Problem
For More Details : https://airccj.org/CSCP/vol6/csit65402.pdf
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