Open AccessImproving the Performance of the Contextual Spaces Re-Ranking Algorithm on Heterogeneous Systems
Author(s) -
Flávia Pisani,
Daniel Carlos Guimarães Pedronette,
Ricardo da Silva Torres,
Edson Borin
Publication year - 2015
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/wscad.2015.14278
Subject(s) - computer science , ranking (information retrieval) , code (set theory) , algorithm , execution time , range (aeronautics) , parallel computing , machine learning , programming language , set (abstract data type) , materials science , composite material
Re-ranking algorithms have been proposed to improve the effectiveness of Content-Based Image Retrieval (CBIR) systems by exploiting contextual information encoded in distance measures and ranked lists. In this paper, we show how we improved the efciency of one of these algorithms, called Contextual Spaces Re-Ranking. We propose a modication to the algorithm that reduces its execution time by 1.6x on average and improves its accuracy in most of our test cases. We also parallelized the implementation with OpenCL to use the CPU and GPU of an Accelerated Processing Unit (APU). Employing these devices to run different parts of the code resulted in speedups that range from 3.3x to 4.2x in comparison with the total execution time of the serial version.