Profile URL: https://hdl.handle.net/20.500.12573/2721
Job Title:Öğr. Gör.
Email Address:salih.yalcin@agu.edu.tr
Main Affiliation:10. Rektörlük
Status: Current Staff
ORCID:
0000-0001-6476-4542
0000-0001-6476-4542YÖK Akademik: 6CDCF9907727F6CA
Google Scholar:
7_JljSYAAAAJ
7_JljSYAAAAJWeb of Science ID:
OTI-1446-2025
OTI-1446-2025Name Variants:
Yalcin, Salih Yalçın, S.
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Master Thesis Bilgisayar Algoritmalarının GPU ile Hızlandırılması(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) Yalçın, Salih; Alkan, Gülay YalçınTravelling Salesman Problem (TSP) is one of the significant problems in computer science which tries to find the shortest path for a salesman who needs to visit a set of cities and it involves in many computing problems such as networks, genome analysis, logistic etc. Using parallel executing paradigms, especially GPUs, is appealing in order to reduce the problem-solving time of TSP. One of the main issues in GPUs is to have limited GPU memory which would not be enough for the entire data. Therefore, transferring data from host device would reduce the performance in execution time. In this study, we present a methodology for compressing data to represent cities in the TSP so that we include more cities in GPU memory. We implement our methodology in Iterated Local Search (ILS) algorithm with 2-opt and show that our implementation presents 29% performance improvement compared to the state-of-the-art GPU implementation.Article Citation - Scopus: 4CompreCity: Accelerating the Traveling Salesman Problem on GPU With Data Compression(Elsevier, 2025) Yalcin, Salih; Usul, Hamdi Burak; Yalcin, GulayTraveling Salesman Problem (TSP) is one of the significant problems in computer science which tries to find the shortest path for a salesman who needs to visit a set of cities and it is involved in many computing problems such as networks, genome analysis, logistics etc. Using parallel executing paradigms, especially GPUs, is appealing in order to reduce the problem solving time of TSP. One of the main issues in GPUs is to have limited GPU memory which would not be enough for the entire data. Therefore, transferring data from the host device would reduce the performance in execution time. In this study, we applied three data compression methodologies to represent cities in the TSP such as (1) Using Greatest Common Divisor (2) Shift Cities to the Origin (3) Splitting Surface to Grids. Therefore, we include more cities in GPU memory and reduce the number of data transfers from the host device. We implement our methodology in Iterated Local Search (ILS) algorithm with 2-opt and The Lin-Kernighan-Helsgaun (LKH) Algorithm. We show that our implementation presents more than 25% performance improvement for both algorithms.