A Study on the Performance of Base-m Polynomial Selection Algorithm Using GPU

Basit Öğe Kaydı
dc.authoridÇabuk, Umut Can/0000-0002-5166-4670
dc.authoridDURMUS, Oguzhan/0000-0002-1941-695X
dc.authoridDalkilic, Feristah/0000-0001-7528-5109
dc.authorwosidÇabuk, Umut Can/AAF-7644-2019
dc.contributor.authorDurmus, Oguzhan
dc.contributor.authorCabuk, Umut Can
dc.contributor.authorDalkilic, Feristah
dc.date.accessioned2023-01-12T19:49:58Z
dc.date.available2023-01-12T19:49:58Z
dc.date.issued2020
dc.departmentN/A/Departmenten_US
dc.descriptionInternational Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME) -- APR 20-22, 2019 -- Antalya, TURKEYen_US
dc.description.abstractFactorization of large integers has been being considered as a challenging problem in computer science and engineering since the earliest times of the computer technology. Despite the comprehensive efforts, there is still no reported deterministic polynomial-time algorithm; however, its complexity class is in fact not yet decided. A fast and robust polynomial-time algorithm for this problem is required to increase the processing capabilities of current systems. Yet, there are also hesitations at the same time within the community, due to the potential security threats that may appear in such a case. The (asymptotically) fastest algorithm ever found so far to factor large integers is the general number field sieve. Its performance depends on selection of good polynomials, which requires a specific procedure for such a selection. Another significant performance factor surely is the power of the processing hardware and their peripherals. This article unveils and discusses the impacts of heterogeneous computing using a graphics processor units (GPU) instead of a central processing unit (CPU) on the performance of polynomial selection and so of factoring large integers. Further, the article presents implementation details and a comparative performance evaluation of the Base-m polynomial selection method to select good polynomials. Accordingly, the GPU is found to be more effective over larger numbers with more roots, while the CPU appeared more effective over smaller numbers with less roots, possibly due to the excessive overheads in the GPU processing procedures.en_US
dc.identifier.doi10.1007/978-3-030-36178-5_40
dc.identifier.endpage517en_US
dc.identifier.isbn978-3-030-36178-5
dc.identifier.isbn978-3-030-36177-8
dc.identifier.issn2367-4512
dc.identifier.issn2367-4512en_US
dc.identifier.startpage509en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-030-36178-5_40
dc.identifier.urihttps://hdl.handle.net/11454/75976
dc.identifier.volume43en_US
dc.identifier.wosWOS:000678771000040en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherSpringer International Publishing Agen_US
dc.relation.ispartofArtificial Intelligence And Applied Mathematics In Engineering Problemsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPolynomial selectionen_US
dc.subjectGeneral number field sieveen_US
dc.subjectGraphical processing unit (GPU)en_US
dc.subjectHeterogeneous computingen_US
dc.subjectRsaen_US
dc.titleA Study on the Performance of Base-m Polynomial Selection Algorithm Using GPUen_US
dc.typeConference Objecten_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu