Publication: Using statistical analysis to fine-tune the results of knapsack-based computational platform benchmarking
Дата
2019
Авторы
Journal Title
Journal ISSN
Volume Title
Издатель
Аннотация
© 2019 IEEE In previous papers, we composed an algorithmic foundation for computational platform benchmarking of well-known exact algorithms for the Knapsack Problem. We suggested using the run time of these algorithms with fixed inputs as the performance estimates. We then derived a single performance estimate, equally impacted by each of the algorithms. Although this approach makes for a reasonable general-purpose benchmark, equalizing the impact of different algorithms is not completely legitimate, as they have different processing requirements. In this paper, we perform an in-depth analysis of algorithm operational requirements and try to fine-tune the integral estimates to describe special-purpose (e.g. data compression or encipherment/decipherment) platforms more accurately.
Описание
Ключевые слова
Цитирование
Natalia, K. Using statistical analysis to fine-tune the results of knapsack-based computational platform benchmarking / Natalia, K., Mikhail, K., Georgii, B. // Proceedings of the 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2019. - 2019. - P. 1816-1820. - 10.1109/EIConRus.2019.8657218
URI
https://www.doi.org/10.1109/EIConRus.2019.8657218
https://www.scopus.com/record/display.uri?eid=2-s2.0-85063468760&origin=resultslist
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000469452600423
https://openrepository.mephi.ru/handle/123456789/16753
https://www.scopus.com/record/display.uri?eid=2-s2.0-85063468760&origin=resultslist
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000469452600423
https://openrepository.mephi.ru/handle/123456789/16753