{"title":"凸包计算中平行结构的评价","authors":"K. Nakata, Yasuaki Ito","doi":"10.1109/CANDAR.2016.0127","DOIUrl":null,"url":null,"abstract":"The main contribution of this paper is to show an implementation of the parallel convex hull algorithm on the Parallella architecture. Parallella is a single-board computer with 16 mesh-connected cores. We have considered the memory architecture and mesh-connected network of the Parallella architecture. We evaluated the computing time and the energy-efficiency by comparing with various computing platforms such as Raspberry Pi, desktop PC, and multicore server. The experimental results show that for 16384 points, although the computing time of Parallella is 17.50 times longer than that of 24-core multicore server, its energy-efficiency is 7.12 times higher.","PeriodicalId":322499,"journal":{"name":"2016 Fourth International Symposium on Computing and Networking (CANDAR)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Evaluation of the Parallella Architecture for the Convex Hull Computation\",\"authors\":\"K. Nakata, Yasuaki Ito\",\"doi\":\"10.1109/CANDAR.2016.0127\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The main contribution of this paper is to show an implementation of the parallel convex hull algorithm on the Parallella architecture. Parallella is a single-board computer with 16 mesh-connected cores. We have considered the memory architecture and mesh-connected network of the Parallella architecture. We evaluated the computing time and the energy-efficiency by comparing with various computing platforms such as Raspberry Pi, desktop PC, and multicore server. The experimental results show that for 16384 points, although the computing time of Parallella is 17.50 times longer than that of 24-core multicore server, its energy-efficiency is 7.12 times higher.\",\"PeriodicalId\":322499,\"journal\":{\"name\":\"2016 Fourth International Symposium on Computing and Networking (CANDAR)\",\"volume\":\"64 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 Fourth International Symposium on Computing and Networking (CANDAR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CANDAR.2016.0127\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 Fourth International Symposium on Computing and Networking (CANDAR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CANDAR.2016.0127","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Evaluation of the Parallella Architecture for the Convex Hull Computation
The main contribution of this paper is to show an implementation of the parallel convex hull algorithm on the Parallella architecture. Parallella is a single-board computer with 16 mesh-connected cores. We have considered the memory architecture and mesh-connected network of the Parallella architecture. We evaluated the computing time and the energy-efficiency by comparing with various computing platforms such as Raspberry Pi, desktop PC, and multicore server. The experimental results show that for 16384 points, although the computing time of Parallella is 17.50 times longer than that of 24-core multicore server, its energy-efficiency is 7.12 times higher.