{"title":"Internal sorting algorithm for large-scale data based on GPU-assisted","authors":"Liu Shenghui, Mao Junfeng, Che Nan","doi":"10.1109/MIC.2013.6758043","DOIUrl":null,"url":null,"abstract":"This paper presents an internal sorting algorithm by GPU assisted. It consists of two algorithms: a GPU-based internal sorting algorithm and a CPU-based multi-way merging algorithm. The algorithm divided the large-scale data into multiple chunks to fit GPU global memory. Then copy the chunks to the GPU's global memory one by one, and sort them by GPU quicksort algorithm. Then we merge these sub-sequences to one sorted sequence by CPU. We use the loser tree algorithm to reduce the number of comparisons when merging. Finally, this algorithm is tested using a variety of data distribution. The experimental results show that our algorithm improves the efficiency of large-scale data sorting effectively.","PeriodicalId":404630,"journal":{"name":"Proceedings of 2013 2nd International Conference on Measurement, Information and Control","volume":"90 2","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 2013 2nd International Conference on Measurement, Information and Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MIC.2013.6758043","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
This paper presents an internal sorting algorithm by GPU assisted. It consists of two algorithms: a GPU-based internal sorting algorithm and a CPU-based multi-way merging algorithm. The algorithm divided the large-scale data into multiple chunks to fit GPU global memory. Then copy the chunks to the GPU's global memory one by one, and sort them by GPU quicksort algorithm. Then we merge these sub-sequences to one sorted sequence by CPU. We use the loser tree algorithm to reduce the number of comparisons when merging. Finally, this algorithm is tested using a variety of data distribution. The experimental results show that our algorithm improves the efficiency of large-scale data sorting effectively.
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