{"title":"经典算法在高线程多核gpu上的理论分析","authors":"Lin Ma, Kunal Agrawal, R. Chamberlain","doi":"10.1145/2555243.2555285","DOIUrl":null,"url":null,"abstract":"The Threaded many-core memory (TMM) model provides a framework to analyze the performance of algorithms on GPUs. Here, we investigate the effectiveness of the TMM model by analyzing algorithms for 3 classic problems -- suffix tree/array for string matching, fast Fourier transform, and merge sort -- under this model. Our findings indicate that the TMM model can explain and predict previously unexplained trends and artifacts in experimental data.","PeriodicalId":286119,"journal":{"name":"ACM SIGPLAN Symposium on Principles & Practice of Parallel Programming","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Theoretical analysis of classic algorithms on highly-threaded many-core GPUs\",\"authors\":\"Lin Ma, Kunal Agrawal, R. Chamberlain\",\"doi\":\"10.1145/2555243.2555285\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Threaded many-core memory (TMM) model provides a framework to analyze the performance of algorithms on GPUs. Here, we investigate the effectiveness of the TMM model by analyzing algorithms for 3 classic problems -- suffix tree/array for string matching, fast Fourier transform, and merge sort -- under this model. Our findings indicate that the TMM model can explain and predict previously unexplained trends and artifacts in experimental data.\",\"PeriodicalId\":286119,\"journal\":{\"name\":\"ACM SIGPLAN Symposium on Principles & Practice of Parallel Programming\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-02-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM SIGPLAN Symposium on Principles & Practice of Parallel Programming\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2555243.2555285\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM SIGPLAN Symposium on Principles & Practice of Parallel Programming","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2555243.2555285","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Theoretical analysis of classic algorithms on highly-threaded many-core GPUs
The Threaded many-core memory (TMM) model provides a framework to analyze the performance of algorithms on GPUs. Here, we investigate the effectiveness of the TMM model by analyzing algorithms for 3 classic problems -- suffix tree/array for string matching, fast Fourier transform, and merge sort -- under this model. Our findings indicate that the TMM model can explain and predict previously unexplained trends and artifacts in experimental data.
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