{"title":"Tile LU分解的数据驱动执行","authors":"George Matheou, C. Kyriacou, P. Evripidou","doi":"10.1145/3292533.3292534","DOIUrl":null,"url":null,"abstract":"The objective of this paper is to analyze, develop and evaluate the tile LU Decomposition using the FREDDO framework. FREDDO is a C++ framework, based on the DDM model of execution, that supports efficient data-driven execution on conventional processors. The performance evaluation shows that FREDDO scales well and tolerates scheduling overheads and memory latencies effectively. The LU implementation is evaluated in both single-node and distributed execution environments. In both cases our framework achieves very good speedups, especially in the larger problem sizes. Particularly, our framework achieves up to 97% of the maximum possible speedup on a single-node and up to 90% of the maximum possible speedup on a 4-node cluster with a total of 128 cores.","PeriodicalId":195082,"journal":{"name":"Proceedings of the Sixth Workshop on Data-Flow Execution Models for Extreme Scale Computing","volume":"128 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Data-Driven execution of the Tile LU Decomposition\",\"authors\":\"George Matheou, C. Kyriacou, P. Evripidou\",\"doi\":\"10.1145/3292533.3292534\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The objective of this paper is to analyze, develop and evaluate the tile LU Decomposition using the FREDDO framework. FREDDO is a C++ framework, based on the DDM model of execution, that supports efficient data-driven execution on conventional processors. The performance evaluation shows that FREDDO scales well and tolerates scheduling overheads and memory latencies effectively. The LU implementation is evaluated in both single-node and distributed execution environments. In both cases our framework achieves very good speedups, especially in the larger problem sizes. Particularly, our framework achieves up to 97% of the maximum possible speedup on a single-node and up to 90% of the maximum possible speedup on a 4-node cluster with a total of 128 cores.\",\"PeriodicalId\":195082,\"journal\":{\"name\":\"Proceedings of the Sixth Workshop on Data-Flow Execution Models for Extreme Scale Computing\",\"volume\":\"128 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-09-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Sixth Workshop on Data-Flow Execution Models for Extreme Scale Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3292533.3292534\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Sixth Workshop on Data-Flow Execution Models for Extreme Scale Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3292533.3292534","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Data-Driven execution of the Tile LU Decomposition
The objective of this paper is to analyze, develop and evaluate the tile LU Decomposition using the FREDDO framework. FREDDO is a C++ framework, based on the DDM model of execution, that supports efficient data-driven execution on conventional processors. The performance evaluation shows that FREDDO scales well and tolerates scheduling overheads and memory latencies effectively. The LU implementation is evaluated in both single-node and distributed execution environments. In both cases our framework achieves very good speedups, especially in the larger problem sizes. Particularly, our framework achieves up to 97% of the maximum possible speedup on a single-node and up to 90% of the maximum possible speedup on a 4-node cluster with a total of 128 cores.