{"title":"高性能图形数据管理和挖掘与X10","authors":"Miyuru Dayarathna","doi":"10.1109/ICDMW.2017.135","DOIUrl":null,"url":null,"abstract":"Graph data management and mining in HPC environments has been a widely discussed issue in recent times. In this talk I will describe the use of Partitioned Global Address Space languages for graph data mining and management. I will first discuss the rationale behind X10 based graph libraries and graph database benchmarks using ScaleGraph and XGDBench as examples. Next, I will take Acacia which is completely developed with X10 language as an example system and describe our experience with implementing such high performance system with X10. In this talk I will describe how RDF processing and Streaming extensions have been implemented in Acacia. Finally, I will highlight some of the notable areas which need further attention in future.","PeriodicalId":389183,"journal":{"name":"2017 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"High Performance Graph Data Management and Mining with X10\",\"authors\":\"Miyuru Dayarathna\",\"doi\":\"10.1109/ICDMW.2017.135\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Graph data management and mining in HPC environments has been a widely discussed issue in recent times. In this talk I will describe the use of Partitioned Global Address Space languages for graph data mining and management. I will first discuss the rationale behind X10 based graph libraries and graph database benchmarks using ScaleGraph and XGDBench as examples. Next, I will take Acacia which is completely developed with X10 language as an example system and describe our experience with implementing such high performance system with X10. In this talk I will describe how RDF processing and Streaming extensions have been implemented in Acacia. Finally, I will highlight some of the notable areas which need further attention in future.\",\"PeriodicalId\":389183,\"journal\":{\"name\":\"2017 IEEE International Conference on Data Mining Workshops (ICDMW)\",\"volume\":\"123 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE International Conference on Data Mining Workshops (ICDMW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDMW.2017.135\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Conference on Data Mining Workshops (ICDMW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDMW.2017.135","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
High Performance Graph Data Management and Mining with X10
Graph data management and mining in HPC environments has been a widely discussed issue in recent times. In this talk I will describe the use of Partitioned Global Address Space languages for graph data mining and management. I will first discuss the rationale behind X10 based graph libraries and graph database benchmarks using ScaleGraph and XGDBench as examples. Next, I will take Acacia which is completely developed with X10 language as an example system and describe our experience with implementing such high performance system with X10. In this talk I will describe how RDF processing and Streaming extensions have been implemented in Acacia. Finally, I will highlight some of the notable areas which need further attention in future.
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