{"title":"海报:超大规模高性能计算系统的内存意识集体I/O","authors":"Yin Lu, Yong Chen, R. Thakur, Zhuang Yu","doi":"10.1145/2491661.2481430","DOIUrl":null,"url":null,"abstract":"The continuing decrease in memory capacity per core and the increasing disparity between core count and off-chip memory bandwidth create significant challenges for I/O operations in exascale systems. The exascale challenges require rethinking collective I/O for the effective exploitation of the correlation among I/O accesses in the exascale system. In this study we introduce a Memory-Conscious Collective I/O considering the constraint of the memory space. 1)Restricts aggregation data traffic within disjointed subgroups 2)Coordinates I/O accesses in intra-node and inter-node layer 3)Determines I/O aggregators at run time considering data distribution and memory consumption among processes.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"27 1","pages":"1362-1362"},"PeriodicalIF":0.0000,"publicationDate":"2013-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Poster: Memory-Conscious Collective I/O for Extreme-Scale HPC Systems\",\"authors\":\"Yin Lu, Yong Chen, R. Thakur, Zhuang Yu\",\"doi\":\"10.1145/2491661.2481430\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The continuing decrease in memory capacity per core and the increasing disparity between core count and off-chip memory bandwidth create significant challenges for I/O operations in exascale systems. The exascale challenges require rethinking collective I/O for the effective exploitation of the correlation among I/O accesses in the exascale system. In this study we introduce a Memory-Conscious Collective I/O considering the constraint of the memory space. 1)Restricts aggregation data traffic within disjointed subgroups 2)Coordinates I/O accesses in intra-node and inter-node layer 3)Determines I/O aggregators at run time considering data distribution and memory consumption among processes.\",\"PeriodicalId\":6346,\"journal\":{\"name\":\"2012 SC Companion: High Performance Computing, Networking Storage and Analysis\",\"volume\":\"27 1\",\"pages\":\"1362-1362\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-06-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 SC Companion: High Performance Computing, Networking Storage and Analysis\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2491661.2481430\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2491661.2481430","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Poster: Memory-Conscious Collective I/O for Extreme-Scale HPC Systems
The continuing decrease in memory capacity per core and the increasing disparity between core count and off-chip memory bandwidth create significant challenges for I/O operations in exascale systems. The exascale challenges require rethinking collective I/O for the effective exploitation of the correlation among I/O accesses in the exascale system. In this study we introduce a Memory-Conscious Collective I/O considering the constraint of the memory space. 1)Restricts aggregation data traffic within disjointed subgroups 2)Coordinates I/O accesses in intra-node and inter-node layer 3)Determines I/O aggregators at run time considering data distribution and memory consumption among processes.