2016 IEEE High Performance Extreme Computing Conference (HPEC)最新文献

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Enhancing the performance and robustness of the FEAST eigensolver 提高了FEAST特征解算器的性能和鲁棒性
2016 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2016-05-27 DOI: 10.1109/HPEC.2016.7761592
Brendan Gavin, E. Polizzi
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引用次数: 3
LLMapReduce: Multi-level map-reduce for high performance data analysis LLMapReduce:用于高性能数据分析的多级map-reduce
2016 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2016-05-23 DOI: 10.1109/HPEC.2016.7761618
C. Byun, J. Kepner, W. Arcand, David Bestor, Bill Bergeron, V. Gadepally, M. Hubbell, P. Michaleas, J. Mullen, Andrew Prout, Antonio Rosa, Charles Yee, A. Reuther
{"title":"LLMapReduce: Multi-level map-reduce for high performance data analysis","authors":"C. Byun, J. Kepner, W. Arcand, David Bestor, Bill Bergeron, V. Gadepally, M. Hubbell, P. Michaleas, J. Mullen, Andrew Prout, Antonio Rosa, Charles Yee, A. Reuther","doi":"10.1109/HPEC.2016.7761618","DOIUrl":"https://doi.org/10.1109/HPEC.2016.7761618","url":null,"abstract":"The map-reduce parallel programming model has become extremely popular in the big data community. Many big data workloads can benefit from the enhanced performance offered by supercomputers. LLMapReduce provides the familiar map-reduce parallel programming model to big data users running on a supercomputer. LLMapReduce dramatically simplifies map-reduce programming by providing simple parallel programming capability in one line of code. LLMapReduce supports all programming languages and many schedulers. LLMapReduce can work with any application without the need to modify the application. Furthermore, LLMapReduce can overcome scaling limits in the map-reduce parallel programming model via options that allow the user to switch to the more efficient single-program-multiple-data (SPMD) parallel programming model. These features allow users to reduce the computational overhead by more than 10x compared to standard map-reduce for certain applications. LLMapReduce is widely used by hundreds of users at MIT. Currently LLMapReduce works with several schedulers such as SLURM, Grid Engine and LSF.","PeriodicalId":308129,"journal":{"name":"2016 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128251796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 15
Software systems for high-performance quantum computing 高性能量子计算软件系统
2016 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 1900-01-01 DOI: 10.1109/HPEC.2016.7761628
T. Humble, Keith A. Britt
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引用次数: 4
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