{"title":"Enhancing the performance and robustness of the FEAST eigensolver","authors":"Brendan Gavin, E. Polizzi","doi":"10.1109/HPEC.2016.7761592","DOIUrl":"https://doi.org/10.1109/HPEC.2016.7761592","url":null,"abstract":"The FEAST algorithm is a subspace iteration method that uses a spectral projector as a rational filter in order to efficiently solve interior eigenvalue problems in parallel. Although the solutions from the FEAST algorithm converge rapidly in many cases, convergence can be slow in situations where the eigenvalues of a matrix are densely populated near the edges of the search interval of interest, which can be detrimental to parallel load balancing. This work introduces two methods that allow one to improve the convergence robustness of the FEAST algorithm in these situations without having to increase the amount of computation. Selected numerical examples are presented and discussed.","PeriodicalId":308129,"journal":{"name":"2016 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117005677","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}
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}
{"title":"Software systems for high-performance quantum computing","authors":"T. Humble, Keith A. Britt","doi":"10.1109/HPEC.2016.7761628","DOIUrl":"https://doi.org/10.1109/HPEC.2016.7761628","url":null,"abstract":"Quantum computing promises new opportunities for solving hard computational problems, but harnessing this novelty requires breakthrough concepts in the design, operation, and application of computing systems. We define some of the challenges facing the development of quantum computing systems as well as software-based approaches that can be used to overcome these challenges. Following a brief overview of the state of the art, we present models for the quantum programming and execution models, the development of architectures for hybrid high-performance computing systems, and the realization of software stacks for quantum networking. This leads to a discussion of the role that conventional computing plays in the quantum paradigm and how some of the current challenges for exascale computing overlap with those facing quantum computing.","PeriodicalId":308129,"journal":{"name":"2016 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124909256","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}