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Data Flow and Data Analysis at
extreme scale
Faisal Imran
 Many of the next generation Grand Challenges
applications will be “Data intensive”
 Large volumes of data need to be moved among
processing elements
 Attention has to be paid to the data flow
 Data required for computation may reside near to
processing elements or may be far away as a central
repository
 Systematic development of data input and output is
great challenge and need investment
Data Flow and Data Analysis at extreme scale
Overview
 Novel Architecture is developed that give advantage of
localized processing or solid state disk processing
 These architecture change the programming model
 Preliminary set of recommendations are needed
 Those recommendation must be shared with comparison
groups working on plan to address HPC
 Coordinated investment in developing these technologies is
needed
 Ad hoc and diffuse funding models are unlikely to succeed
Data Flow and Data Analysis at extreme scale
Overview
 Success required single organization to get the
leading role in coordinating this investment
Data Flow and Data Analysis at extreme scale
Overview
 On what basis next generation Grand Challenges
applications will be “Data intensive”
 What are possible steps of data moving toward processing
elements
 Which step of data flow needs attention most and why?
 Identify challenging aspects in systematic development
Data Flow and Data Analysis at extreme scale
Research Questions
 What are aspects that give advantage of using Novel
Architecture in localized processing
 What are aspects that give advantage of using Novel
Architecture in solid state disk processing
 Identify the reasons in Novel Architecture that change the
programing model
 What are the reasons needed for Coordinated investment
in developing technologies
Data Flow and Data Analysis at extreme scale
Research Questions
 On what basis Ad hoc and diffuse funding models are
unlikely to succeed ?
 What are the reasons to give leading role to single
organization?
Data Flow and Data Analysis at extreme scale
Research Questions

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Data flow and data analysis

  • 1. Data Flow and Data Analysis at extreme scale Faisal Imran
  • 2.  Many of the next generation Grand Challenges applications will be “Data intensive”  Large volumes of data need to be moved among processing elements  Attention has to be paid to the data flow  Data required for computation may reside near to processing elements or may be far away as a central repository  Systematic development of data input and output is great challenge and need investment Data Flow and Data Analysis at extreme scale Overview
  • 3.  Novel Architecture is developed that give advantage of localized processing or solid state disk processing  These architecture change the programming model  Preliminary set of recommendations are needed  Those recommendation must be shared with comparison groups working on plan to address HPC  Coordinated investment in developing these technologies is needed  Ad hoc and diffuse funding models are unlikely to succeed Data Flow and Data Analysis at extreme scale Overview
  • 4.  Success required single organization to get the leading role in coordinating this investment Data Flow and Data Analysis at extreme scale Overview
  • 5.  On what basis next generation Grand Challenges applications will be “Data intensive”  What are possible steps of data moving toward processing elements  Which step of data flow needs attention most and why?  Identify challenging aspects in systematic development Data Flow and Data Analysis at extreme scale Research Questions
  • 6.  What are aspects that give advantage of using Novel Architecture in localized processing  What are aspects that give advantage of using Novel Architecture in solid state disk processing  Identify the reasons in Novel Architecture that change the programing model  What are the reasons needed for Coordinated investment in developing technologies Data Flow and Data Analysis at extreme scale Research Questions
  • 7.  On what basis Ad hoc and diffuse funding models are unlikely to succeed ?  What are the reasons to give leading role to single organization? Data Flow and Data Analysis at extreme scale Research Questions