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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 at extreme scale

  • 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