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DTSTAMP:20230831T095745Z
LOCATION:Seehorn
DTSTART;TZID=Europe/Stockholm:20230626T150000
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UID:submissions.pasc-conference.org_PASC23_sess132_msa244@linklings.com
SUMMARY:A Data-Centric Perspective on Scientific Workflows in the Computin
 g Continuum
DESCRIPTION:Minisymposium\n\nSilvina Caino-Lores (University of Tennessee)
 \n\nConverging high-performance computing with systems and methods inspire
 d in Big Data Science and artificial intelligence is crucial to advance kn
 owledge discovery in a data-intensive landscape. Nowadays, complex scienti
 fic workflows must be able to extract knowledge and produce insight at eve
 ry stage from the instruments to the scientist. This requires the integrat
 ion of heterogeneous and geographically distributed computing environments
  into a<em> computing continuum</em> to seamlessly bridge simulations, mac
 hine learning and data-driven analytics. Current approaches to large-scale
  computing are accommodating the need to support hyper-heterogeneous envir
 onments in which different ecosystems and devices work together. From edge
  devices to supercomputers, each hardware technology has unique physical p
 roperties that determine their performance for different tasks, leading to
  different system architectures, software stacks and data management metho
 ds that maximize their individual performance. This opens challenges on ho
 w to enable the interoperability of the different computing paradigms, pro
 gramming models, and data abstractions coexisting in the continuum. This t
 alk presents use cases and new research directions on a data-centric persp
 ective towards scientific wokflow composition in the computing continuum.\
 n\nDomain: Computer Science, Machine Learning, and Applied Mathematics &#8232;\n
 \nSession Chair: François Tessier (INRIA)
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