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DTSTART;TZID=Europe/Stockholm:20240603T153000
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UID:submissions.pasc-conference.org_PASC24_sess157_msa248@linklings.com
SUMMARY:Optimizing Dataflow Pipelines from Self-Driving Labs to the Cloud
DESCRIPTION:Minisymposium\n\nMichela Taufer (University of Tennessee)\n\nT
 he rapid advancements in cloud computing and the integration of experiment
 al facilities, including self-driving labs, have resulted in an era where 
 scientists can generate unprecedented amounts of data and conduct more ext
 ensive analyses across various scientific domains, including chemistry, ma
 terials sciences, molecular biology, and drug design. This capability enab
 les a broader exploration of natural phenomena but also introduces signifi
 cant challenges in effectively composing and scaling dataflow pipelines. T
 his talk addresses these challenges by presenting innovative solutions for
  optimizing dataflow pipelines across cloud resources, thereby enhancing t
 he study and application of scientific dataflows.\n\nThis talk will cover 
 three main research components of our work when optimizing dataflow pipeli
 nes from self-driving labs to the cloud. First, we establish a taxonomy of
  common dataflow motifs ranging from simple producer-consumer pairs to com
 plex multi-scale pipelines, applying these motifs to real-world use cases.
  Second, we discuss methods to mitigate data loss and pipeline inefficienc
 ies, especially those arising from disparities in moving pipelines traditi
 onally executed on high performance computing systems to the cloud. Last, 
 we highlight our efforts to train and build a community of experts, emphas
 izing the development of tailored data analytics material across scientifi
 c domains.\n\nDomain: Chemistry and Materials, Climate, Weather, and Earth
  Sciences, Engineering, Life Sciences, Physics, Computational Methods and 
 Applied Mathematics\n\nSession Chairs: Ewa Deelman (University of Southern
  California) and Michela Taufer (University of Tennessee)
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