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UID:submissions.supercomputing.org_SC23_sess291_rpost181@linklings.com
SUMMARY:Two-Phase IO Enabling Large-Scale Performance Introspection
DESCRIPTION:Ke Fan and Sidharth Kumar (University of Illinois, Chicago)\n\
 nNumerous sophisticated profiling and visualization tools have been develo
 ped to enable programmers to expose semantic information from their applic
 ation components.  However, effective and interactive exploration of the p
 rofiles of large-scale parallel programs remains a challenge due to the hi
 gh I/O overheads of profiles and the difficulties in scaling downstream vi
 sualization tools. In this poster, we present a full-stack approach to a p
 erformance introspection framework that tackles key challenges in profilin
 g and visualizing performance data at scale. Our novelty lies in a scalabl
 e and compact data model and a two-phase I/O system, which instill scalabi
 lity into the profiler making it low overhead-- even at high process count
 s (< 5%). We then build a web-based, visual-analytic dashboard with linked
  views. Our profiling and visualization tools are both lightweight and eas
 y-to-use, which strikes a balance between providing sophisticated features
  and operating quickly and efficiently at high process counts.\n\nTag: Art
 ificial Intelligence/Machine Learning, Architecture and Networks, Heteroge
 neous Computing, I/O and File Systems, Performance Measurement, Modeling, 
 and Tools, Post-Moore Computing, Programming Frameworks and System Softwar
 e, Quantum Computing\n\nRegistration Category: Tech Program Reg Pass, Exhi
 bits Reg Pass\n\n
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