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DTSTART;TZID=America/Denver:20231115T160000
DTEND;TZID=America/Denver:20231115T163000
UID:submissions.supercomputing.org_SC23_sess174_pap322@linklings.com
SUMMARY:Application Performance Modeling via Tensor Completion
DESCRIPTION:Edward Hutter and Edgar Solomonik (University of Illinois)\n\n
 Performance tuning, software/hardware co-design, and job scheduling are am
 ong the many tasks that rely on models to predict application performance.
  We propose and evaluate low-rank tensor decomposition for modeling applic
 ation performance. We discretize the input and configuration domains of an
  application using regular grids. Application execution times mapped withi
 n grid-cells are averaged and represented by tensor elements. We show that
  low-rank canonical-polyadic (CP) tensor decomposition is effective in app
 roximating these tensors. We further show that this decomposition enables 
 accurate extrapolation of unobserved regions of an application's parameter
  space. We then employ tensor completion to optimize a CP decomposition gi
 ven a sparse set of observed execution times. We consider alternative piec
 ewise/grid-based models and supervised learning models for six application
 s and demonstrate that CP decomposition optimized using tensor completion 
 offers higher prediction accuracy and memory-efficiency for high-dimension
 al performance modeling.\n\nTag: Artificial Intelligence/Machine Learning,
  Compilers, Performance Measurement, Modeling, and Tools, Performance Opti
 mization, Programming Frameworks and System Software, Tensors\n\nRegistrat
 ion Category: Tech Program Reg Pass\n\nReproducibility Badges: Artifact Av
 ailable, Artifact Functional, Results Reproduced\n\nSession Chair: Kazem C
 heshmi (McMaster University, Ontario, Canada)\n\n
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