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UID:submissions.pasc-conference.org_PASC23_sess144_msa232@linklings.com
SUMMARY:Synthesizing Gradient for Parallel Paradigms Using Enzyme in Julia
DESCRIPTION:Minisymposium\n\nValentin Churavy (Massachusetts Institute of 
 Technology)\n\nAutomatic differentiation (AD), i.e., the augmentation of a
  program to compute derivatives algorithmically, is a powerful tool in sci
 entific computing, with a wide range of applications such as sensitivity a
 nalysis, uncertainty quantification, shape optimization, or machine learni
 ng. Automatic differentiation can be implemented on different layers of th
 e programming stack, common approaches include operator overloading or sou
 rce-to-source transformation. Enzyme is a framework for compiler-aided aut
 omatic differentiation performing automatic differentiation directly on LL
 VM's intermediate representation. AD over parallel programs brings with it
  a host of challenges and in this talk I will discuss how Enzyme can be us
 ed in Julia to synthesize gradients, in particular gradients of codes that
  uses parallel paradigms such as tasks, MPI or GPU programming.\n\nDomain:
  Computer Science, Machine Learning, and Applied Mathematics 
\n\nSession 
 Chair: Samuel Omlin (ETH Zurich / CSCS)
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