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DTSTAMP:20230831T095745Z
LOCATION:Flüela
DTSTART;TZID=Europe/Stockholm:20230626T170000
DTEND;TZID=Europe/Stockholm:20230626T173000
UID:submissions.pasc-conference.org_PASC23_sess142_msa149@linklings.com
SUMMARY:Generating Optimal HPC Code with Machine Learning
DESCRIPTION:Minisymposium\n\nEmil Vatai (RIKEN)\n\nFollowing the deep lear
 ning revolution started approximately a decade ago, experts in both fields
  of compilers and machine learning have approached the problem of generati
 ng code using ML methods. Results include, e.g., ML models generating (oft
 en suboptimal and sometimes incorrect) code or non-negligible performance 
 improvements by applying ML at various stages of compilation. With the end
  of Dennard scaling and continuing with the denouement of Moore’s law, kee
 ping the performance growth of HPC systems is becoming increasingly diffic
 ult and a more holistic and general mechanism is required to leverage both
  the robustness and accuracy of compilers with the ability of ML models to
  handle a wider context then separate stages of compilation. Buried behind
  mathematical abstraction, the polyhedral model is potentially the right t
 ool for this job and ripe for combining it with ML. This is especially tru
 e when considering HPC codes, which often consists of regular loops which 
 in the end amount to programs with complex data and control flow. We prese
 nt our efforts both in tackling the mathematics and tooling behind polyhed
 ral compilation as well as our strides to combine it with ML, with the goa
 l to unlock the potential of optimal code generation via ML/AI.\n\nDomain:
  Computer Science, Machine Learning, and Applied Mathematics 
\n\nSession 
 Chair: Anshu Dubey (Argonne National Laboratory, University of Chicago)
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