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DTSTAMP:20230831T095745Z
LOCATION:Flüela
DTSTART;TZID=Europe/Stockholm:20230626T163000
DTEND;TZID=Europe/Stockholm:20230626T170000
UID:submissions.pasc-conference.org_PASC23_sess142_msa145@linklings.com
SUMMARY:Data-Centric Python: Bridging Productivity and Performance via Dat
 a Movement Minimization
DESCRIPTION:Minisymposium\n\nTal Ben-Nun (Lawrence Livermore National Labo
 ratory)\n\nComputational scientists are migrating towards high-productivit
 y languages for rapid prototyping and reproducible experiment sharing. Spe
 cifically, Python is becoming the language of choice for several fields, p
 artly driven by the attention from the Machine Learning community. However
 , productivity often clashes with performance, as the (usually ML-specific
 ) frameworks are not geared towards the needs of large-scale scientific co
 mputing. In this talk, we will characterize the performance/productivity g
 ap in Python and discuss how to address it, all while retaining the high-l
 evel semantics of the language and leveraging embedded DSLs such as NumPy.
  The talk will review the barriers that inhibit optimizing Python code, de
 fine a subset thereof that enables its compilation, and discuss how to dea
 l with the remainder of the code. We will then show how the compilable sub
 set, called Data-Centric (DaCe) Python, can be subject to both local and g
 lobal optimization via data movement minimization. As a case study, we wil
 l review the FV3 climate model and a recent porting of its full dynamical 
 core to GPUs using a combination of the GT4Py DSL and DaCe Python.\n\nDoma
 in: Computer Science, Machine Learning, and Applied Mathematics 
\n\nSessi
 on Chair: Anshu Dubey (Argonne National Laboratory, University of Chicago)
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