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DTSTART;TZID=America/Chicago:20251117T143000
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UID:submissions.supercomputing.org_SC25_sess218_ws_waccpd105@linklings.com
SUMMARY:Reduction-Aware Directive-Based Programming via Multi-Dimensional 
 Homomorphisms
DESCRIPTION:Richard Schulze, Sergei Gorlatch, and Ari Rasch (University of
  Muenster)\n\nDirective-based programming is a productive way to target pa
 rallel architectures like GPUs and CPUs. Popular solutions such as OpenMP 
 and OpenACC are widely used because they are simple and broadly applicable
  to general-purpose codebases. However, they often fail to deliver consist
 ently high and portable performance, particularly for reduction-intensive 
 computations.\n\nWe introduce a new directive design based on Multi-Dimens
 ional Homomorphisms (MDH). Unlike existing methods, this MDH-based directi
 ve focuses on data-parallel computations, such as tensor expressions, achi
 eving superior and portable performance even for reduction-heavy workloads
  found in deep learning, data mining, and quantum chemistry. It also maint
 ains or improves programmer productivity by using Python as the host langu
 age.\n\nExperiments show that our approach outperforms current directive-b
 ased solutions across various workloads, including linear algebra, stencil
  computations, data mining, quantum chemistry, and deep learning.\n\nRecor
 ding: Livestreamed, Recorded\n\nRegistration Category: Technical Program R
 eg Pass, Workshop Reg Pass\n\nSession Chairs: Andreas Herten (Forschungsze
 ntrum Jülich, Jülich Supercomputing Centre (JSC)); Rabab Alomairy (Massach
 usetts Institute of Technology (MIT), King Abdullah University of Science 
 and Technology (KAUST)); and Jorge Luis Galvez Vallejo (Australian Nationa
 l University)\n\n
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