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UID:submissions.supercomputing.org_SC25_sess533_post143@linklings.com
SUMMARY:Unified Performance Modeling Stack for Distributed GPU Application
 s: Complementing Analytical Insights with Machine Learning
DESCRIPTION:Urvij Saroliya (Technical University of Munich)\n\nModern HPC 
 applications increasingly use GPUs to solve larger problems with higher ac
 curacy and speed. However, committing resources to these large-scale syste
 ms is often costly and time-consuming. Hence, performance modeling enables
  developers to estimate runtime, analyze scalability, and identify resourc
 e bottlenecks in advance. In this work, we propose a unified software ecos
 ystem for end-to-end performance modeling of distributed GPU applications.
  To this end, we propose a combination of analytical and machine learning-
 based modeling methodology, and design a comprehensive software stack to c
 ombine the various components for implementing such an approach. We valida
 te the proposed framework using two real-life applications and provide per
 formance estimations for the GPU kernel and inter-GPU MPI communications.\
 n\nTag: Research & ACM SRC Posters\n\nRegistration Category: Technical Pro
 gram Reg Pass\n\nSession Chairs: Kento Sato (RIKEN Center for Computationa
 l Science (R-CCS)); Chris Schlipalius (Pawsey Supercomputing Research Cent
 re; Commonwealth Scientific and Industrial Research Organisation (CSIRO), 
 Australia); and Anja Gerbes (Georg-August-Universität Göttingen)\n\n
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