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DTSTART;TZID=America/Denver:20231113T140000
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UID:submissions.supercomputing.org_SC23_sess451_misc233@linklings.com
SUMMARY:Programming Model for Habana/Gaudi2 Accelerators and Its Impact on
  Deep Learning Inference/Training Performance at Scale
DESCRIPTION:Workshop\n\nKarthikeyan Vaidyanathan (Intel Corporation)\n\nI 
 will discuss the multi-stream based execution environment of Habana/Gaudi 
 systems that is exposed to deep learning frameworks and I will show how on
 e can combine compute, networking and DMA at high performance and with low
  run-time overheads. I will highlight the performance of Habana Collective
  Communication Library at scale in terms of bandwidth, message rate and de
 monstrate its impact on deep learning training and inference performance o
 f a few neural network models including vision and Large Language Models. 
 In the second part of the talk, I will highlight the challenges in communi
 cation scaling, especially the associated congestion that we observe betwe
 en leaf and spine switches in certain conditions. I will highlight solutio
 ns that we are currently deploying including congestion control algorithms
  and packet/message spraying techniques at the endpoint and share our resu
 lts.\n\nTag: Large Scale Systems, Middleware and System Software, Programm
 ing Frameworks and System Software\n\nRegistration Category: Workshop Reg 
 Pass\n\nSession Chairs: Dhabaleswar K. (DK) Panda (Ohio State University),
  Karl Schulz (Advanced Micro Devices (AMD) Inc), Aamir Shafi (Ohio State U
 niversity), and Hari Subramoni (Ohio State University)
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