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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:Karthikeyan 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 one can combin
 e compute, networking and DMA at high performance and with low run-time ov
 erheads. I will highlight the performance of Habana Collective Communicati
 on Library at scale in terms of bandwidth, message rate and demonstrate it
 s impact on deep learning training and inference performance of a few neur
 al network models including vision and Large Language Models. In the secon
 d part of the talk, I will highlight the challenges in communication scali
 ng, especially the associated congestion that we observe between leaf and 
 spine switches in certain conditions. I will highlight solutions that we a
 re currently deploying including congestion control algorithms and packet/
 message spraying techniques at the endpoint and share our results.\n\nTag:
  Large Scale Systems, Middleware and System Software, Programming Framewor
 ks and System Software\n\nRegistration Category: Workshop Reg Pass\n\nSess
 ion Chairs: Dhabaleswar K. (DK) Panda (The Ohio State University), Karl Sc
 hulz (Advanced Micro Devices (AMD) Inc), Aamir Shafi (The Ohio State Unive
 rsity), and Hari Subramoni (The Ohio State University)\n\n
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