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DTSTART;TZID=America/Denver:20231112T083000
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UID:submissions.supercomputing.org_SC23_sess219_tut132@linklings.com
SUMMARY:Principles and Practice of High Performance Deep/Machine Learning 
 Training and Inference
DESCRIPTION:Dhabaleswar K. (DK) Panda, Hari Subramoni, Aamir Shafi, Nawras
  Alnaasan, Chen Chun Chen, Jinghan Yao, and Kaushik Kandadi Suresh (Ohio S
 tate University)\n\nRecent advances in Machine and Deep Learning (ML/DL) h
 ave led to many exciting challenges and opportunities.  Modern ML/DL frame
 works including TensorFlow, PyTorch, and cuML enable high-performance trai
 ning, inference, and deployment for various types of ML models and Deep Ne
 ural Networks (DNNs).  This tutorial provides an overview of recent trends
  in ML/DL and the role of cutting-edge hardware architectures and intercon
 nects in moving the field forward.  We will also present an overview of di
 fferent DNN architectures, ML/DL frameworks, DL Training and Inference, an
 d Hyperparameter Optimization with special focus on parallelization strate
 gies for model training.  We highlight new challenges and opportunities fo
 r communication runtimes to exploit high-performance CPU/GPU architectures
  to efficiently support large-scale distributed training.  We also highlig
 ht some of our co-design efforts to utilize MPI for large-scale DNN traini
 ng on cutting-edge CPU/GPU/DPU architectures available on modern HPC clust
 ers.  Throughout the tutorial, we include several hands-on exercises to en
 able attendees to gain first-hand experience of running distributed ML/DL 
 training and hyperparameter optimizations on a modern GPU cluster.\n\nTag:
  Artificial Intelligence/Machine Learning\n\nRegistration Category: Tutori
 al Reg Pass\n\n
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