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DTSTART;TZID=America/Chicago:20251117T083000
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UID:submissions.supercomputing.org_SC25_sess255_tut121@linklings.com
SUMMARY:Accelerating and Scaling Python for HPC
DESCRIPTION:Bryce Adelstein Lelbach (NVIDIA Corporation), Daniel Margala (
 Lawrence Berkeley National Laboratory (LBNL)), Stan Seibert (Anaconda), Ma
 tthew Cawood (Texas Advanced Computing Center (TACC)), Sam Foreman (Argonn
 e National Laboratory (ANL)), and Andy Terrel (NVIDIA Corporation)\n\nPyth
 on is powering breakthrough exascale scientific discoveries—come learn how
  to program the world's largest supercomputers with it! In this interactiv
 e tutorial you’ll learn how to write, debug, profile, and optimize high-pe
 rformance, multi-node GPU applications in Python. You'll learn and master:
  CuPy for drop-in GPU acceleration of NumPy workflows; Numba for writing c
 ustom kernels that match the performance of C++ and Fortran; and mpi4py fo
 r scaling across thousands of nodes. Along the way we’ll learn how to prof
 ile our code, debug tricky kernels, and leverage foundational and domain-s
 pecific accelerated libraries. Everything is hands-on: short and interacti
 ve lectures by expert instructors will be paired with guided Jupyter Noteb
 ooks that introduce each concept and have you immediately apply it in bite
 -sized exercises (2D heat equation, SVD image compression, Mandelbrot, etc
 .). The labs culminate in porting the miniWeather mini-app from serial Pyt
 hon to a hybrid MPI + GPU implementation. We'll use a web-based environmen
 t that requires no installs or setup—just a laptop and a browser. Whether 
 you’re a domain scientist seeking faster turnaround or a software engineer
  evaluating portable acceleration strategies, you’ll leave with a roadmap,
  skills, and code for bringing scalable Python practices back to your own 
 HPC facility.\n\nRecording: Livestreamed, Recorded\n\nRegistration Categor
 y: Tutorial Reg Pass\n\n
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