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UID:pretalx-pyconde-pydata-2025-HPGEKH@pretalx.com
DTSTART;TZID=CET:20250424T105500
DTEND;TZID=CET:20250424T112500
DESCRIPTION:Many data and simulation scientists use NumPy for its ease of u
 se and good performance on CPU.  This approach works well for single-node 
 tasks\, but scaling to handle larger datasets or more resource-intensive c
 omputations introduces significant challenges. Not to mention\, using GPUs
  requires another level of complexity. We present the cuPyNumeric library\
 , which  gives developers the same familiar NumPy interface\, but seamless
 ly distributes work across CPUs and GPUs.\nIn this talk we showcase the pr
 oductivity and performance of cuPyNumeric library on one of the user's exa
 mples covering some detail on its implementation.
DTSTAMP:20260421T224612Z
LOCATION:Hassium
SUMMARY:Outgrowing your node? Zero stress scaling with cuPyNumeric. - Bo Do
 ng
URL:https://pretalx.com/pyconde-pydata-2025/talk/HPGEKH/
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