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SUMMARY:Array API and library dispatching - Tim Head\, Sebastian Berg
DTSTART;TZID=Europe/Warsaw:20250820T110500
DTEND;TZID=Europe/Warsaw:20250820T115000
DTSTAMP:20260915T034118Z
UID:pretalx-euroscipy-2025-DN7SMP@pretalx.com
DESCRIPTION:There has been much progress in SciPy\, scikit-learn and inter
 esting efforts around Array API as well as some progress in dispatching si
 milar to the NetworkX dispatching.\nThis session is to discuss d future pl
 ans and pain points for libraries to further adopt these patterns.
LOCATION:Room 2.41 (First Floor\, Turing)
URL:https://pretalx.com/euroscipy-2025/talk/DN7SMP/
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SUMMARY:Understanding Dispatching Approaches in the Scientific Python Ecos
 ystem - Sebastian Berg\, Aditi Juneja
DTSTART;TZID=Europe/Warsaw:20250821T133000
DTEND;TZID=Europe/Warsaw:20250821T140000
DTSTAMP:20260915T034118Z
UID:pretalx-euroscipy-2025-9LAVJW@pretalx.com
DESCRIPTION:In recent years\, many specialised libraries have emerged\, im
 plementing optimised subsets of algorithms from larger Scientific Python l
 ibraries-- supporting GPUs for acceleration\, parallel processing\, or dis
 tributed computing\, or written in a lower-level programming language like
  Rust or C. These implementations offer significant performance improvemen
 ts—but integrating them smoothly into existing workflows can be challeng
 ing. This talk explores different dispatching approaches that enable seaml
 ess integration of these faster implementations without breaking APIs or r
 equiring users to switch libraries. We'll focus on the following two appro
 aches:\n\n- **Backend library-based dispatching** : allowing existing libr
 ary function calls to be routed to a faster backend implementation present
  in a separate backend library written for GPUs or in a different language
 \, etc. \, as adopted by projects like NetworkX and scikit-image.\n\n- **A
 rray API standardization and adoption** : more specific to dispatching in 
 array libraries. Based on the type of array that is passed into a numpy fu
 nction\, the call is dispatched to the appropriate array library such as T
 ensorflow\, PyTorch\, Dask\, JAX\, CuPy\, Xarray\, etc. This allows for th
 e array consuming libraries like SciPy and Sklearn to be used in workflows
  that are using these other array libraries.\n\nThen we will go over how t
 hese approaches are different from each other and when to use which approa
 ch based on different use cases and requirements.
LOCATION:Room 1.38 (Ground Floor)
URL:https://pretalx.com/euroscipy-2025/talk/9LAVJW/
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