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UID:pretalx-euroscipy-2024-QLVBYY@pretalx.com
DTSTART;TZID=CET:20240828T110500
DTEND;TZID=CET:20240828T113500
DESCRIPTION:Hi! Have you ever wished your pure Python libraries were faster
 ? Or wanted to fundamentally improve a Python library by rewriting everyth
 ing in a faster language like C or Rust? Well\, wish no more... NetworkX's
  backend dispatching mechanism redirects your plain old NetworkX function 
 calls to a FASTER implementation present in a separate backend package by 
 leveraging the Python's [`entry_point`](https://packaging.python.org/en/la
 test/specifications/entry-points) specification!\n\nNetworkX is a popular\
 , pure Python library used for graph(aka network) analysis. But when the g
 raph size increases (like a network of everyone in the world)\, then Netwo
 rkX algorithms could take days to solve a simple graph analysis problem. S
 o\, to address these performance issues this backend dispatching mechanism
  was recently developed. In this talk\, we will unveil this dispatching me
 chanism and its implementation details\, and how we can use it just by spe
 cifying a `backend` kwarg like this:\n\n    >>> nx.betweenness_centrality(
 G\, backend=“parallel”)\n\nor by passing the backend graph object(type
 -based dispatching):\n\n    >>> H = nxp.ParallelGraph(G)\n    >>> nx.betwe
 enness_centrality(H)\n\nWe'll also go over the limitations of this dispatc
 h mechanism. Then we’ll use the example of nx-parallel as a guide to bui
 lding our own custom NetworkX backend. And then\, using NetworkX's existin
 g test suite\, we'll test this backend that we build. Ending with a quick 
 dive into the details of the nx-parallel backend.
DTSTAMP:20260520T013146Z
LOCATION:Room 6
SUMMARY:Understanding NetworkX's API Dispatching with a parallel backend - 
 Erik Welch\, Aditi Juneja
URL:https://pretalx.com/euroscipy-2024/talk/QLVBYY/
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UID:pretalx-euroscipy-2024-8MXPRW@pretalx.com
DTSTART;TZID=CET:20240829T132000
DTEND;TZID=CET:20240829T150000
DESCRIPTION:Scientific python libraries struggle with the existence of seve
 ral array and dataframe providers.  Many important libraries currently mai
 nly support NumPy arrays or pandas dataframes.\nHowever\, as library autho
 rs we wish to allow users to smoothly use other array provides and simplif
 y for example the use of GPUs without the need for explicit use of cuda en
 abled libraries.\n\nThis session will be split into three related discussi
 ons around efforts to tackle this situation:\n* Dispatching and backend se
 lection discussion\n* Array API adoption progress and discussion\n* Datafr
 ame compatibility layer discussion
DTSTAMP:20260520T013146Z
LOCATION:Room 5
SUMMARY:Dispatching\, Backend Selection\, and Compatibility APIs - Guillaum
 e Lemaitre\, Joris Van den Bossche\, Tim Head\, Erik Welch\, Marco Gorelli
 \, Sebastian Berg\, Aditi Juneja\, Stéfan van der Walt
URL:https://pretalx.com/euroscipy-2024/talk/8MXPRW/
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