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UID:pretalx-euroscipy-2026-WENFS9@pretalx.com
DTSTART;TZID=CET:20260721T160000
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DESCRIPTION:Hi! Have you ever run a NetworkX algorithm on a large graph and
  watched it take… longer than you expected? You look at your machine\, s
 ee all those CPU cores sitting idle\, and wonder — shouldn’t this be f
 aster?\n\nNetworkX is one of the most widely used graph analysis libraries
  in Python. But as the graph sizes become more realistic and huge\, the pe
 rformance becomes a bottleneck. So what if we could make NetworkX faster 
 — without rewriting it in C\, and without giving up its philosophy?\n\nI
 n this talk\, I’ll introduce **nx-parallel**\, a backend that brings mul
 ti-core parallelism to NetworkX algorithms with the help of Joblib. But pa
 rallelism isn’t just a magic switch you turn on. We’ll dig into what a
 ctually makes a graph algorithm embarrassingly parallel\, why only certain
  algorithms qualify\, and how design decisions determine whether paralleli
 sm truly scales.
DTSTAMP:20260603T201104Z
LOCATION:Room 1.19 (Ground Floor\, Shannon)
SUMMARY:Unpacking parallelising NetworkX algorithms in nx-parallel backend 
 - Akshita Sure
URL:https://pretalx.com/euroscipy-2026/talk/WENFS9/
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