BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//pretalx//pretalx.com//euroscipy-2026//speaker//8WLXFT
BEGIN:VTIMEZONE
TZID:Poland
BEGIN:DAYLIGHT
DTSTART:20250721T000000
TZNAME:CEST
TZOFFSETFROM:+0200
TZOFFSETTO:+0200
END:DAYLIGHT
BEGIN:STANDARD
DTSTART:20251026T030000
RDATE:20261025T030000
TZNAME:CET
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20260329T030000
RDATE:20270328T030000
TZNAME:CEST
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
SUMMARY:Unpacking parallelising NetworkX algorithms in nx-parallel backend
  - Akshita Sure
DTSTART;TZID=Poland:20260721T160000
DTEND;TZID=Poland:20260721T162000
DTSTAMP:20260911T050502Z
UID:pretalx-euroscipy-2026-WENFS9@pretalx.com
DESCRIPTION:Hi! Have you ever run a NetworkX algorithm on a large graph an
 d watched it take… longer than you expected? You look at your machine\, 
 see all those CPU cores sitting idle\, and wonder — shouldn’t this be 
 faster?\n\nNetworkX is one of the most widely used graph analysis librarie
 s in Python. But as the graph sizes become more realistic and huge\, the p
 erformance 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.
LOCATION:Room 1.19 (Ground Floor\, Shannon)
URL:https://pretalx.com/euroscipy-2026/talk/WENFS9/
END:VEVENT
END:VCALENDAR