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TZID:Europe/Amsterdam
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SUMMARY:`Memory` in Julia - Jameson Nash\, Oscar Smith
DTSTART;TZID=Europe/Amsterdam:20240710T143000
DTEND;TZID=Europe/Amsterdam:20240710T150000
DTSTAMP:20260817T235320Z
UID:pretalx-juliacon2024-CYJ7V7@pretalx.com
DESCRIPTION:Julia 1.11 rewrites Array from being an opaque type implemente
 d mostly in C to being just another type in Julia. In order to do this\, w
 e introduced a new\, more primitive type for homogenous data storage\, cal
 led Memory. Then we could re-use that work to add an AtomicMemory type tha
 t allow for better expressing multi-threaded memory operations. And along 
 the way\, also added the full set of atomic operators to globals\, known a
 s Binding types. In this talk\, we  we will look at the new Memory type\, 
 t
LOCATION:REPL (2\, main stage)
URL:https://pretalx.com/juliacon2024/talk/CYJ7V7/
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SUMMARY:GPU Acceleration of Julia's SciML: ODEs\, Optimization\, and more 
 - Oscar Smith\, Oscar Smith
DTSTART;TZID=Europe/Amsterdam:20240711T140000
DTEND;TZID=Europe/Amsterdam:20240711T143000
DTSTAMP:20260817T235320Z
UID:pretalx-juliacon2024-EFVEKH@pretalx.com
DESCRIPTION:Julia's SciML ecosystem is burgeoning in applying machine lear
 ning to scientific computing. Moreover\, a key emphasis is the performance
  paradigm\, accelerating scientific discovery. This is possible because at
  the core of the SciML are the numerical methods that automatically suppor
 t hardware accelerators like GPUs\, making the simulations tractable. This
  talk will provide a state of GPU acceleration in the SciML ecosystem and 
 its applications\, ranging from ODE solvers to Optimization methods.
LOCATION:For Loop (3.2)
URL:https://pretalx.com/juliacon2024/talk/EFVEKH/
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