Introduction to Parallelizing Data Science With Julia Parallelizing Data Science With Julia
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This is the fourth talk in the series introducing the high level, high performance language WestGrid webinar For information on other WestGrid events WestGrid on Ā ... This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course andĀ ... This lecture gives an introduction to multithreading (programming shared memory We present a straightforward approach for distributed The remote memory access routines from MPI-3 are a natural fit for the
Transducers are composable algorithms that operate on collections of inputs. This concept is first introduced in Clojure languageĀ ... This poster was presented at JuliaCon2021. Abstract: Lale.jl is a Are you looking to enhance the performance of your programs and unlock the true potential of
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JuliaCon 2020 | Sponsor Talk: Effortless Parallel Computing on JuliaHub | Julia Computing
Parallelization, Random Numbers and Reproducibility | Phillip Alday | JuliaCon 2020
Parallel Julia webinar
Parallel Computing. Workshop | Julia Lab at MIT | JuliaCon 2016
JuliaCon 2020 | MPI.jl: Julia meets classic HPC | Simon Byrne
Julia Solutions : Basic Concepts of Parallel Computing | packtpub.com
Parallel Computing with Julia, illustrated by numerical linear algebra and Monte Carlo simulation
Distributed Parallelization of xPU Stencil Computations in Julia | S. Omlin, L. RƤss | JuliaCon 2022
Parallel Computing With MPI-3 RMA and Julia | Bart Janssens | JuliaCon 2018
Shared memory parallelism in Julia | Kiran Pamnany | Cambridge Julia Meetup (May 2018)
HiFrames: High Performance Distributed DataFrames in Julia | Ehsan Totoni | JuliaCon 2017
Transducers: Data-oriented Abstraction for Sequential and Parallel Algorithms | JuliaCon 2019
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Last Updated: June 10, 2026
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