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Getting Started with Julia (for Experienced Programmers)
12. Optimisation Tips & Tricks [HPC in Julia]
JuliaCon 2020 | Accurate and Efficiently Vectorized Sums and Dot Products | François Févotte
Understanding memory allocation in Julia
`Memory` in Julia | Jameson Nash, Oscar Smith |
Most important tools for optimizing Julia code: @profview and @code_warntype
Debugging and Optimizing Julia Code
Vectorized Query Evaluation in Julia | Richard Gankema, Alex Hall | JuliaCon2021
Loop Analysis in Julia | Chris Elrod | JuliaCon 2020
Julia Programming Tutorial - Vector Operations (Part 1)
JuliaCon 2020 | Adventures in Avoiding Allocations | Brian Jackson
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Last Updated: October 31, 2026
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In Fall 2020 and Spring 2021, this was MIT's 18.337J/6.338J: Parallel Computing and Scientific Machine Learning course. The ODE solver spit out dt less than dtmin, what do you do? MethodError Dual{...}, what does it mean? Plenty of people ask these ... In this video we make small changes to our N body simulation example to show various easy This talk will present how basic operations on vectors, summation and dot products, can be made more accurate with respect ... This video briefly covers how to write, debug, and This talk was presented as part of JuliaCon2021 Abstract: Modern databases can choose between two approaches to evaluating ... This talk will focus on the library LoopVectorization, providing an overview of how the library represents loops, and how this ... Lessons learned while achieving a 100x speedup of TrajectoryOptimization.jl by eliminating allocations.
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