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Code Profiling and Optimization (in Julia)
JuliaCon 2020 | Accurate and Efficiently Vectorized Sums and Dot Products | François Févotte
Julia Programming Tutorial - Vector Operations (Part 1)
Understanding memory allocation in Julia
Vectorized Query Evaluation in Julia | Richard Gankema, Alex Hall | JuliaCon2021
JuliaCon 2020 | Adventures in Avoiding Allocations | Brian Jackson
Debugging and Optimizing Julia Code
InferOpt.jl: Combinatorial Optimization in ML Pipelines | G Dalle, L Bouvier, L Baty | JuliaCon 2022
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Last Updated: October 16, 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. You might not know all of the latest methods in differential equations, all of the best knobs to tweak, how to properly handle ... This talk will present how basic operations on vectors, summation and dot products, can be made more accurate with respect ... This talk was presented as part of JuliaCon2021 Abstract: Modern databases can choose between two approaches to evaluating ... Lessons learned while achieving a 100x speedup of TrajectoryOptimization.jl by eliminating allocations. This video briefly covers how to write, debug, and We present InferOpt.jl, a generic package for combining combinatorial
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