Understanding Optimizing Serial Code In Julia 1 Memory Models Mutation And Vectorization
Let's dive into the details surrounding Optimizing Serial Code In Julia 1 Memory Models Mutation And Vectorization. In Fall 2020 and Spring 2021, this was MIT's 18.337J/6.338J: Parallel Computing and Scientific Machine Learning course.
Key Takeaways about Optimizing Serial Code In Julia 1 Memory Models Mutation And Vectorization
- Lessons learned while achieving a 100x speedup of TrajectoryOptimization.jl by eliminating allocations.
- Presented by Arch Robison at JuliaCon 2014.
- SIMD (Single Instruction, Multiple Data) is a term for when the processor executes the same operation (like addition) on multiple ...
- This talk was presented as part of JuliaCon2021 Abstract: Modern databases can choose between two approaches to evaluating ...
- In this video we make small changes to our N body simulation example to show various easy
Detailed Analysis of Optimizing Serial Code In Julia 1 Memory Models Mutation And Vectorization
In Fall 2020 and Spring 2021, this was MIT's 18.337J/6.338J: Parallel Computing and Scientific Machine Learning course. 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 was part of SciMLCon 2022! For more information, check out https://scimlcon.org/2022/. For more information on the ...
That wraps up our extensive overview of Optimizing Serial Code In Julia 1 Memory Models Mutation And Vectorization.