Keynote. Professor Steven G. Johnson | JuliaCon 2019

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Strong rec eigenspace · 2019-08 · on “JuliaCon 2019 Videos”

If anyone is looking for video recommendations to get started: here’s a couple that I thought were great and should be interesting and approachable to just about anyone. Steven G. Johnson’s keynote address. It’s longer format but really interesting, informative, entertaining and well delivered. The main topic is metaprogramming. [this video] Stefan Karpinski’s talk on the unreasonable effectiveness of multiple dispatch. He discusses the fact that Julia has such dramatic code re-use and sharing between libraries and he discusses some reasons why he thinks multiple dispatch is uniquely able to offer this. [LINK]

Recommends ViralBShah · 2020-10 · on “CSV Reader Benchmarks: Julia Reads CSVs 10-20x Faster than Python and R”

You can write slow programs in all languages. There are many cases where Julia's language features enable higher performance than in comparable C libraries. Have a look at Steve Johnson's keynote from JuliaCon 2019: [this video]

Cites montalbano · 2020-10 · on “CSV Reader Benchmarks: Julia Reads CSVs 10-20x Faster than Python and R”

Agree, but to add, there are many transcendental functions (e.g. I needed to use the Mittag-Leffler function many times in my work) which are near-impossible to implement in Numpy in a way that gives anywhere near usable performance. It's not a common enough function to be pre-implemented in Scipy special functions. In my case, I wrote the algorithm in Numpy/python myself which was almost unusably slow. I then outsourced it to a precompiled fortran program. Finally I just switched to Julia. There was already a library, MittagLeffler.jl which did everything I needed and was written in pure Julia. In the end everything was much faster than the Python/Fortran frankenstein code I had. Edit: For

Cites miguelraz · 2021-06 · on “From Julia to Rust”

What is the focus on numerical computing that you think Julia is missing? The ecosystem seems plenty healthy to me. (Maybe PDEs... but those are so close!) It's not uncommon for good Julia codes to smoke Fortran codebases, like the DiffEq verse or with staged programming, see Steven G. Johnson's keynote at JuliaCon 2019: [this video]

Cites neolog · 2021-06 · on “From Julia to Rust”

Julia is great for numerical computing, but it's not as good as it can be, even in that area. Widening the community's base of expertise will help. I'll just list a few things that come to mind. Many Base functions are currently untyped, which means it's hard to abstract out the key properties that enable reuse, compiler optimizations, and parallelism. See [0] for a language expert's take on numerical computing, which involves defining traits like associativity that enable automatic parallelism by default. A stronger culture of functional programming could make your code faster and easier to understand. It's often (though not always) easier for tooling to optimize pure functions over immutab

Cites mwattsun · 2022-01 · on “Understanding the Power of Lisp (2020)”

When I first started writing asm code, it was accepted to write self-modifying code (programmatically create new code, then jmp to it.) Then I discovered and was told it was a bad idea. So that's how I view Lisp macros - creating code on the fly and executing it. When I was studying this issue, I came across femtolisp, which is a standalone Lisp by the author of Julia, Jeff Bezanson. Julia has metaprogramming so I wanted to look at it. In this video [1] Professor Steve G. Johnson explains metaprogramming (around 10 min mark) and says "most of the time, don't do metaprogramming." He points out in the base Julia there are 36,798 methods, but only 138 macros and 14 generated functions (.04% of

Cites IlyaOrson · 2022-01 · on “Julia Macros for Beginners”

It is not necessarily better in all cases and should not be overused: [this video] However, it is useful to provide a nicer syntax and DSLs. Some examples: [LINK] [LINK] [LINK]