21–30 of 216 posts
Does anyone know what the units are for TTL and TTFX? I can’t tell how significant those results are. Label your graphs, people!
I guess it is milliseconds based on the names and the results later in the article. - time-to-first-execution (TTFX) - time-to-load (TTL)
[flagged]
For me, the issue is that there's too much magic happening, e.g. with macros, but finding or solving the issues seems needlessly convoluted. Haskell, Rust, Python, or even C++ feel a lot less magic, and more reasonable despite all being quite different.
I used to have a reasonably simple notebook for a paper which took about 35 minutes to compile on an old university-provided CPU; even when opening it for the second time. Therefore, I‘m really excited for the improvements in code caching! Thanks to Tim Holy, Jameson Nash, Valentin Churavy, and others for your work
> Reasonably simple > 35 minutes to compile What kind of CPU are we talking about here!?
I have similar compilation times with Rust on an old Asus 1215B, where 8GB and SSD hardly help the compile the world from scratch cargo model, when starting a new project.
Matlab users should switch to Julia. It’s a real programming language, and better in many ways. I provide the option of Julia in my tutorials. Students are lazy, and don’t want to explore something new. Most of them stick with matlab. What prevents matlab users from switching? The syntax is similar.
I'm very comfortable in Matlab and often know immediately what's wrong when I hit those oddities. In Python it usually means I spend considerable time googling and tinkering before I even understand what I did wrong because I have far less experience... Same when I tinker with Julia.
And for some people it being a Real Programming Language may be a disadvantage actually... Typically means you need a better understanding than just type-and-run.
[flagged]
Earlier quoted context omitted.
> Reasonably simple > 35 minutes to compile What kind of CPU are we talking about here!?
Most likely a single or dual core CPU. I have similar compilation times with Rust on an old Asus 1215B, where 8GB and SSD hardly help the compile the world from scratch cargo model, when starting a new project.
[flagged]
Given the premices on which the Python language was designed (giving access to programming to non-programmers), your stance is surprising. To me, it looks like, to the contrary, that Python is so full of counter-intuitive bits... like doing a［begin:end+1］ to take a slice.
To all Julia users: Go forth, and make use of PrecompileTools.jl in your packages! The latency only drops if you actually make use of precompilation, and it's pretty easy to use. I can't wait for more of the ecosystem to start making use of it.
does it mean I still have to invoke special workflows and commands to get compilation benefits or does it work out of the box for normal julia invocations?
"Together with PrecompileTools.jl, Julia 1.9 delivers many of the benefits of PackageCompiler without the need for user-customization." does it mean I still have to invoke special workflows and commands to get compilation benefits or does it work out of the box for normal julia invocations?
The tradeoffs are somewhat larger load times (TTL), increased precompilation time (because some of the compilation moves to precompile time), and increased disk usage by the package.