4 ms·

Come on, it is silly to compare a full general purpose language against a special-purpose tool. Yes, grep is also better than julia at searching for a string in

Come on, it is silly to compare a full general purpose language against a special-purpose tool. Yes, grep is also better than julia at searching for a string in a file. Julia is a terrible replacement for gnuplot and gnuplot is a terrible replacement for julia.

enriquto 5y ago
Sure! That's why I set a somewhat reasonable target at being just 5x or 10x times slower than a specific-purpose plotting tool. But even that is considered to be chimerical! (or, in your words, "ridiculous")
- krastanov 5y ago
  Now you are just putting words in my mouth. I would completely agree that 10x latency to first plot is a reasonable target (i.e. first plot in about a second, like you get in python and much faster than what you get in matlab/mathematica). And plenty of devs closer to core of Julia and the plotting libraries in it would agree. And to be clear, I do expect my second plot to be ready in tens of milliseconds.
reikonomusha 5y ago
Why is it such a ridiculous comparison? Gnuplot is still interpreting a language, loading plotting code, etc. If Julia folks wanted to, they could bundle pre-compiled plotting code that loads as fast as memory moves bytes. They don’t want to, of course, likely because it’s inelegant, but they could, and a general-purpose language doesn’t stop them from doing that.
- krastanov 5y ago
  You can already bundle pre-compiled plotting code in your Julia sys-image if you want. But Julia is not a plotting tool so it would be ridiculous to optimize it just for plotting. I want ODE solvers to have less latency, should I start expecting gnuplot to have built-in ODE solvers or the official installer of Julia to have the ODE libraries pre-compiled? Maybe this example would make it clearer: why does your argument not apply to Python? Should we expect python libraries to come pre-cached so that the first time I load `sympy` I do not need to wait for tens of seconds to have .pyc files created. Or about matlab? Again, I am all on board with the idea that julia needs lower latency and if you look at what their devs say, they also agree with that. But expecting Julia to be super low-latency (lower-latency than python/c/matlab) for your pet task is silly.
  - reikonomusha 5y ago
    I gave a proof-of-concept argument as to why something doesn’t need to take as long straight out of the box with no customization. Python is doing it sub-1s. You can also include a non-optimizing interpreter. My point is that being a general purpose language doesn’t inherently limit you in any way; instead it’s one’s choice of implementation strategy. Another strategy: when a user installs Julia, they select “fast-loading” libraries. You’d be surprise how small changes in UI/UX make huge perceived differences in quality and performance. I bet “Julia can already do this” too, but nobody does it because it’s not idiomatic and it’s not recommended up front. At the end of the day, people don’t complain about Python or MATLAB as much because they feel nicer. If it feels nicer because of some other reason than absolute time, then they’re doing something about UX that Julia is not, because everybody really does feel Julia is extremely sluggish to use.
    - ChrisRackauckas 5y ago
      >Another strategy: when a user installs Julia, they select “fast-loading” libraries. You’d be surprise how small changes in UI/UX make huge perceived differences in quality and performance. I bet “Julia can already do this” too, but nobody does it because it’s not idiomatic and it’s not recommended up front. Don't most people already use a bit of PackageCompiler.jl in their workflows? Maybe it just need to be mentioned more in introduction videos, but it's literally one line of code to do this and I don't see how that isn't the solution already. Most users I know have been doing it for a few years now. For reference, with Plots and DifferentialEquations the command is: using PackageCompiler; create_sysimage([:Plots,:DifferentialEquations]) It's simple enough to add to every tutorial. I think the issue is that intro videos just need to be updated.
      - qart 5y ago
        I'm new to Julia. I had heard of PackageCompiler, but assumed that was only for creating redistributable binaries for others. I also assumed `]precompile` was the best I could do to speed up libraries. My suggestions: 1- please forget about the intro videos. Those are impossible to keep up-to-date, and are not a good reference resource. Sure, a future video should explain PackageCompiler, but that is not sufficient. 2- please update docs.julialang.org with all such guidelines. This is the likely the first place people look, so if there are important guidelines that everyone could benefit from, this is where they belong.
ku-man 5y ago
"... it is silly to compare a full general purpose language against a special-purpose tool..." Wait, wait... isn't Julia a general purpose language? At least is what the fanboys keep repeating ad-nauseaum

enriquto 5y ago

Sure! That's why I set a somewhat reasonable target at being just 5x or 10x times slower than a specific-purpose plotting tool. But even that is considered to be chimerical! (or, in your words, "ridiculous")

krastanov 5y ago
Now you are just putting words in my mouth. I would completely agree that 10x latency to first plot is a reasonable target (i.e. first plot in about a second, like you get in python and much faster than what you get in matlab/mathematica). And plenty of devs closer to core of Julia and the plotting libraries in it would agree. And to be clear, I do expect my second plot to be ready in tens of milliseconds.

reikonomusha 5y ago

Why is it such a ridiculous comparison? Gnuplot is still interpreting a language, loading plotting code, etc. If Julia folks wanted to, they could bundle pre-compiled plotting code that loads as fast as memory moves bytes. They don’t want to, of course, likely because it’s inelegant, but they could, and a general-purpose language doesn’t stop them from doing that.

krastanov 5y ago
You can already bundle pre-compiled plotting code in your Julia sys-image if you want. But Julia is not a plotting tool so it would be ridiculous to optimize it just for plotting. I want ODE solvers to have less latency, should I start expecting gnuplot to have built-in ODE solvers or the official installer of Julia to have the ODE libraries pre-compiled? Maybe this example would make it clearer: why does your argument not apply to Python? Should we expect python libraries to come pre-cached so that the first time I load `sympy` I do not need to wait for tens of seconds to have .pyc files created. Or about matlab? Again, I am all on board with the idea that julia needs lower latency and if you look at what their devs say, they also agree with that. But expecting Julia to be super low-latency (lower-latency than python/c/matlab) for your pet task is silly.
- reikonomusha 5y ago
  I gave a proof-of-concept argument as to why something doesn’t need to take as long straight out of the box with no customization. Python is doing it sub-1s. You can also include a non-optimizing interpreter. My point is that being a general purpose language doesn’t inherently limit you in any way; instead it’s one’s choice of implementation strategy. Another strategy: when a user installs Julia, they select “fast-loading” libraries. You’d be surprise how small changes in UI/UX make huge perceived differences in quality and performance. I bet “Julia can already do this” too, but nobody does it because it’s not idiomatic and it’s not recommended up front. At the end of the day, people don’t complain about Python or MATLAB as much because they feel nicer. If it feels nicer because of some other reason than absolute time, then they’re doing something about UX that Julia is not, because everybody really does feel Julia is extremely sluggish to use.
  - ChrisRackauckas 5y ago
    >Another strategy: when a user installs Julia, they select “fast-loading” libraries. You’d be surprise how small changes in UI/UX make huge perceived differences in quality and performance. I bet “Julia can already do this” too, but nobody does it because it’s not idiomatic and it’s not recommended up front. Don't most people already use a bit of PackageCompiler.jl in their workflows? Maybe it just need to be mentioned more in introduction videos, but it's literally one line of code to do this and I don't see how that isn't the solution already. Most users I know have been doing it for a few years now. For reference, with Plots and DifferentialEquations the command is: using PackageCompiler; create_sysimage([:Plots,:DifferentialEquations]) It's simple enough to add to every tutorial. I think the issue is that intro videos just need to be updated.
    - qart 5y ago
      I'm new to Julia. I had heard of PackageCompiler, but assumed that was only for creating redistributable binaries for others. I also assumed `]precompile` was the best I could do to speed up libraries. My suggestions: 1- please forget about the intro videos. Those are impossible to keep up-to-date, and are not a good reference resource. Sure, a future video should explain PackageCompiler, but that is not sufficient. 2- please update docs.julialang.org with all such guidelines. This is the likely the first place people look, so if there are important guidelines that everyone could benefit from, this is where they belong.

ku-man 5y ago

"... it is silly to compare a full general purpose language against a special-purpose tool..." Wait, wait... isn't Julia a general purpose language? At least is what the fanboys keep repeating ad-nauseaum