3 ms·

Legitimate and honest question: in which circumstances would you choose Julia over more mainstream alternative like Go?

by pachico 7mo ago

bandrami 7mo ago
Math. Places you might use Wolfram or Sage.
Human-Cabbage 7mo ago
Julia is aimed at scientific computing. It competes against Python with numpy/scipy, R, etc.
- ziotom78 7mo ago
  Correct, but I would add: Julia is better than Python+NumPy/SciPy when you need extreme speed in custom logic that can’t be easily vectorized. As Julia is JIT-compiled, if your code calls most of the functions just once it won’t provide a big advantage, as the time spent compiling functions can be significant (e.g., if you use some library heavily based on macros). To produce plots out of data files, Python and R are probably the best solutions.
  - jey 7mo ago
    And I would further add: In addition to performance, Julia's language and semantics are much more ergonomic and natural for mathematical and algorithmic code. Even linear algebra in Python is syntactically painful. (Yes, they added the "@" operator for matmul, but this is still true).
  - dgfl 7mo ago
    Disagree on the last statement. Makie is tremendously superior to matplotlib. I love ggplot but it is slow, as all of R is. And my work isn’t so heavy on statistics anyway. Makie has the best API I’ve seen (mostly matlab / matplotlib inspired), the easiest layout engine, the best system for live interactive plots (Observables are amazing), and the best performance for large data and exploration. It’s just a phenomenal visualization library for anything I do. I suggest everyone to give it a try. Matlab is the only one that comes close, but it has its own pros and cons. I could write about the topic in detail, as I’ve spent a lot of time trying almost everything that exists across the major languages.
    - dan-robertson 7mo ago
      I tried some Julia plotting libraries a few years ago and they had apis that were bad for interactively creating plots as well as often being buggy. I don’t have performance problems with ggplot so that’s what I tend to lean to. Matplotlib being bad isn’t much of a problem anymore as LLMs can translate from ggplot to matplotlib for you.
    - Certhas 7mo ago
      I love Makie but for investigating our datasets Python is overall superior (I am not familiar enough with R), despite Julia having the superior Array Syntax and Makie having the better API. This is simply because of the brilliant library support available in scikit learn and the whole compilation overhead/TTFX issue. For these workflows it's a huge issue that restarting your interactive session takes minutes instead of seconds.
    - versteegen 7mo ago
      I recently used Makie to create an interactive tool for inspecting nodes of a search graph (dragging, hiding, expanding edges, custom graph layout), with floating windows of data and buttons. Yes, it's great for interactive plots (you can keep using the REPL to manipulate the plot, no freezing), yes Observables and GridLayout are great, and I was very impressed with Makie's plotting abilities from making the basics easy to the extremely advanced, but no, it was the wrong tool. Makie doesn't really do floating windows (subplots), and I had to jump through hoops to create my own float system which uses GridLayout for the GUI widgets inside them. I did get it to all work nearly flawlessly in the end, but I should probably have used a Julia imGUI wrapper instead: near instant start time!
      - leephillips 7mo ago
        Are you referring to https://github.com/JuliaImGui/CImGui.jl https://github.com/JuliaImGui/CImGui.jl ?
        versteegen 7mo ago
        Yes. And I did port my GUI layer to CimGui.jl. The rest of it is pretty intertwined with Makie, didn't do that yet. The Makie version does look better than ImGui though.
  - ainch 7mo ago
    Even then, if you're familiar with NumPy it's pretty easy to switch to Jax's NumPy API, and then you can easily jit in Python as well.
    - JanisErdmanis 7mo ago
      As long as someone else does the porting and maintains the compatability between both subecosystems of thoose who prefer using Jax and thoose who prefer depending on the NumPy. Also not having zero overhead structs that one can in an array handicaps types of performance codes one can write.
markkitti 7mo ago
When I need to do serious math, I use Julia.
setopt 7mo ago
Scientific computing. AFAIK, library support for that in Go is almost nonexistent.
kryptiskt 7mo ago
Go is a total non-starter, it's not interactive at all. The competitors are things like Matlab, Mathematica, R or Python (with the right math libs). If you're weird you could use something like Haskell, APL or Lisp in this role, but you'd pay a hefty price in available libs.
- VorpalWay 7mo ago
  In what situations would a non-interactive language be a non-starter? I have never felt that I missed having a REPL when coding C++ or Rust. The only reason it is even useful in python is that the type info is laughably bad, so you need to poke things interactivly to figure out what shape of data you should even expect. (I'll take strong static typing every day, it is so much better.)
  - SatvikBeri 7mo ago
    REPLs/notebooks are really nice in situations where you don't know what you want ahead of time and are throwing away 90% of the code you write, such as trying to find better numerical algorithms to accomplish some goal, exploring poorly documented APIs (most of them), making a lot of plots for investigating, or working a bunch with matrices and Dataframes (where current static typing systems don't really offer much.)
    - VorpalWay 7mo ago
      Yeah, this is a entirely different domain than what I work in (hard real-time embedded and hard real-time Linux). Though poorly documented APIs exist everywhere, but they are not something you can rely on anyway: if it isn't documented the behaviour can change without it being a breaking change. It would be irresponsible to (intentionally) depend on undocumented behaviour. Rather you should seek to get whatever it is documented. Otherwise there is a big risk that your code will break some years down the line when someone tries to upgrade a dependency. Most software I deal with is long-lived. There is code I wrote 15 years ago that is still in production and where the code base is still evolving, and I see no reason why that wouldn't be true in another 15 years as well. At least you should write tests to cover any dependencies on undocumented behaviour. (You do have comprehensive tests right?)
wolvesechoes 7mo ago
It is highly interactive and dynamic, yet performant. And it is not only about scientific computing, for almost any application can take advantage of interactive, modifiable system, where you can explore your state at any point. In others, more static langs good debuggers help with this to lesser or larger extend, but it is not the same from my experience. So better question is: in which circumstances would you choose Julia over more mainstream-y alternative like Clojure? And here scientific and numerical angle comes to play. At the same time I think Julia is failed attempt, with unsolvable problems, but it is a different topic.
Certhas 7mo ago
On top of what others have said: In many situations the alternative to Julia isn't Go but C++ (or maybe Rust though its library support is lacking). E.g. if you are writing high-ish* performance algorithmic code that should also run on GPU. Julia also heavily prioritizes composability of libraries. And though that can be a source of bugs at times, there are things we are doing in Julia with one or two PhD students that would be practically impossible (as in require an order of magnitude more implementation work) in any other language.
huijzer 7mo ago
To replace uses where you would use Matlab or R probably. I prefer Julia over Matlab or R. So data science. For production code however, it's not great since it has no static typing. Imagine having your production code crash mid-execution with the error "Function foo not found". Only dynamic languages can do that to you.
- jondea 7mo ago
  I broadly agree that it can be hard to nail down Julia's behaviour but it does have static typing and I think it is more subtle. Function arguments and variables can be concrete types e.g. if you were implementing an approximation for sin, you could restrict arguments to Float32 if you knew it was only suitably accurate for that type.
  - huijzer 7mo ago
    > it does have static typing and I think it is more subtle. Yes sure Julia isn't fully dynamically typed, but that doesn't change the fact that it isn't fully static typed. If it was, it should be pretty easy to create static binaries and find bugs like "func not defined" at compilation time.
Joel_Mckay 7mo ago
Julia collapses entire programming paradigms into single character syntax, and often will transparently handle clean parallelism or cluster instance batching. https://docs.julialang.org/en/v1/manual/mathematical-operations/#man-dot-operators https://docs.julialang.org/en/v1/manual/mathematical-operati... Go is similar in many ways, but takes a performance hit in areas like Garbage collection. The Julia community is great, and performance projects may also be compiled into a binary image. =3
- ForceBru 7mo ago
  Judging by Julia's Discourse, compiling actual production Julia code into a standalone binary is highly nontrivial and ordinary users don't really know how and why to do this.
  - Joel_Mckay 7mo ago
    Example should still work: https://julialang.github.io/PackageCompiler.jl/dev/devdocs/binaries_part_2.html https://julialang.github.io/PackageCompiler.jl/dev/devdocs/b... They greatly improved the generated binary file size last year. ymmv with "--trim=unsafe-warn" =3
andriamanitra 7mo ago
If you need to deal with matrices Julia's built-in support for that kind of stuff is the best out of any language I've ever seen (and I've tried dozens of different languages). It's like having first-class numpy arrays without installing any third party packages, and the syntax is even more convenient than Python. The standard library is reasonably comprehensive (not quite as big as in Python or Ruby or Go, but it's usually more well-designed). It is also an excellent language for messing about because the language and especially the REPL have tons of quality-of-life features. I often use it when I want to do something interactively (eg. inspect a data set, draw a graph, or figure out what's going on with an Unicode string, or debug some bitwise trickery). What Julia is not great at is things where you need minimal overhead. It is performant for serious number crunching like simulations or machine learning tasks, but the runtime is quite heavy for simple scripting and command-line tools (where the JIT doesn't really get a chance to kick in).
qalmakka 7mo ago
My two cents: while Julia is arguably more complex than Go, it's type system and especially its data types (N-dimensional arrays, ...) make it way more suited anything that needs to process complex data, or do anything that's even closely related to geometry. `.|>` is wonderful and makes functional-like code easier, and that's just the tip of the iceberg, macros are also beautiful and absurdly useful. Also LLVM more often than not generates faster code than the Go backend, albeit the slowdown at startup in Julia programs is often a dealbreaker for small scripts and iteration in my experience Also, the REPL. Julia's REPL is vastly better than any other language REPL, by far. Python's is good but Julia is way better, even as a calculator for instance, it has fractions and it is more suited for maths
- dandanua 7mo ago
  Yeah, Julia's REPL deserves special attention as it allows to do the package management (by pressing "]"), look for the functions help ("?") and do shell operations (";") without leaving it.
xeonmc 7mo ago
Its syntax is great for math and has a comprehensive ecosystem of scientific computing libraries stewarded by that timelord guy from Men in Black 3.
dandanua 7mo ago
Julia is not an alternative to Go. It is the alternative to Python (slow) and to C++ (hard and complex). Go is fast and simple but doesn't have abstractions to create complex code required by math libraries.
a-french-anon 7mo ago
You want real macros and a type system actually designed instead of "grown" on top of C/Aleph.
SatvikBeri 7mo ago
We use Julia at our quant fund. We looked into it and several other alternatives 5 years ago as a more performant replacement for numpy/scipy/etc., and Go was one of the alternatives we considered, along with things like numba and writing C extensions. Julia won for a very simple reason: we tried porting one of our major pipelines in several languages, and the Julia version was both the fastest to port and the most performant afterwards. Plus, Julia code is very easy to read/write for researchers who don't necessarily have a SWE background, while Go or C++ are not. We started using Julia in the Research Infrastructure team, but other teams ended up adopting it voluntarily because they saw the performance gains.
sinnsro 7mo ago
While others have mentioned plenty of reasons, for this particular case I want to highlight 3 things: 1. Julia has great tooling for operations research/linear programming. JuMP provides an standardise interface to interact with solvers (e.g., Gurobi, CPLEX) via wrapper libraries. 2. I like its overall ergonomics. It is fast enough that a programmer might not need to use a compiled language for performance. The type system allows for multiple dispatch. And the syntax is more approachable than say Python for matrix algebra. 3. I would say the performance is overstated by the community but out of the box it is good enough to avoid languages like C/C++ to build solutions. The two-language problem in academia is real, and Julia helps to reduce that gap somewhat in certain fields.
- SatvikBeri 7mo ago
  > might not need to use a compiled language A very minor nit: Julia is a compiled language, but it has an unusual model where it compiles functions the first time they're used. This is why highly-optimized Julia can have pretty extreme performance. > I would say the performance is overstated by the community but out of the box it is good enough to avoid languages like C/C++ to build solutions. For about a year we had a 2-hour problem in our hiring pipeline where the main goal was to write the fastest code possible to do a task, and the best 2 solutions were in Julia. C++ was a close third, and Rust after that.
  - sinnsro 7mo ago
    > A very minor nit: Julia is a compiled language Caught. Should have just listed the usual suspects (C, C++, maybe Rust nowadays?). > and the best 2 solutions were in Julia. C++ was a close third, and Rust after that. Awesome. Which type of problem was this, if you can share?
    - SatvikBeri 7mo ago
      It was roughly "given this dataset with 100 time series, write code to calculate these statistics as performantly as possible. Make one single-threaded version and one using 4 cores." The C++ versions were around ~250 lines long, and the developers generally only had time to try one approach. While the Julia versions were around ~80 lines long, and the developers had time to try several approaches. I'm sure the best theoretically possible C++ version is faster than the best Julia version, but given that we're always working with time constraints, the performance per developer hour in Julia tends to be really good in my experience.
pjmlp 7mo ago
Besides all the listed points, Go's type system is relatively simple for the kind of stuff done in Julia, Mathematica, R, C++,...

Legitimate and honest question: in which circumstances would you choose Julia over more mainstream alternative like Go?

by pachico 7mo ago

Legitimate and honest question: in which circumstances would you choose Julia over more mainstream alternative like Go?

bandrami 7mo ago

Math. Places you might use Wolfram or Sage.

Human-Cabbage 7mo ago

Julia is aimed at scientific computing. It competes against Python with numpy/scipy, R, etc.

ziotom78 7mo ago
Correct, but I would add: Julia is better than Python+NumPy/SciPy when you need extreme speed in custom logic that can’t be easily vectorized. As Julia is JIT-compiled, if your code calls most of the functions just once it won’t provide a big advantage, as the time spent compiling functions can be significant (e.g., if you use some library heavily based on macros). To produce plots out of data files, Python and R are probably the best solutions.
- jey 7mo ago
  And I would further add: In addition to performance, Julia's language and semantics are much more ergonomic and natural for mathematical and algorithmic code. Even linear algebra in Python is syntactically painful. (Yes, they added the "@" operator for matmul, but this is still true).
- dgfl 7mo ago
  Disagree on the last statement. Makie is tremendously superior to matplotlib. I love ggplot but it is slow, as all of R is. And my work isn’t so heavy on statistics anyway. Makie has the best API I’ve seen (mostly matlab / matplotlib inspired), the easiest layout engine, the best system for live interactive plots (Observables are amazing), and the best performance for large data and exploration. It’s just a phenomenal visualization library for anything I do. I suggest everyone to give it a try. Matlab is the only one that comes close, but it has its own pros and cons. I could write about the topic in detail, as I’ve spent a lot of time trying almost everything that exists across the major languages.
  - dan-robertson 7mo ago
    I tried some Julia plotting libraries a few years ago and they had apis that were bad for interactively creating plots as well as often being buggy. I don’t have performance problems with ggplot so that’s what I tend to lean to. Matplotlib being bad isn’t much of a problem anymore as LLMs can translate from ggplot to matplotlib for you.
  - Certhas 7mo ago
    I love Makie but for investigating our datasets Python is overall superior (I am not familiar enough with R), despite Julia having the superior Array Syntax and Makie having the better API. This is simply because of the brilliant library support available in scikit learn and the whole compilation overhead/TTFX issue. For these workflows it's a huge issue that restarting your interactive session takes minutes instead of seconds.
  - versteegen 7mo ago
    I recently used Makie to create an interactive tool for inspecting nodes of a search graph (dragging, hiding, expanding edges, custom graph layout), with floating windows of data and buttons. Yes, it's great for interactive plots (you can keep using the REPL to manipulate the plot, no freezing), yes Observables and GridLayout are great, and I was very impressed with Makie's plotting abilities from making the basics easy to the extremely advanced, but no, it was the wrong tool. Makie doesn't really do floating windows (subplots), and I had to jump through hoops to create my own float system which uses GridLayout for the GUI widgets inside them. I did get it to all work nearly flawlessly in the end, but I should probably have used a Julia imGUI wrapper instead: near instant start time!
    - leephillips 7mo ago
      Are you referring to https://github.com/JuliaImGui/CImGui.jl https://github.com/JuliaImGui/CImGui.jl ?
      - versteegen 7mo ago
        Yes. And I did port my GUI layer to CimGui.jl. The rest of it is pretty intertwined with Makie, didn't do that yet. The Makie version does look better than ImGui though.
- ainch 7mo ago
  Even then, if you're familiar with NumPy it's pretty easy to switch to Jax's NumPy API, and then you can easily jit in Python as well.
  - JanisErdmanis 7mo ago
    As long as someone else does the porting and maintains the compatability between both subecosystems of thoose who prefer using Jax and thoose who prefer depending on the NumPy. Also not having zero overhead structs that one can in an array handicaps types of performance codes one can write.

markkitti 7mo ago

When I need to do serious math, I use Julia.

setopt 7mo ago

Scientific computing. AFAIK, library support for that in Go is almost nonexistent.

kryptiskt 7mo ago

Go is a total non-starter, it's not interactive at all. The competitors are things like Matlab, Mathematica, R or Python (with the right math libs). If you're weird you could use something like Haskell, APL or Lisp in this role, but you'd pay a hefty price in available libs.

VorpalWay 7mo ago
In what situations would a non-interactive language be a non-starter? I have never felt that I missed having a REPL when coding C++ or Rust. The only reason it is even useful in python is that the type info is laughably bad, so you need to poke things interactivly to figure out what shape of data you should even expect. (I'll take strong static typing every day, it is so much better.)
- SatvikBeri 7mo ago
  REPLs/notebooks are really nice in situations where you don't know what you want ahead of time and are throwing away 90% of the code you write, such as trying to find better numerical algorithms to accomplish some goal, exploring poorly documented APIs (most of them), making a lot of plots for investigating, or working a bunch with matrices and Dataframes (where current static typing systems don't really offer much.)
  - VorpalWay 7mo ago
    Yeah, this is a entirely different domain than what I work in (hard real-time embedded and hard real-time Linux). Though poorly documented APIs exist everywhere, but they are not something you can rely on anyway: if it isn't documented the behaviour can change without it being a breaking change. It would be irresponsible to (intentionally) depend on undocumented behaviour. Rather you should seek to get whatever it is documented. Otherwise there is a big risk that your code will break some years down the line when someone tries to upgrade a dependency. Most software I deal with is long-lived. There is code I wrote 15 years ago that is still in production and where the code base is still evolving, and I see no reason why that wouldn't be true in another 15 years as well. At least you should write tests to cover any dependencies on undocumented behaviour. (You do have comprehensive tests right?)

wolvesechoes 7mo ago

It is highly interactive and dynamic, yet performant. And it is not only about scientific computing, for almost any application can take advantage of interactive, modifiable system, where you can explore your state at any point. In others, more static langs good debuggers help with this to lesser or larger extend, but it is not the same from my experience. So better question is: in which circumstances would you choose Julia over more mainstream-y alternative like Clojure? And here scientific and numerical angle comes to play. At the same time I think Julia is failed attempt, with unsolvable problems, but it is a different topic.

Certhas 7mo ago

On top of what others have said: In many situations the alternative to Julia isn't Go but C++ (or maybe Rust though its library support is lacking). E.g. if you are writing high-ish* performance algorithmic code that should also run on GPU. Julia also heavily prioritizes composability of libraries. And though that can be a source of bugs at times, there are things we are doing in Julia with one or two PhD students that would be practically impossible (as in require an order of magnitude more implementation work) in any other language.

huijzer 7mo ago

To replace uses where you would use Matlab or R probably. I prefer Julia over Matlab or R. So data science. For production code however, it's not great since it has no static typing. Imagine having your production code crash mid-execution with the error "Function foo not found". Only dynamic languages can do that to you.

jondea 7mo ago
I broadly agree that it can be hard to nail down Julia's behaviour but it does have static typing and I think it is more subtle. Function arguments and variables can be concrete types e.g. if you were implementing an approximation for sin, you could restrict arguments to Float32 if you knew it was only suitably accurate for that type.
- huijzer 7mo ago
  > it does have static typing and I think it is more subtle. Yes sure Julia isn't fully dynamically typed, but that doesn't change the fact that it isn't fully static typed. If it was, it should be pretty easy to create static binaries and find bugs like "func not defined" at compilation time.

Joel_Mckay 7mo ago

Julia collapses entire programming paradigms into single character syntax, and often will transparently handle clean parallelism or cluster instance batching. https://docs.julialang.org/en/v1/manual/mathematical-operations/#man-dot-operators https://docs.julialang.org/en/v1/manual/mathematical-operati... Go is similar in many ways, but takes a performance hit in areas like Garbage collection. The Julia community is great, and performance projects may also be compiled into a binary image. =3

ForceBru 7mo ago
Judging by Julia's Discourse, compiling actual production Julia code into a standalone binary is highly nontrivial and ordinary users don't really know how and why to do this.
- Joel_Mckay 7mo ago
  Example should still work: https://julialang.github.io/PackageCompiler.jl/dev/devdocs/binaries_part_2.html https://julialang.github.io/PackageCompiler.jl/dev/devdocs/b... They greatly improved the generated binary file size last year. ymmv with "--trim=unsafe-warn" =3

andriamanitra 7mo ago

If you need to deal with matrices Julia's built-in support for that kind of stuff is the best out of any language I've ever seen (and I've tried dozens of different languages). It's like having first-class numpy arrays without installing any third party packages, and the syntax is even more convenient than Python. The standard library is reasonably comprehensive (not quite as big as in Python or Ruby or Go, but it's usually more well-designed). It is also an excellent language for messing about because the language and especially the REPL have tons of quality-of-life features. I often use it when I want to do something interactively (eg. inspect a data set, draw a graph, or figure out what's going on with an Unicode string, or debug some bitwise trickery). What Julia is not great at is things where you need minimal overhead. It is performant for serious number crunching like simulations or machine learning tasks, but the runtime is quite heavy for simple scripting and command-line tools (where the JIT doesn't really get a chance to kick in).

qalmakka 7mo ago

My two cents: while Julia is arguably more complex than Go, it's type system and especially its data types (N-dimensional arrays, ...) make it way more suited anything that needs to process complex data, or do anything that's even closely related to geometry. `.|>` is wonderful and makes functional-like code easier, and that's just the tip of the iceberg, macros are also beautiful and absurdly useful. Also LLVM more often than not generates faster code than the Go backend, albeit the slowdown at startup in Julia programs is often a dealbreaker for small scripts and iteration in my experience Also, the REPL. Julia's REPL is vastly better than any other language REPL, by far. Python's is good but Julia is way better, even as a calculator for instance, it has fractions and it is more suited for maths

dandanua 7mo ago
Yeah, Julia's REPL deserves special attention as it allows to do the package management (by pressing "]"), look for the functions help ("?") and do shell operations (";") without leaving it.

xeonmc 7mo ago

Its syntax is great for math and has a comprehensive ecosystem of scientific computing libraries stewarded by that timelord guy from Men in Black 3.

dandanua 7mo ago

Julia is not an alternative to Go. It is the alternative to Python (slow) and to C++ (hard and complex). Go is fast and simple but doesn't have abstractions to create complex code required by math libraries.

a-french-anon 7mo ago

You want real macros and a type system actually designed instead of "grown" on top of C/Aleph.

SatvikBeri 7mo ago

We use Julia at our quant fund. We looked into it and several other alternatives 5 years ago as a more performant replacement for numpy/scipy/etc., and Go was one of the alternatives we considered, along with things like numba and writing C extensions. Julia won for a very simple reason: we tried porting one of our major pipelines in several languages, and the Julia version was both the fastest to port and the most performant afterwards. Plus, Julia code is very easy to read/write for researchers who don't necessarily have a SWE background, while Go or C++ are not. We started using Julia in the Research Infrastructure team, but other teams ended up adopting it voluntarily because they saw the performance gains.

sinnsro 7mo ago

While others have mentioned plenty of reasons, for this particular case I want to highlight 3 things: 1. Julia has great tooling for operations research/linear programming. JuMP provides an standardise interface to interact with solvers (e.g., Gurobi, CPLEX) via wrapper libraries. 2. I like its overall ergonomics. It is fast enough that a programmer might not need to use a compiled language for performance. The type system allows for multiple dispatch. And the syntax is more approachable than say Python for matrix algebra. 3. I would say the performance is overstated by the community but out of the box it is good enough to avoid languages like C/C++ to build solutions. The two-language problem in academia is real, and Julia helps to reduce that gap somewhat in certain fields.

SatvikBeri 7mo ago
> might not need to use a compiled language A very minor nit: Julia is a compiled language, but it has an unusual model where it compiles functions the first time they're used. This is why highly-optimized Julia can have pretty extreme performance. > I would say the performance is overstated by the community but out of the box it is good enough to avoid languages like C/C++ to build solutions. For about a year we had a 2-hour problem in our hiring pipeline where the main goal was to write the fastest code possible to do a task, and the best 2 solutions were in Julia. C++ was a close third, and Rust after that.
- sinnsro 7mo ago
  > A very minor nit: Julia is a compiled language Caught. Should have just listed the usual suspects (C, C++, maybe Rust nowadays?). > and the best 2 solutions were in Julia. C++ was a close third, and Rust after that. Awesome. Which type of problem was this, if you can share?
  - SatvikBeri 7mo ago
    It was roughly "given this dataset with 100 time series, write code to calculate these statistics as performantly as possible. Make one single-threaded version and one using 4 cores." The C++ versions were around ~250 lines long, and the developers generally only had time to try one approach. While the Julia versions were around ~80 lines long, and the developers had time to try several approaches. I'm sure the best theoretically possible C++ version is faster than the best Julia version, but given that we're always working with time constraints, the performance per developer hour in Julia tends to be really good in my experience.

pjmlp 7mo ago

Besides all the listed points, Go's type system is relatively simple for the kind of stuff done in Julia, Mathematica, R, C++,...