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How an MIT research project became the Julia programming language

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Re: How an MIT research project became the Julia programming language

#11

Post by JanisErdmanis »

adgjlsfhk1 wrote: post #3
maxall4 wrote: post #2

I really want to like Julia. It has a nice type system, a good ecosystem, reasonable syntax, and it’s far faster than Python. But there are several issues with its DX that prevent me from using it: the most severe of which being the complete lack of a cache for the JIT (or JIT like system), inducing multi second compile times for scripts that run in (I last tried Julia a few years ago; perhaps this has been improved…

a between session cache had been merged for 1.14 (release expected within 6-12 months).

What is this magic, how does it differ from PkgImages Julia already has?

Re: How an MIT research project became the Julia programming language

#12

Post by ashton314 »

Julia is my go-to when I need a fast language that I can reason about in a functional way. The type system is fine, the pattern matching is pretty darn good, the metaprogramming story could be significantly better but it’s not bad and boy howdy it’s fast. So stinking fast.

Re: How an MIT research project became the Julia programming language

#15

Post by skew-aberration »

muragekibicho wrote: post #4

Julia uses 1-based indexing. It's competes with R and Matlab for the same set of users. Both R and Julia have their core functions written in C++. Absolutely nothing new. From my experience, grad students use Julia when their PI thinks a new programming language will help differentiate their next NSF proposal among vast funding requests.

It kinda feels like Julia competes for the people who write the libraries for R and Matlab. Writing fast and elegant ODE solvers, etc in Julia seems to be easier than the others and they've attracted a lot of academics for that reason.

Re: How an MIT research project became the Julia programming language

#16

Post by tonyarkles »

skew-aberration wrote: post #15
muragekibicho wrote: post #4

Julia uses 1-based indexing. It's competes with R and Matlab for the same set of users. Both R and Julia have their core functions written in C++. Absolutely nothing new. From my experience, grad students use Julia when their PI thinks a new programming language will help differentiate their next NSF proposal among vast funding requests.

It kinda feels like Julia competes for the people who write the libraries for R and Matlab. Writing fast and elegant ODE solvers, etc in Julia seems to be easier than the others and they've attracted a lot of academics for that reason.

I'm currently split between Python and Julia, having used R happily in the past for data analysis and Matlab for this and that in my EE program. For me, Julia crushes one niche that the rest of them are not good at: making the math look like the math.

https://docs.sciml.ai/ModelingToolkit/stable/tutorials/nonli...

If you've used something like SciPy or symbolic Matlab or Maxima or whatever, it always feels like I'm very carefully converting the equations I've scribbled down on paper into code and always a little nervous that I've accidentally split one variable into two names or used the wrong equality operator and am going to end up hating life, or accidentally assigned x = sp.Symbol("y") somewhere.

The Julia version is just plain beautiful. There's no ceremony other than the three @parameters, @variables, @mtkcompile macros. It lives in its own little world where you don't have to constantly watch your back to make sure you haven't duplicated a symbol somewhere.

Re: How an MIT research project became the Julia programming language

#18

Post by hn_submit »

Safe for BASIC I believe it's very difficult to make a programming language which is easy-to-learn like the creators of Julia proclaim. It may be somewhat easier to implement specific scientific functionality in the language but does that warrant the creation of yet another language?

Scanning the language it doesn't strike me at all as "simple."

Re: How an MIT research project became the Julia programming language

#19

Post by hasley »

tonyarkles wrote: post #16
skew-aberration wrote: post #15

Earlier quoted context omitted.

It kinda feels like Julia competes for the people who write the libraries for R and Matlab. Writing fast and elegant ODE solvers, etc in Julia seems to be easier than the others and they've attracted a lot of academics for that reason.

I'm currently split between Python and Julia, having used R happily in the past for data analysis and Matlab for this and that in my EE program. For me, Julia crushes one niche that the rest of them are not good at: making the math look like the math. https://docs.sciml.ai/ModelingToolkit/stable/tutorials/nonli... If you've used something like SciPy or symbolic Matlab or Maxima or whatever, it always feels like I'm v…

Getting linear algebra in a programming language close to math formulas was, for a long time, my reason to use Octave.

When I first read about Julia, I was really amazed - especially the type system with its multiple dispatching and not automatically converting between types (e.g., between integers and floats). Though, I do not know, how Julia is today.

Today, I use Python instead of Octave (or Julia) - just because it has a large ecosystem and is widely adopted. An additional advantage is that Python has much better OOP features than Octave had back then.

However, I wished Julia had the status that Python has today.

Re: How an MIT research project became the Julia programming language

#20

Post by Joel_Mckay »

maxall4 wrote: post #2

I really want to like Julia. It has a nice type system, a good ecosystem, reasonable syntax, and it’s far faster than Python. But there are several issues with its DX that prevent me from using it: the most severe of which being the complete lack of a cache for the JIT (or JIT like system), inducing multi second compile times for scripts that run in (I last tried Julia a few years ago; perhaps this has been improved…

> tried Julia a few years ago; perhaps this has been improved since?

The pre-compiled binary outputs are much smaller, and load a lot faster. =3

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