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Julia 0.5 Highlights

julialang.org

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Re: Julia 0.5 Highlights

#21

Post by n00b101 »

krastanov wrote: post #15

Can anybody explain the remark about LLVM towards the end of the post? What were the difficulties of updating it, especially with respect the intermediate versions?

I'm guessing that they use LLVM's C++ API, which is not stable between releases. The C++ API changes dramatically from one version to the next, so upgrading LLVM versions typically a big effort for downstream projects. The LLVM C API, however, is meant to be stable. For C++ users, it may make some sense to write a stable C++ API wrapping the LLVM C API.

Disclaimer: I am not affiliated with the LLVM project.

Re: Julia 0.5 Highlights

#22

Post by StefanKarpinski »

krastanov wrote: post #15

Can anybody explain the remark about LLVM towards the end of the post? What were the difficulties of updating it, especially with respect the intermediate versions?

Hmm. It's hard to remember all the issues, but this was the period where LLVM moved from their "old JIT" backend to MCJIT [1]. IIRC, 3.4 broke the old JIT but MCJIT still wasn't developed enough for Julia to use. At some point the old JIT was removed entirely but MCJIT was still not usable (for us) and that didn't resolve itself entirely until 3.7.

[1] http://llvm.org/docs/MCJITDesignAndImplementation.html

Re: Julia 0.5 Highlights

#23

Post by hasch »

baldfat wrote: post #7
christophilus wrote: post #2

I'm curious to hear from people who are using Julia on a regular basis. What's the UX like? I only looked at it briefly and was turned off by some things (1-based indexing comes to mind). But I really liked other things (multiple dispatch).

1-based for statistics is a STRONG reason to use it. 0 based is ridiculous for a statistics program and the reason why I left Python. I understand for looping and the arras to be internally 0 but when doing statistics I should have to use two different index.

can you give me an example? I did all sorts of numerical indexing programming, from fortran, matlab, python/numpy and R, and I am definitely more comfortable with 0 based indexing.

Re: Julia 0.5 Highlights

#24

Post by 3JPLW »

krastanov wrote: post #15

Can anybody explain the remark about LLVM towards the end of the post? What were the difficulties of updating it, especially with respect the intermediate versions?

If you're really curious, you can follow some of the breadcrumbs through the "Activate LLVM 3.7" GitHub issue (which was originally started for version 3.5).

https://github.com/JuliaLang/julia/issues/9336

Re: Julia 0.5 Highlights

#26

Post by daemonomania »

dreww2 wrote: post #16
baldfat wrote: post #8

5 years ago I thought for sure I would be using Julia today. (I mostly use R) I have found that news tools have come into play that I haven't really felt the need for a faster language then R for my work. I'm interested in people's everyday use of Julia and how it has impacted your workflow. I don't work with "Big Data" most of my data sets are bellow 100k in size. Anyone using Julia for medium and small data sets?

I thought the same thing as well. Unfortunately, there is still a bit of work to be done on the Julia side. The tidyverse ecosystem in R has relentlessly focused on ease of use and package compatibility, such that everything "fits" together in a logical way. I can't emphasize enough how much of a difference this makes in being productive with a language. Many of the things I do on a regular basis in R are actually ra…

As the maintainer of StructuredQueries.jl, I appreciate the recognition :) The JuliaStats community is indeed aiming for a more integrated ecosystem. It will take time, but I agree with you that our general direction is heartening.

Re: Julia 0.5 Highlights

#27

Post by kkylin »

baldfat wrote: post #8

5 years ago I thought for sure I would be using Julia today. (I mostly use R) I have found that news tools have come into play that I haven't really felt the need for a faster language then R for my work. I'm interested in people's everyday use of Julia and how it has impacted your workflow. I don't work with "Big Data" most of my data sets are bellow 100k in size. Anyone using Julia for medium and small data sets?

I've used Julia for prototyping Monte Carlo and data assimilation algorithms, and also solving simple (1d) nonlinear PDEs (Fourier-spectral code with exponential RK timestepping -- not very complicated as these things go). It's also more or less completely replaced Matlab for me in terms of plotting and visualization (most of what I need is very simple). So far it has been very pleasant, so long as one remembers that breaking changes can and do occur between versions.

I generally find Julia to be a more expressive and fun language to program in than R or Python. It may be my background -- I've done a fair bit of work in Scheme, and in many ways Julia has lots of what I liked about Lisp in it. I don't need all the numerics and stats libraries that R or Python have; what little I need is easy to cobble together very quickly in Julia or has already been implemented. And the FFI is easy to use; calling C is pretty straightforward.

Re: Julia 0.5 Highlights

#28

Post by kkylin »

christophilus wrote: post #2

I'm curious to hear from people who are using Julia on a regular basis. What's the UX like? I only looked at it briefly and was turned off by some things (1-based indexing comes to mind). But I really liked other things (multiple dispatch).

As others have mentioned, numerical packages like Matlab use 1-based indexing. Most of us who have a numerics background are used to switching back and forth, so I'm fairly oblivious to it.

Re: Julia 0.5 Highlights

#29

Post by dreww2 »

daemonomania wrote: post #26
dreww2 wrote: post #16

Earlier quoted context omitted.

I thought the same thing as well. Unfortunately, there is still a bit of work to be done on the Julia side. The tidyverse ecosystem in R has relentlessly focused on ease of use and package compatibility, such that everything "fits" together in a logical way. I can't emphasize enough how much of a difference this makes in being productive with a language. Many of the things I do on a regular basis in R are actually ra…

As the maintainer of StructuredQueries.jl, I appreciate the recognition :) The JuliaStats community is indeed aiming for a more integrated ecosystem. It will take time, but I agree with you that our general direction is heartening.

Of all the Julia packages out there I am most excited about StructuredQueries :) Please keep up the great work, things look great so far.

Re: Julia 0.5 Highlights

#30

Post by preygel »

baldfat wrote: post #9
preygel wrote: post #6

Exciting to hear -- I have used Julia for prototyping, and have found it to be excellent for that: In my experience there are still some rough edges as compared to the Python ecosystem (of course!), which together with the 0.x status make it impractical for many production situations. However it is fantastic for prototyping numerical code, the type system is a pleasure, and the JuMP mathematical optimization library…

Really interested in why you would use a "faster" language in julia then move to a "slower" language like Python or R?

Typically a mix of reasons:

- Needing to interact with an existing codebase, and an existing developer base. If everyone knows and uses Python and only a few use Julia, it is too early to put Julia in production. If there are proprietary libraries, now may not be the best time to commit to porting them to Julia.

- Language and ecosystem stability. I started something on 0.4, and with 0.5 there were a raft of deprecations. If the code will live several years, that's a support commitment with unclear value.

- Library maturity. If I need to build a web app, read an Excel, read a CSV with dates quickly, consume a SOAP endpoint, etc etc in Python -- no problem. With Julia I will mostly be fine, but am likely to run into some cases that are not yet 100% there.

- Most code does not need the extra performance, so once you have a fast prototype as a performance target it is often not that hard to hit similar performance with Python + numba/Cython.

Note for that last point: there is a lot of value to not worrying about this in the exploratory stage, and getting a performance target (for later optimization) as a nice byproduct.

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