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Can someone please explain to me, a mere mortal, what is the big deal with Julia. Why use it, when there are so many other good languages out there with more community/support? Honest question.
designers seems to have a good amount of PLT knowledge and made good foundations
Can someone please explain to me, a mere mortal, what is the big deal with Julia. Why use it, when there are so many other good languages out there with more community/support? Honest question.
My understanding is that it runs faster than native python and R. That said with Numba and other libraries, see no point.
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My understanding is that it runs faster than native python and R. That said with Numba and other libraries, see no point.
Numba is great for pure functions on primitive types but it breaks down when you need to pass objects around. PyPy is fantastic for single-threaded applications but doesn't play nicely with multiprocessing or distributed computing IME. Numpy helps for stuff you can vectorize, but there's a lot of stuff you can't (or can but shouldn't); it also brings lots of minor inconveniences by virtue of not being a native type -…
I guess this would be a good place to mention that we're hiring for lots of positions, so if you would like to help build JuliaHub, or work on compilers, or come play with SDRs, please take a look at our job openings :) : https://juliacomputing.com/jobs/
>or come play with SDRs This sounds like an absolute dream!
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If you are doing very high-performance numerical work, your choices¹ are Fortran, C, C++, or Julia. Julia is way more fun to program in than the other choices. Also, it has some properties² that make code re-use and re-combination especially easy. 1 https://www.hpcwire.com/off-the-wire/julia-joins-petaflop-cl... 2 https://arstechnica.com/science/2020/10/the-unreasonable-eff...
What's the argument against using R and dropping into RCpp for very limited tasks? I (helped) write a very widely used R modelling package and while I wasn't doing anything on the numerical side, we seemed to get great performance from this approach -- and workflow-wise it wasn't too dissimilar to 25 years ago where I had to occasionally drop in X86 assembly to speed up C code! (Not a hater of Julia at all, very much…
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>or come play with SDRs This sounds like an absolute dream!
What's SDR in this context? Not software-defined radio, right? (Though I suppose Julia is a good fit for signal processing!)
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If you are doing very high-performance numerical work, your choices¹ are Fortran, C, C++, or Julia. Julia is way more fun to program in than the other choices. Also, it has some properties² that make code re-use and re-combination especially easy. 1 https://www.hpcwire.com/off-the-wire/julia-joins-petaflop-cl... 2 https://arstechnica.com/science/2020/10/the-unreasonable-eff...
What's the argument against using R and dropping into RCpp for very limited tasks? I (helped) write a very widely used R modelling package and while I wasn't doing anything on the numerical side, we seemed to get great performance from this approach -- and workflow-wise it wasn't too dissimilar to 25 years ago where I had to occasionally drop in X86 assembly to speed up C code! (Not a hater of Julia at all, very much…
I am a python developer who has dabbled with Julia but it never stuck for me.
I think Julia was built by academics for other academics running innovative high performance computing tasks. It excels at the intersection of 1) big data, so speed is important, and 2) innovative code, so you can't just use someone else's C package. Indeed, Julia's biggest successful applications outside academica closely resemble an academic HPC project (eg Pumas). I think it will continue to have success in that niche. And that's not a small niche! Maybe it's enough to support a billion dollar company.
But most of us in industry are not in that niche. Most companies are not dealing with truly big data, on our scale, it is cheaper to expand the cluster than it is to rewrite everything in Julia. Most who ARE dealing with truly big data, do not need innovative code; basic summary statistics and logistic regression will be good enough, or maybe some cloud provider's prepackaged turn key neural nets scale out training system if they want to do something fancy.
I think for Julia to have an impact outside of academia (and academia-like things in industry) it will need to develop killer app packages. The next PyTorch needs to be written in Julia. Will that happen? Maybe! I hope so! The world would be better off with more cool data science packages.
But I think the sales pitch of "it's like Pandas and scikit but faster!" is not going to win many converts. So is Jax, Numba, Dask, Ray, Pachyderm, and the many other attempts within the Python community of scaling and speeding Python, that require much less work and expense on my part for the same outcome.
Again, congrats to the team, I will continue to follow Julia closely, and I'm excited to see what innovative capabilities come out of the Julia ecosystem for data scientists like me.
Earlier quoted context omitted.
If you are doing very high-performance numerical work, your choices¹ are Fortran, C, C++, or Julia. Julia is way more fun to program in than the other choices. Also, it has some properties² that make code re-use and re-combination especially easy. 1 https://www.hpcwire.com/off-the-wire/julia-joins-petaflop-cl... 2 https://arstechnica.com/science/2020/10/the-unreasonable-eff...
What's the argument against using R and dropping into RCpp for very limited tasks? I (helped) write a very widely used R modelling package and while I wasn't doing anything on the numerical side, we seemed to get great performance from this approach -- and workflow-wise it wasn't too dissimilar to 25 years ago where I had to occasionally drop in X86 assembly to speed up C code! (Not a hater of Julia at all, very much…
In other words, you can (empirically) get a lot done that way, but there is always friction.