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Julia Computing Raises $4.6M in Seed Funding

juliacomputing.com

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Re: Julia Computing Raises $4.6M in Seed Funding

#91

Post by jacobolus »

flavio81 wrote: post #53
zbobet2012 wrote: post #50

Earlier quoted context omitted.

Julia is a great language, but it is still focused on the numerical computing world and is not "1.0" (language and API's are not locked down). I wouldn't really compare it to Go or Rust at this point. Go is focused on building services at its core (e.g. external event driven workflows). Aka microservices. Rust is focused on being a "traditional" systems programming language. Things like web browsers (duh), office sui…

If you take a look at the features (esp. macros and multiple dispatch) you can see that is readily extensible for lots of applications. Thus the reason it is also sold as a "general purpose" language. Additonally, the very good support for threading and multiprocessing means it has a good future with services, async IO, and with event-loop web servers (a la node.js and friends). Finally, the focus on fast speed for n…

> good future with services, async IO, and with event-loop web servers [...] particularly suited to games

This all seems extremely unlikely (compared to e.g. widespread success in scientific computing, which is still far from assured). Good luck though.

Re: Julia Computing Raises $4.6M in Seed Funding

#92

Post by walshemj »

Fomite wrote: post #54
flavio81 wrote: post #43

I wish them all the best. Julia seems to a good idea -- a high performance language with easy syntax (easy for Python users to jump to Julia), very good features for threading/multiprogramming, good type system... ...here it seem like any other modern language, until you see that Julia has something that many other languages lack: true macros (true metaprogramming.) A big feature. And multiple dispatch on all functio…

For non-CS scientific computing users, "a high performance language with easy syntax" is a big feature.

Would that not be true for all technical / scientific use cases if not most use cases in general?

Re: Julia Computing Raises $4.6M in Seed Funding

#93

Post by walshemj »

simonbyrne wrote: post #65
SolarNet wrote: post #18

Earlier quoted context omitted.

Answering the actual question: JuliaPro is basically VisualStudio Express vs. Pro (e.g. there's a free version and then an expensive version with better integrations, better enterprise level tools, etc). JuliaRun is basically a cloud service (e.g. charge for hosting) designed for julia code. JuliaBox seems to be a cloud IDE / google docs / notebook style product. Probably with a similar model to github eventually (e.…

- JuliaPro is free, but the paid version comes with support and integration with Excel and JuliaRun (possibly more features in future). - JuliaRun is for easily scalable deployment of code, either for cloud or local servers. - JuliaBox is as you say, available here https://juliabox.com/ - JuliaFin is completely separate, and currently consists of Bloomberg integration and the Miletus contract specification and pricin…

I would say that NOT having excel integration would be a feature :-)

Re: Julia Computing Raises $4.6M in Seed Funding

#94

Post by ferdterguson »

baybal2 wrote: post #15
olympus wrote: post #10

Earlier quoted context omitted.

They sell an enterprise version of Julia for $1500 per year. The main things you get are support, MS Excel integration, and a non-GPL license. They also offer consulting, training, and a few other flavors of Julia (large scale server based gets its own edition). I'm sure the main draw will be companies that don't want to be stuck with a GPL license in their product.

>They sell an enterprise version of Julia for $1500 per year. $1500 per year You can't support 100 people strong developer collective on proceeds from licensing of such product at all, given how small that commercial "data science" market is.

Maybe that's just the language, not the support?

Re: Julia Computing Raises $4.6M in Seed Funding

#95

Post by ferdterguson »

flavio81 wrote: post #84
nnm wrote: post #83

Earlier quoted context omitted.

For all the cases I worked on, R is not slow at all. At home, I wrote a R package to do deep learning (include most common layers, but only conv layer was implemented in C++) from scratch, and I also wrote one in python (using numpy + numba). My R version is same fast as python on the MNIST dataset (without conv layers, hence all code is in plain R or python).

What this means is that for your purposes R is fast enough. But in the absolute sense (or in any case, relative to other programming platforms), R is one of the slowest ever. R is usually slower than Python, and Python in some cases can be 100x slower than C. Performance, assuming identical hardware, depends on the program itself (that is, on what you are trying to calculate or compute or perform), but if you choose…

> Python in some cases can be 100x slower than C

Yeah, the Python implemented version is. But people doing serious computing in Python that requires speed are doing it with NumPy or even Cython or just straight up calling C/Fortran libraries in Python.

Re: Julia Computing Raises $4.6M in Seed Funding

#96

Post by maimaiml »

flavio81 wrote: post #43

I wish them all the best. Julia seems to a good idea -- a high performance language with easy syntax (easy for Python users to jump to Julia), very good features for threading/multiprogramming, good type system... ...here it seem like any other modern language, until you see that Julia has something that many other languages lack: true macros (true metaprogramming.) A big feature. And multiple dispatch on all functio…

> (If you need more power and flexibility than Julia with good processing speed, i think only Common Lisp will clearly provide it.)

Common Lisp is terrible for numerical computing, and the type system is really quite weak IMO.

Re: Julia Computing Raises $4.6M in Seed Funding

#97

Post by throwaway7645 »

faitswulff wrote: post #79

I wonder if this would be a feasible funding strategy for Crystal ( https://crystal-lang.org/ ) as well. From the scattered benchmarks I could find online†, Crystal seems to be just as fast, if not a bit faster. † https://github.com/kostya/benchmarks , http://blog.seraum.com/crystal-lang-vs-nodejs-vs-golang-vs-j...

Well, they're both implemented in LLVM, but Julia uses a JIT and not straight to a native binary. I bet they have very similar performance. The use cases are different though. Julia is a replacement for MATLAB, R, Fortran, & scientific Python with speed and good macro support. Crystal is basically fast/native Ruby, so websites and business apps. You could technically use either for those applications, but the communities will be mostly scientific computing for Julia and business apps for Crystal.

Re: Julia Computing Raises $4.6M in Seed Funding

#98

Post by throwaway7645 »

flavio81 wrote: post #55
drej wrote: post #36

Julia frustrates me. I was in a mathematical modelling sphere, so I learned the language basics years ago and immediately fell in love (multiple dispatch, optional types, broadcasting, ...). But I couldn't get approvals at work to push it, because it was immature. Then a month later, all my code broke. I rewrote it using new APIs. It broke again. I know they reserved the right to make breaking changes up until 1.0, b…

An alternative would be getting your bosses approve for you to use Common Lisp. It is mature (30+ years in use, rock solid ANSI standard), many compilers available for many plataforms, highly portable code, multiple dispatch (CLOS is arguably the most powerful object oriented system available), you can also very easily call C libraries with CFFI and there is portable support for threading... ...and well written CL co…

Yes Common Lisp is freaking amazing, but it isn't as good of an out of the box experience as a lot of these products. How do you do plotting besides pushing to GNUPlot? The REPL is great, but can you specify types? Yes it can reach near C/Fortran speeds, but how much time will I spend optimizing it? Are the built-in linear algebra, optimization...etc libraries good, or must I toil with FFI? How much time is really saved over dealing with all that and learning SBCL, slime, emacs...etc? Data scientists just don't have the time.

Re: Julia Computing Raises $4.6M in Seed Funding

#99

Post by hpcjoe »

bokertov wrote: post #78
flavio81 wrote: post #58

Earlier quoted context omitted.

Exactly, that's why I think Julia is a good idea. For scientific users used to R or Python, the performance should knock their socks off. (But users doing their work in C, C++ or Fortran will probably see little or no improvement).

Do we really need Julia given that Python has Numpy which does a lot of the heavy lifting in numerical computation and is pretty fast?

It should be understood that numpy is written in C, and is an example of the "2 language problem" that Julia solves rather well.

The question really is the converse ... given how deft Julia is with general programming as well as numerically intensive programming, do you really need Python + numpy(C).

Re: Julia Computing Raises $4.6M in Seed Funding

#100

Post by throwaway7645 »

smt88 wrote: post #40
drej wrote: post #36

Julia frustrates me. I was in a mathematical modelling sphere, so I learned the language basics years ago and immediately fell in love (multiple dispatch, optional types, broadcasting, ...). But I couldn't get approvals at work to push it, because it was immature. Then a month later, all my code broke. I rewrote it using new APIs. It broke again. I know they reserved the right to make breaking changes up until 1.0, b…

My girlfriend does incredibly complex modeling (she's a computational geneticist). From my limited understanding, it sounds like you may have some of the same challenges that she does. Do you think Python and Go are more suited to that domain than R or Julia?

Go isn't meant for computational science, but business code as services. Python is really good in this area when combined with Matplotlib, Numpy, & ScyPy.
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