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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

#81

Post by bachmeier »

matt4077 wrote: post #30
drenvuk wrote: post #19

So this is why every once in a while we see some article about Julia being as easy to use as Python while having nearly the performance of C. That's some very slick but sketchy marketing tactics. I'd rather deal with Cython apache license than deal with this GPL stuff for commercial use.

This seems to be a terrible misinformed comment. Not only isn't there really a problem with using GPL tools in a commercial context, as long as they don't become part of your work product. GCC is probably used to compile a large percentage of commercial software. And I'm sure you're writing comments in Chrome, yet expect to retain the copyright on those words. But what makes this really asinine is that Julia isn't ev…

What I really dislike about a lot of the GPL arguments is that they are complaints that the GPL doesn't let you freeload masked as arguments that the GPL is anti-business. The GPL is one of the most business-friendly licenses there is. The freedoms guaranteed by the GPL mean dual licensing works. Good luck with a dual licensing strategy if you release your software under the MIT license.

Re: Julia Computing Raises $4.6M in Seed Funding

#82

Post by giancarlostoro »

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…

I have yet to fully jump into Julia but it is one language that has impressed me for a while, and I've seen a good number of them that impress me but Julia has a few things that to this day I think back to like code_native() and how it returns assembly code for whatever you throw at it, might seem trivial to others and y'know you can do it with C too but it's still cool you can just do it.

Your description of it makes it very intriguing and makes me want to revisit it.

Re: Julia Computing Raises $4.6M in Seed Funding

#83

Post by nnm »

flavio81 wrote: post #56
smt88 wrote: post #40

Earlier quoted context omitted.

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?

R is very, very slow compared to all the other programming platforms (including Ruby, a nice language but with some of the slowest implementations out there). R syntax is very unlike most programming languages so jumping to other languages can feel strange. Python with Numpy, SciPy and Pandas, is the current widespread alternative to R and a good starting point. Recommended!!

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).

Re: Julia Computing Raises $4.6M in Seed Funding

#84

Post by flavio81 »

nnm wrote: post #83
flavio81 wrote: post #56

Earlier quoted context omitted.

R is very, very slow compared to all the other programming platforms (including Ruby, a nice language but with some of the slowest implementations out there). R syntax is very unlike most programming languages so jumping to other languages can feel strange. Python with Numpy, SciPy and Pandas, is the current widespread alternative to R and a good starting point. Recommended!!

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 many different programs you can have a comparison between platforms ("platform" in this case means combination of programming language plus compiler or interpreter.)

Here is a graphic comparing Julia to other platforms: https://julialang.org/benchmarks/

You can see that according to that graphic, R can be up to 400x slower than Julia (or C).

There is another independent benchmark here. It does not include R but it includes Python, and it's very interesting.

http://benchmarksgame.alioth.debian.org/

But the best comparison graphs are here, this is worth a look: http://blog.gmarceau.qc.ca/2009/05/speed-size-and-dependabil...

Re: Julia Computing Raises $4.6M in Seed Funding

#85

Post by KenoFischer »

tnecniv wrote: post #77
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…

> Julia is a great language, but it is still focused on the numerical computing world and is not "1.0" Word on the street is that Julia is supposed to get a 1.0 announcement at JuliaCon, which started today.

Unfortunately 1.0 is delayed for a couple of months. 0.6 was released yesterday though, which already includes many of the features that were originally scheduled for 1.0.

Re: Julia Computing Raises $4.6M in Seed Funding

#86

Post by tavert »

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?

Numpy is good for vectorized calculations on dense multidimensional arrays of homogenous 64 or 32 bit floating point numbers, or integers. If that describes your problem and you like Python, by all means use numpy. If that doesn't describe your entire problem, you'll need more tools.

Re: Julia Computing Raises $4.6M in Seed Funding

#87

Post by ghc »

pixelmonkey wrote: post #67

Anyway else think it's interesting that $4.6m could be labeled "seed funding"? Seed round 2016 average is around $1m per Crunchbase: https://techcrunch.com/2016/09/07/crunchbase-sees-rise-in-av... Cooley has seed round pre-moneys at $4-6m. (Thus, a $5m check is pretty much out of question at that stage.) https://www.cooleygo.com/trends/ So, this is much more like a Series A? I wonder if they were advised to call it s…

General Catalyst and Founder Collective are both Boston funds, so SV rules don't apply here. For better or worse raising in Boston is very different. I did a bit of diligence on this deal as it was going around (for a different fund), and the dynamics of the company stage probably dictated the raise size.

It's not uncommon for an institutional seed round to be $2-3MM on a valuation of $6-15MM in Boston right now. The size of rounds is partially a function of how frothy the OSS tools market has gotten. I'm looking at another hybrid enterprise/OSS deal that has a valuation north of $15MM.

A rounds nowadays look for $!MM ARR so you can say the letters shifted as well :)

Re: Julia Computing Raises $4.6M in Seed Funding

#88

Post by wott »

rattray wrote: post #7
krupan wrote: post #2

What is the business model? How are they making money?

https://juliacomputing.com/products/ - JuliaPro is the go-to product for anyone who wants to use Julia – professionals, engineers, software developers and data scientists. Beginners and experts can build better software quicker and dramatically improve performance. - JuliaRun is for scalable deployment of Julia in production for real time analytics and large-scale parallel simulations in the public cloud or a private…

> JuliaBox is the number one choice for universities teaching Julia since students can get started with Julia in seconds with no installation required.

I don't understand. Julia is not stable at all yet. Who would want to teach it now? What would be the point?

Re: Julia Computing Raises $4.6M in Seed Funding

#89

Post by Q6T46nT668w6i3m »

mac01021 wrote: post #38
Q6T46nT668w6i3m wrote: post #34

Earlier quoted context omitted.

Interesting. I'm a member of a Broad Institute lab that recently evaluated Julia. We reached the conclusion that Julia, while promising, lacked the flexibility and performance of our existing tools. I suppose this is a problem that investment could address!

What are the existing tools that it doesn't match up to?

Python’s scientific ecosystem (i.e. Keras, matplotlib, NumPy, pandas, scikit-image, scikit-learn, SciPy, and TensorFlow, et al.)

Re: Julia Computing Raises $4.6M in Seed Funding

#90

Post by apesti »

wott wrote: post #88
rattray wrote: post #7

Earlier quoted context omitted.

https://juliacomputing.com/products/ - JuliaPro is the go-to product for anyone who wants to use Julia – professionals, engineers, software developers and data scientists. Beginners and experts can build better software quicker and dramatically improve performance. - JuliaRun is for scalable deployment of Julia in production for real time analytics and large-scale parallel simulations in the public cloud or a private…

> JuliaBox is the number one choice for universities teaching Julia since students can get started with Julia in seconds with no installation required. I don't understand. Julia is not stable at all yet. Who would want to teach it now? What would be the point?

I recently used Julia in my numerical analysis course and it worked well for the most part. We couldn't use it for some of the larger projects because it wasn't as optimized as MATLAB for certain matrix operations. However, it was great most of the time and better than using MATLAB on the university computers.
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