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tanlerminon Why I am betting on Julia (2014)

Agreed.

tanlerminon Why I am betting on Julia (2014)

I'm not sure you can make a linear projection.

This could be early exponential growth.

Or the constant growth may hit a tipping point as a critical mass of tools and infrastructure is developed.

tanlerminon Why I am betting on Julia (2014)

Gotcha!

tanlerminon Why I am betting on Julia (2014)

It means that I thought Julia wanted to avoid inching too much towards tracing JIT optimization.

Well, luckily dask isn't meant for MPI style parallelism. I was wondering how it compared so something like computeframework.

Good to hear that union type performance will be optimized. Are there any issues I can follow?

tanlerminon Why I am betting on Julia (2014)

Try numpy record arrays

tanlerminon Why I am betting on Julia (2014)

What other channels?

tanlerminon Why I am betting on Julia (2014)

I don't see why all that can't be reflected in dynd type system and array metadata. It's just early and foundations are still being laid.

Revelation julia union etc performance...are these improvents a given, hypothetocal or hope? Doesnt fast code on these types fly kn the face of julia static optimization ethos?

Also serious question: dask's use of fast python datastructures like dictionaries gives it a 1ms per task overhead. Is that slow? How does it compare to other dag frameworks like julia etc

tanlerminon Why I am betting on Julia (2014)

Granted special Numba class syntax, and nowhere near as elegant or integrated as Julia.

Dask distributed interfaces directly with HDFS...no need for serialization.

Dynd is specifically designed to deal with arrays of custom types...criticism sounds like it would be more aptly directly towards numpy.

once it gains Numba binding it will lose the Python overhead.

Re Dask...is 1ms per task too much? It has a threading scheduler that can work with numpy arrays to release the GIL....so not bound by that with numerical code.

Regarding regular list and text processing... Julia's poor performance with heterogenous data actually makes it slower than python for cleaning the corresponding dirty and text datasets.

tanlerminon Why I am betting on Julia (2014)

Yea... I don't think so:

https://github.com/JuliaLang/julia/issues/9430 http://juliacomputing.com/blog/2016/02/09/static-julia.html

tanlerminon Why I am betting on Julia (2014)

I think it will all come down to web assembly. Why?

Python has blaze, numba, dynd and dask going for it. These all ameliorate (and exceed Julia in some respects) many of the disadvantages of python (including fast user defined types). Then there are the libraries that while some can be used in Julia, you will never get full reliability, ease of use and compatibility.

On the other hand, Julia has amazing metaprogramming and much cleaner scientific syntax.

I think it will come down to whether Python can compile to fast LLVM regular standard lib programs.

Once (if?) Julia can be run in the browser with web assembly (using ahead of time compilation to produce small binaries), python has no chance if it doesn't follow suit. Python has no advantage that can permanently match Julia's potential ability to run on mobile, front end web, back end etc all from one beautiful codebase.

What does HN think?

tanlerminon Inference Convergence algorithm in Julia

Does this improve performance by widening the bounds of type stability?

tanlerminon A Python-to-JavaScript Transpiler with BabelJS and SourceMaps

Can someone who downvoted me explain the flaw in my argument?

tanlerminon A Python-to-JavaScript Transpiler with BabelJS and SourceMaps

http://www.transcrypt.org/

Does multiple inheritance

tanlerminon A Python-to-JavaScript Transpiler with BabelJS and SourceMaps

Too late. Python is screwed. Its still slow and not portable.

Julia is a dynamic language that can compile to fast binary code. Once it can be run in the browser with web assembly, faster and nicer syntax than javascript, why would anyone use python?

tanlerminon A Python-to-JavaScript Transpiler with BabelJS and SourceMaps

Is this supposed to be able to work for really large apps at some point? Some commenter lamented how it didn't work with django.

tanlerminon A Python-to-JavaScript Transpiler with BabelJS and SourceMaps

Did you really expect that to work?!

tanlerminon Feather: A Fast On-Disk Format for Data Frames for R and Python

What do you use Julia for?

tanlerminon Ask HN: Will there be native web assembly Mobile APIs?

Thanks for the information.

tanlerminon Python, Machine Learning, and Language Wars (2015)

No you don't. It now does python to JS compilation. And that's if you want client side callbacks.

tanlerminon Dplython: Dplyr for Python

Blaze has already spent alot of time on this usecase and seems quite promising for a whole host of other reasons. Perhaps it would be best to combine forces?

Also see this:

https://github.com/blaze/blaze/pull/484

tanlerminon Dplython: Dplyr for Python

Blaze for python does this

tanlerminon Sorting a Billion Numbers with Julia

Are you being sarcastic?

tanlerminon Julia: A Fast Language for Numerical Computing

Okay, check this out: DataFlow programming for Julia https://github.com/MikeInnes/Flow.jl

tanlerminon Experimental support for WebAssembly in V8

Yea I meant the native binaries in the recent ahead of time compilation work.

tanlerminon Julia: A Fast Language for Numerical Computing

Very interesting I didn't realize the difference.