> including fast user defined types

Can numba do this yet? Without having to write special "numba classes" code?

Blaze has a giant serialization wall between it and the JVM which it needs to solve to ever be competitive in its target market. Ibis is the much more compelling effort here.

Dynd is an overly complex C++ disaster that is trying way too hard to solve problems that aren't what anyone really struggles with. The number of real world problems for which homogeneous dense multidimensional arrays of floating point numbers is the solution isn't growing much. Real problems have interesting structure that your data structures need to be able to exploit on a problem specific basis. Doing the array layer all in templated C++ with Python bindings is not a recipe for making something easy to use and effective at problems it wasn't previously designed and compiled to solve. Displacing numpy is not going to happen in the Python space.

Dask has good ideas, but the insistence on implementing everything in pure python means you're stuck with the GIL and lousy performance of your scheduler. This needs to be low overhead if they want anyone to actually use it.

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.

Interfaces to HDFS how? PyObject has to get translated somewhere.

Arrays of custom element types is a good first start, but boy is dynd a seriously overengineered way to accomplish that. I'm referring to array structure, sparsity, symmetry, linear algebraic properties that should be reflected in the type system. Python's type system is lousy for this, and C++ isn't extensible.

Julia can fix performance on heterogeneous data with gradual compiler improvements, and the string representation is due for a major rework. Python can't fix the fact that the language and libraries were not designed to be efficiently JITted, and extension interfaces are closely coupled to the CPython interpreter's API.

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

> Doesnt fast code on these types fly [in] the face of julia static optimization ethos?

What does that even mean? Julia's union types aren't intentionally slow, they just aren't implemented very efficiently yet. Major revisions of how they're implemented are definitely on the roadmap, not far away.

For fine-grained parallelism of the type you'd use MPI for, 1ms overhead (assuming that's pure overhead above and beyond the actual cost of data movement) could be significant, sure. If you have calculations that need to go for thousands of individually cheap iterations, it adds up.

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?

A better compiler is a good thing, as long as its performance model can stay reasonably transparent.

https://github.com/JuliaLang/julia/issues/8974

Agreed.

> Julia's potential ability to run on mobile, front end web,...

Julia is designed for interactive, exploratory scientific programming, like Matlab and R. I don't see any reason someone would want to do that on a phone or in a web browser when it already runs natively on a PC. Then there's the matter of Julia being designed to interface efficiently with optimized native C/Fortran libraries.

This. Scientific computing is not interested in running in web browsers.

As a former neuroscientist, I occasionally had connectivity analyses that would take 3 weeks to run. No way would I risk slowing it down with anything unnecessary. A 25% slowdown for using VMs in the browser would mean milliseconds for users and days for me.

R's "shiny" is a counterpoint. Obviously something that takes a week to run isn't going on the web. But a complex model and visualization that can be incrementally updated quickly and in real time? That would be cool.

+1 for this! For large programs -- server side Julia, but having browser native Julia via WebAssembly would make visualization and interactive analysis for data science fantastic. Performance of running large GUI applications and libraries would be much faster since WebAssembly could take advantage of the huge amount of work done for optimizing JS by tiered interpretation/JIT'ing.

I agree there's value on the visualization side. Most scientists still do a poor job of sharing results to the public and other scientists (other than by printed images in your article...)

I understand why some may see Julia as 'designed for scientific computing', but this is mostly a reflection of the work that's been put in to build up the library ecosystem. Julia could end up being a good general purpose language too, the only thing holding it back from this is the lack of library diversity and size.

WebAssembly is a neat idea, and could be useful for creating cool ways of interacting with scientific data. But a lot of code scientists write only needs to run once on one machine. And in that case, there's no reason to take the inevitable performance hit associated with WebAssembly.

> Once Julia can be run in the browser with web assembly [...]

That will never happen for Julia, because the language is designed to be fast for scientific computing... targeting web assembly is a pointless additional layer of abstraction that only negatively impacts performance.

Yea... I don't think so:

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

A "compiled binary emitted by a hypothetical slimmed down non-numerical Julia" seems to be the going theory about WebAssembly support in that issue thread, as the numerical C/Fortran libraries needed by Julia have inline assembly too. Doesn't sound like there is a direct way forward.

I see value in Julia web notebooks, where the lifting is done by a server (or localhost), but what are the motivations for running Julia in a browser?

There are definitely a lot of hypotheticals here, but many of those goals are desired in their own right and aren't specific to web assembly.

Interestingly, there's a slow-but-steady effort underway to implement a generic BLAS and LAPACK in Julia itself, since this allows for computations with custom datatypes that aren't supported by the Fortran libraries. For example, you can now do QR factorizations and solves with any numeric type, including Rationals and Quaterions: http://stackoverflow.com/questions/20985783/rational-matrix-...

Julia's dispatch system allows for these kinds of layered algorithms very nicely, and they could just as well live in separate external packages.

Web assembly would be great. Most browsers support WebGL now. Julia can utilize the GPU via the browser; the gain would be huge. Second, browsers are perfect for SETI-style distributed computation. Imagine a beowulf cluster of these browsers running downloaded Julia snippets...