Hey Boston Lisp

[Geoffrey S. Knauth]

I first encountered Julia at StrangeLoop a few years ago.
I haven't spent time with it but I intend to look into Julia more.

Do you think there is a possibility that numerical code that would have
been written in Fortran in the past might now be coded or recoded in Julia?

Do Julia macros affect performance in a noticeable way?

Geoff

On Wed, Apr 15, 2020, at 14:05, Michael Bukatin wrote:
> 
> 
> I have been looking at Julia and its ecosystem in the last few months, and 
> it is a very interesting experience. The language has full-strength Lisp 
> macros, and full-strength multiple dispatch (so it is a full Lisp), while 
> the user-facing syntax is not Lisp-like:
> 
> https://docs.julialang.org/en/v1/manual/metaprogramming/
> 
> So it's both a Lisp and a non-Lisp.
> 
> Generally speaking, people who creat Julia are consistently trying to "eat 
> one's cake and to have it too", along multiple dimensions. Another axis is 
> that the language is more flexible than Python, but is as fast as C. This 
> is achieved via a very tasteful language design (the compiler is a normal 
> competent LLVM compiler without miracles, it does not play any special 
> role in this combination of expressiveness and speed).
> 
> I have also found Julia open-source software on github unusually readable 
> and easy to understand (it also tends to be very compact).
> 
> The reason I was looking at Julia was that I was having an unusually 
> flexible class of machine learning problems (a class of neural machines
> which is based on processing complicated structured data streams, and on 
> using "flexible tensors" with tree-shaped indices; so one can do much more 
> with these neural machines than with traditional neural nets).
> 
> Even the most flexible Python frameworks, such as PyTorch, are too rigid 
> for this class of problems, because they are oriented towards fixed 
> multidimensional arrays ("tensors").
> 
> In this sense, Julia ecosystem seems to have a perfect fit, the Julia Flux 
> machine learning framework, which is specifically oriented towards maximal 
> flexibility and away from "tensors", while still being focused on high 
> performance:
> 
> https://github.com/FluxML/Flux.jl
> 
> So far I was mostly reading other people's code, and doing small-scale
> explorations of my own (and creating publicly available notes in the 
> process): https://github.com/anhinga/2020-julia-drafts
> 
> I think that what I am trying to do with Julia Flux should be doable
> single-handedly (the tools seem to be that good), but I also hope to find
> collaborators (a small team would be able to move really fast with this).
> 
>    - Mishka
> 
> On Tue, 14 Apr 2020, Jonathan Godbout wrote:
> 
> > Hey Everyone,
> > I hope you're doing well and staying safe.
> > Sorry for the long wait between messages.
> > As Didier just said the ELS will be online, yay!
> > 
> > How's everyone doing?
> > Fare, how's the startup, I miss the details.
> > 
> > Jon
> > 
> > 
> >
> 
>

-- 
Geoffrey S. Knauth | http://knauth.org/gsk