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Julia 1.0

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Re: Julia 1.0

#191

Post by mikec3010 »

ur-whale wrote: post #169
mikec3010 wrote: post #122

Earlier quoted context omitted.

> for a computer and how it functions And what about for a human and how it functions? Are humans here to make computers' lives easier or vice versa? Humans think from 1...N inclusive and this is the source of a litany of bugs when users first learn a language. And what you described in asm is just one implementation. In fact, the array documentation says Julia doesn't guarantee tight packing so it doesn't even apply…

>Humans think from 1...N I think you're assuming everyone thinks the way you do. I assure you that's not the case. There are legions of people (and not just programmers, math folks do it too) who don't "think from 1 to N"

True, after they learn a programming language. I meant when we're children and taught to count, we don't start at 0.

Re: Julia 1.0

#192

Post by jabl »

askaboutit wrote: post #139

I don’t do any scientific or numerical programming. So as someone interested in web development. Backend programming. Serverless. High performing code without being any more strenuous than Ruby or Python. Is Julia a good fit? I tried Crystal. It’s nice but the type system definitely makes it much harder to achieve things than Ruby.

In principle Julia is a general purpose language, although so far the ecosystem is heavily biased towards scientific computing.

So while you might find the language itself pleasant, you'll probably find a lack of libraries.

(personally I'm a fan of strong static type systems (e.g. haskell), and I think it is a shame the designers didn't go down that path. But hey, they did all the work and not me, so who am I to complain. Kudos to the 1.0 milestone!)

Re: Julia 1.0

#193

Post by ur-whale »

SolarNet wrote: post #184
ur-whale wrote: post #164

Earlier quoted context omitted.

>It's not any harder to get used to than using whitespace for blocks in Python, or having to end every damn statement with a semicolon in the C inspired languages. Strongly disagree. I can move from C to Python without a second thought and rewrite code from one to the other without even thinking. Having to rewrite an algorithm working on multidimensional arrays that was written with 0-based conventions to 1-based con…

Ok, then use the built in Julia features to use any offset you want inside your algorithm's code. Compilers are pretty good about optimizing integer constants.

Everyone in this thread seems to believe that 1-based indices is a non-problem because Julia "offers a choice".

My reaction:

   - the default is 1-based, which means the bulk of Julia code will adopt the convention and therefore the vast majority of coders in 2018 will have to do mental gymnastics to understand what the code does.

   - when I read Julia code, I will never know which convention the code was written with unless I dig to find where that particular flag is set.

   - passing 0-based arrays to a library routine that expects 1-based stuff, what happens then?

   - for code that will be 0-based and uses library code that expects 1-based (assuming that's possible without paying copying overhead) will force the code to mentally switch between the two modes. Ugh.
In short: offering is a choice is maybe even worse than enforcing 1-based.

Re: Julia 1.0

#194

Post by dnautics »

conjectures wrote: post #107

Julia is a great language and was really useful in my PhD. The #1 requirement I have is the ability to make binaries for some program. You can compile a C program and get a binary. There's no practical equivalent for Julia at the moment and I think this limits its production potential.

these days you can just put it into a container, though! If docker is a problem bc no ability to install root daemons, I can attest that julia works great with singularity ("with one small problem - you can't trivially have both in-container and in-home-directory libraries") and I have deployed to supercomputers for real research purposes, using it.

Re: Julia 1.0

#195

Post by ur-whale »

DandyDev wrote: post #173
ur-whale wrote: post #157

Earlier quoted context omitted.

History has shown the exact opposite.

Examples?

You just have to look at the most popular programming languages on 2018.

They're all 0-based (with maybe R as an exception, but R is a niche language. If that's what Julia aims to remain -- their loss).

The folks who designed these languages knew how not to alienate their future market.

Re: Julia 1.0

#196

Post by ur-whale »

mikec3010 wrote: post #191
ur-whale wrote: post #169

Earlier quoted context omitted.

>Humans think from 1...N I think you're assuming everyone thinks the way you do. I assure you that's not the case. There are legions of people (and not just programmers, math folks do it too) who don't "think from 1 to N"

True, after they learn a programming language. I meant when we're children and taught to count, we don't start at 0.

Again assuming: that may be true in the place where you were educated.

Re: Julia 1.0

#197

Post by [deleted] »

3JPLW wrote: post #167
athenot wrote: post #147

As an outsider, I'd like to see somewhere near the home page a few short snippets of code to get a feel for Julia and hopefully show the kind of uses for which it is a natural choice. Nim's home page¹ shows a piece of sample code right at the top. Perl6's page² has a few tabs quickly showing some patterns it's good at. Golang³ has a dynamic interpreter prepopulated with a Hello World. Julia's home page shows a nice f…

Definitely agree. We just revamped the website and I'd love to see some domain-specific examples of Julia code in the multi-tab "ecosystem" section. I think it'd make a great addition. :)

[deleted]

Re: Julia 1.0

#198

Post by ur-whale »

athenot wrote: post #147

As an outsider, I'd like to see somewhere near the home page a few short snippets of code to get a feel for Julia and hopefully show the kind of uses for which it is a natural choice. Nim's home page¹ shows a piece of sample code right at the top. Perl6's page² has a few tabs quickly showing some patterns it's good at. Golang³ has a dynamic interpreter prepopulated with a Hello World. Julia's home page shows a nice f…

Agreed. Even better if they had code snippets that highlights what makes the language cool (instead of the usual "Hello World").

Re: Julia 1.0

#199

Post by dnautics »

jabl wrote: post #192
askaboutit wrote: post #139

I don’t do any scientific or numerical programming. So as someone interested in web development. Backend programming. Serverless. High performing code without being any more strenuous than Ruby or Python. Is Julia a good fit? I tried Crystal. It’s nice but the type system definitely makes it much harder to achieve things than Ruby.

In principle Julia is a general purpose language, although so far the ecosystem is heavily biased towards scientific computing. So while you might find the language itself pleasant, you'll probably find a lack of libraries. (personally I'm a fan of strong static type systems (e.g. haskell), and I think it is a shame the designers didn't go down that path. But hey, they did all the work and not me, so who am I to comp…

there's a reason why julia doesn't have a strong static type system.

In julia, you can write a custom type, and immediately have access to all of the builtin libraries.

For example, I wrote a drop-in replacement for floating points, and immediately had complex numbers, matrix math, gaussian elimination, fourier transform, etc... And could rapidly compare the numerical performance of that with fp.

For a more exotic example, I wrote a galois field GF256 type and was immediately able to reed-solomon encoding and decoding using the matrix multiplication and matrix solving libraries.

For an even more exotic example, I wrote a "lexical GF256" type (basically passing strings) and had the system generate optimized C code for reed-solomon decoding, using the builtin julia matrix solver without having to manually do matrix solving for every possible (n,k) reed-solomon system.

It was also relatively easy to write a verilog generator (~3 days of work), so you could pass bit-arrays representing wires, run you unit and property tests on the binary representation, then redispatch the function passing lexical types and get verilog code out the other end, then transpile the verilog to C using verilator, dynamically load the verilog into the julia runtime, and then unit and property tests on the transpiled verilog.

I'm sure it's possible to do this in haskell, but I imagine it would be harder.

Re: Julia 1.0

#200

Post by celrod »

marmaduke wrote: post #186
mlthoughts2018 wrote: post #84

Earlier quoted context omitted.

I am a machine learning library developer and I don’t share your feelings. For example the specific example you cite, I feel, should never be something scientists or engineers actively think about, only language implementers. Once you make that distinction, then whether you write it as a Cython module exposed in Python or you can use native language features to do it in Julia, nobody cares. It’s encapsulated away fro…

I see your comment grayed out, and I just want to chime in, as some who does a lot of numerical stuff (more than a decade, published stuff, support multiple lab research projects etc), I want to second this point of view. When it’s time to get real work done Python is more than good enough, and there’s plenty of strategies for acceleration where required. And when I want Julia’s promise of fast loops, I use Numba. If…

I think it is easier to write fast, complicated code in Julia than in C, C++, or Fortran. Not just the syntax, but because of great support for generic code by default and metaprogramming making it relatively simple to write code to generate the code you actually want for any given scenario. Interactive benchmarking and profiling are a boon too.

An example of the value of generic code is that forward mode AD is extremely easy, and almost always just works on whatever code you run it on.

Then, once that's done, multiple dispatch (and possible macros for a DSL) allows for a much cleaner user interface than Python offers for numeric code.

I have a lot more experience with R than Python, but seeing more of the scientist/mathematician/researchers side of things, I have to strongly disagree with the view that they should write slow code and contact a CS guy to write fast code in another language when they need it. Do you honestly think that's practical for grad student's projects? Recently, one of my friends wrote a simulation in R. Most of the work was done by hcubature -- a package written in C -- integrating a function written in R. Could just have easily been written in Python. That function was slow, and the simulation ran for days. Before an error caused it to crash, losing days of compute time. I -- a statistics grad student -- helped him rewrite it in Julia, and it finished in 2 hours.

That C/C++ code will still run slowly if they have to call your R/Python code is a problem. They also can't apply things like AD easily. A common solution, used by Stan for example, is to create a whole new modeling language and have users interface through that. Learning a new language -- albeit relatively simple/domain specific -- which they then cannot debug interactively, is another pain point. All this can be avoided by simply using Julia.

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