Posted by helloplanets 17 hours ago
The results surprised me:
F# 100 19.17s ±0.04s
C++ 96 19.92s ±0.13s
Rust 95 20.20s ±0.38s
Kotlin 89 21.51s ±0.04s
Scala 88 21.68s ±0.04s
Kotlin-native 81 23.69s ±0.11s
Scala-native 77 24.72s ±0.03s
Nim 69 27.92s ±0.04s
Julia 63 30.54s ±0.08s
Swift 52 36.86s ±0.03s
Ocaml 47 41.10s ±0.10s
Haskell 40 47.94s ±0.06s
Chez 39 49.46s ±0.04s
Lean 10 198.63s ±1.02s
I don't think these results are quite comparable because of slightly differing parallelism strategies; I'd expect the F# implementation of just spinning off threads to be more a little more performant than a Rayon parallel iterator, which presumably has some overhead. But that really just shows how hard it is to do a cross-language comparison; Rust and C++ can certainly be made faster than the F# code by carefully manipulating a ton of low-level OS concurrency primitives. This would arguably also be little misleading. Likewise C and Haskell have good C FFI; does that count? It's a tricky and highly qualitative analysis.
[1] FYI, one possible performance improvement with the Chez code is keeping the permutations in fxvectors and replace math operations with the fixnum-specific equivalent - this tells the compiler/interpreter that the data are guaranteed to be machine integers rather than bigints, so they aren't boxed/unboxed. I am not sure without running it myself, but there seems to be avoidable allocations in the Chez implementation. https://cisco.github.io/ChezScheme/csug/objects.html#./objec...
[1]: https://www.cambridge.org/ir/universitypress/subjects/comput...
Ruby is object oriented from the ground up. Everything (and I do mean everything) is an object, and method call is conceived as passing messages to objects.
While Ruby is most often compared to Python (an Algol), they come from very different evolutionary routes, and have converged towards the same point in the ecosystem. I think of Ruby as a cuddly Alpaca compared to Python's spitting camel.
I love to point this out to OOP haters,
>>> type(42)
<class 'int'>
>>> dir(42)
['__abs__', '__add__', '__and__', '__bool__', '__ceil__', '__class__', '__delattr__', '__dir__', '__divmod__', '__doc__', '__eq__', '__float__', '__floor__', '__floordiv__', '__format__', '__ge__', '__getattribute__', '__getnewargs__', '__getstate__', '__gt__', '__hash__', '__index__', '__init__', '__init_subclass__', '__int__', '__invert__', '__le__', '__lshift__', '__lt__', '__mod__', '__mul__', '__ne__', '__neg__', '__new__', '__or__', '__pos__', '__pow__', '__radd__', '__rand__', '__rdivmod__', '__reduce__', '__reduce_ex__', '__repr__', '__rfloordiv__', '__rlshift__', '__rmod__', '__rmul__', '__ror__', '__round__', '__rpow__', '__rrshift__', '__rshift__', '__rsub__', '__rtruediv__', '__rxor__', '__setattr__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', '__truediv__', '__trunc__', '__xor__', 'as_integer_ratio', 'bit_count', 'bit_length', 'conjugate', 'denominator', 'from_bytes', 'imag', 'is_integer', 'numerator', 'real', 'to_bytes']Perhaps this is the counterfactual: I program in Python regularly, but don't program in an OOP style; I use dataclasses and enums as the basis, in a way similar to Rust, which by some definitions can't do OOP. So, if Rust can't do OOP (assumption) and I can write Python and Rust with equivalent structure (Assumption), does that mean Python isn't strictly OOP?
Rust handles basic OOP, but not all of the characteristics seen in C++ or Java:
I wonder if my formal university python training predated this change (~2010), or if the professors were themselves unaware of this.
That seems like a pretty lame gotcha--saying "Aha! The language you write in uses your hated paradigm under the hood" seems to invite the immediate response of "So? I don't use it."
That's a deep cut. :-)
For anyone reading this, O'Reilly was once legendary for their cover-art mascots.
- Agda, Idris, etc. are functional languages extended with complex types
- Isabelle, Lean, etc. are functional languages extended with complex types and unreadable interactive proofs
- Dafny etc. are imperative languages extended with theorems and hints
- ACL2 is a LISP with theorems and hints
Related, typeclasses are effectively logic programming on an otherwise functional/imperative language (like traits in Rust, mentioned in https://rustc-dev-guide.rust-lang.org/traits/chalk.html).
I think they are not. No amount of type level extensions can turn a regular functional language like Haskell into something suitable for theorem proving. Adding dependent types to Haskell, for example, doesn't suffice. To build a theorem prover you need to take away some capability (namely, the ability to do general recursion - the base language must be total and can't be Turing complete), not add new capabilities. In Haskell everything can be "undefined" which means that you can prove everything (even things that are supposed to be false).
There's some ways you can recover Turing completeness in theorem provers. You can use effects like in F* (non-termination can be an effect). You can separate terms that can be used in proofs (those must be total) from terms that can only be used in computations (those can be Turing complete), like in Lean. But still, you need the base terms to be total, your logic is done in the fragment that isn't Turing complete, everything else depends on it.
Yes haskell's `bottom` breaks soundness, but that doesn't mean you need to take away some capability from the language. You just need to extend the language with a totality checker for the new proof fragment.
Idris absolutely is a general-purpose functional language in the ML family. It is Haskell, but boosted with dependent types.
While reading TFA I thought the theorem stuff deserved its own category, but I guess it's a specialization within an ur-family (several), rather than its own family?
It definitely sounds like it deserves its own category of programming language, though. The same way Lojban has ancestry in many natural languages but is very much doing its own thing.
But I think that the theorem prover that excels most at regular code is actually Lean. The reason I think that is because Lean has a growing community, or at least is growing much faster than other similar languages, and for regular code you really need a healthy ecosystem of libraries and stuff.
Anyway here an article about Lean as a general purpose language https://kirancodes.me/posts/log-ocaml-to-lean.html
> You can separate terms that can be used in proofs (those must be total) from terms that can only be used in computations (those can be Turing complete), like in Lean
What I meant is that the part of Idris that lets people prove theorems is the non-total part
But, I think you are right. Haskell could go there by adding a keyword to mark total functions, rather than marking nontotal functions like Idris does. It's otherwise very similar languages
Also, basically every such language has escape hatches similar to unsafe in Rust to allow expressions that are not provably terminating.
They can then just be accepted as an axiom.
OTOH if you really want to emphasize "intended to express proofs" then surely Prolog has that covered, so Lean can be seen as half ML, half Prolog. From this view, the Curry-Howard correspondence is just an implementation detail about choosing a particular computational approach to logic.
plus up and coming (actual production-ready) languages that don't fit perfectly in the 7 categories: unison, darklang, temporal dataflow, DBSP
It may feel like a little bit of cheating mentioning the above ones, as most are parallel to the regular von Neumann machine setup, but was meaning for a while to do an article with 'all ways we know how to compute (beyond von Neumann)'.
Would be very glad to read this.
In the meantime, I reproduce a part of an article by Steve Yegge:
---
What Computers Really Are
Another realization I had while reading the book is that just about every course I took in my CS degree was either invented by Johnny von Neumann, or it's building on his work in mostly unintelligent ways.
Where to start? Before von Neumann, the only electronic computing devices were calculators. He invented the modern computer, effectively simulating a Universal Turing Machine because he felt a sequential device would be cheaper and faster to manufacture than a parallel one. I'd say at least 80% of what we learned in our undergrad machine-architecture course was straight out of his first report on designing a programmable computer. It really hasn't changed much.
He created a sequential-instruction device with a fast calculation unit but limited memory and slow data transfer (known as the infamous "von Neumann bottleneck", as if he's somehow responsible for everyone else being too stupid in the past 60 years to come up with something better. In fact, Johnny was well on his way to coming up with a working parallel computer based on neuron-like cellular automata; he probably would have had one in production by 1965 if he hadn't tragically died of cancer in 1957, at age 54.)
Von Neumann knew well the limitations of his sequential computer, but needed to solve real problems with it, so he invented everything you'd need to do so: encoding machine instructions as numbers, fixed-point arithmetic, conditional branching, iteration and program flow control, subroutines, debugging and error checking (both hardware and software), algorithms for converting binary to decimal and back, and mathematical and logical systems for modelling problems so they could be solved (or approximated) on his computing machine.
-Steve Yegge, Math Every Day
In uni we had to make a spreadsheet software.
I volunteered to do the formula parser, thinking it sounded like a fun challenge.
I was stumped for a week, until I realized I could rewrite the formulas into a form I knew how to parse. So it would rewrite 1+1 into ADD(1,1) and so on.
I also refused to learn regex, so the parsing code was "interesting" ;)
I recall a comment from a colleague. "Okay, Andy says it works. Don't touch it." XD
Guy from another group used regex and his solution was 20x shorter than mine.
Plus up and coming (not quite production-ready IMO, but used in production anyways): ChatGPT and the like.
Of course, it’s debatable whether they are programming languages, but why wouldn’t they be. They aren’t deterministic, but I don’t think that is a must for a programming language, and they are used to let humans tell computers what to do.
[0] The Art of the Propagator (mit url down for the moment)
Found a working link to the paper about propagators.
The Art of the Propagator, Alexey Radul and Gerald Jay Sussman. https://groups.csail.mit.edu/mac/users/gjs/6.945/readings/ar... (PDF)
https://www.t3x.org/amk/index.html
You can just get the code without buying the book, learn with Simply Scheme or any other book and apply the functions from the code, the solvers are really easy to understand.
We agree on Algol, Lisp, Forth, APL, and Prolog. For ground-breaking functional language, I have SASL (St Andrews Static Language), which (just) predates ML, and for object oriented language, I have Smalltalk (which predates Self).
I also include Fortran, COBOL, SNOBOL (string processing), and Prograph (visual dataflow), which were similarly ground-breaking in different ways.
The only languages I was familiar with before this were BASIC, Logo, and a bit of 6502 assembly, though I had only used the latter by hand-assembly and calling it from BASIC following an example in the Atari BASIC manual[1].
Also, it's hard for me to imagine how anyone could make a list of ground-breaking programming languages that doesn't include Fortran and COBOL (or FLOW-MATIC as the source of many of its innovations).
[1] https://archive.org/details/atari-basic-reference-manual/pag...
Also ML is seen as a child of Lisp.
For those that never seen it, there are some old videos (taken from VHS) on the language site, https://selflanguage.org/
It made learning Elixir years later much easier.
We also had a course that basically summed up to programming agents to play Unreal Tournament in a language called GOAL which was based on Prolog.
For years I've wanted to use Prolog but could not figure out how. I ended up making a spellcheck to allow LLM's to iterate over and fix the dismal Papiamentu they generate.
Even having the thinnest surface level understanding of the other ur-languages is so useful (and even more-so with assembly). I can't do anything useful with them, but it helps keep you from the "when all you have is a hammer, every problem looks like a nail" trap if you're at least aware of the existence of screwdrivers.
The Unreal Tournament was the coolest thing I've ever seen. I think they shut it down the year after mine. (Now they have boring old regular AI like everyone else!)
I haven't found a good use for Prolog, though I haven't put much effort into it. I admit I was much more impressed by GOAL though, and I didn't realize until recently that you can replicate the whole thing in a more "ordinary" language (and that this gives many benefits). D'oh!
At the same time, I don't agree with that it does not matter if one picks "Java, C#, C++, Python, or Ruby". If your goal is to do quick sort, then well, it does not.
If you want to use language for something (not only for its sake), then it makes a day and night difference. A person who wants to do 3D games and being shown Ruby or a person wanting to do exploratory data science and deep learning and being given Java are likely to get discouraged.
I think from a historical perspective, describing COBOL and Fortran as part of the ALGOL family is a stretch, but I suppose it’s a good reminder that all history is reductive.
Fortran is one of the reasons OpenCL lost to CUDA, and now even AMD and Intel have finally Fortran support on their own stacks, not Khronos based.
https://developer.nvidia.com/cuda-fortran
Whereas Cobol, even has cloud and microservices.
https://www.rocketsoftware.com/en-us/products/cobol/visual-c...
https://aws.amazon.com/mainframe/
Incredible how being monetary relevant keeps some languages going.
Also note how the beloved UNIX and C are from 1971 - 73, only about 10 younger than COBOL.
FWIW, I loved using CUDA-Fortran. I think the ease of use of array variables maps very well with the way CUDA kernels work. It feels much more natural than in C++.
I mean, programming languages do not live; and they do not "die", per se, either. Just the usage may go down towards 0.
COBOL would then be close to extinction. I think it only has a few niche places in the USA and perhaps a very few more areas, but I don't think it will survive for many more decades to come, whereas I think C or python will be around in, say, three decades still.
> family with horizontal gene transfer
Well, you refer here to biology; viruses are the most famous for horizontal gene transfer, transposons and plasmids too. But I don't think these terms apply to software that well. Code does not magically "transfer" and work, often you have to adjust to a particular architecture - that was one key reason why C became so dynamic. In biology you basically just have DNA, if we ignore RNA viruses (but they all need a cell for their own propagation) 4 states per slot in dsDNA (A, T, C, G; here I exclude RNA, but RNA is in many ways just like DNA, see reverse transcriptase, also found in viruses). So you don't have to translate much at all; some organisms use different codons (mitochondrial DNA has a few different codon tables) but by and large what works in organism A, works in organism B too, if you just look to, say, wish to create a protein. That's why "genetic engineering" is so simple, in principle: it just works if you put genes into different organisms (again, some details may be different but e. g. UUU would could for phenylalanine in most organisms; UUU is the mRNA variant of course, in dsDNA it would be TTT). Also, there is little to no "planning" when horizontal gene transfer happens, whereas porting requires thinking by a human. I don't feel that analogy works well at all.
Also 30 Sept 2021, 29 comments, <https://news.ycombinator.com/item?id=28704495>.