While it is not fast in a single-thread, it is still 5x-7x faster than Node.js today for programs that are allocate a lot. If all you want is to run a program faster, and doesn't mind a bit more energy, Bend could be useful for you today.
And that's comparing a first-version interpreter against a SOTA runtime deployed in all browsers around the world and optimized by all major companies over 20+ years. If that's not useful to you, that's useful to me, which is why I wanted to share so it can be useful to more people.
It is not in alpha, nor not ready. You can use it in production today, if you want to. It is just not fast. That is different. CPython is still 100x slower than C, and is widely deployed in practice.
Just a note: we are NOT 10x slower than Python. I think a lot of people got the wrong message from this thread. HVM is actually quite fast already. It is just that, on this specific program, Python was doing no allocations, while HVM was allocating a lot.
If you compare programs that do the same allocation, HVM already outperforms not just Python but even compiled languages like Haskell/GHC, due to using all cores. See the Bitonic Sort example. And that's extremely relevant, because real world programs in high-level languages allocate a lot!
I think I made a huge mistake of using a "sum" example on the GUIDE, since it is actually one of the few specific programs where HVM will do poorly today.
I really appreciate the feedback, but the claim is that the performance scales linearly with cores, and it does. Also that it runs on GPUs, and it does. Yet, asking what is its "floating point operations per second" is nonsense, because it is not doing floating point computations. It is doing interactions. Thus the "interactions per second" term, which I didn't invent, it is the term used on the domain's literature.
I truly want to help here, but that is like asking us to tell you how many gallops per second hour car does. It just makes no sense in context. If I did invent some conversion, I would be lying, and that would be much worse than using a non-familiar term. The way to compare across languages is to benchmark and report on time. Which is like "horsepower" in that sense, as it applies to both domains.
I really think I take criticism well... The problem is that people were criticizing us for not doing things that were literally done on the second paragraph. So at this point it didn't feel like productive criticism? That's like being criticized for being naked when you're full clothed. How do you even make sense of that...
I have no idea what you're trying to convey, but I'm Victor Taelin. Also very cool comment on that thread, hypothesizing on whether we'd be able to ever run it on GPUs. We did it! That is what we're announcing today.
We do use multi-level caching, and you can achieve 5x higher performance by using it correctly. FFI is already implemented, just not published, because we want to release it with graphics rendering, which I think will be really cool. Haskell/GHC uses a graph and trees too, and nobody would say it is not practical of useful. And while it is true that arrays are king, there are many SOTA algorithms that are implemented in Haskell (including compilers, type-checkers, solvers) because they do not map well to arrays at all.
The main reason ICs are not fast is that nobody ever has done low-level optimization work over it. All previous implementations were terribly inefficient. And my own work is too, because I spent all time so far trying to get it to run *correctly* on GPUs, which was very hard. As you said yourself, there aren't even loops yet. So, how can we solve that? By adding the damn loops! Or do you think there is some inherent limitation preventing us to do that? If you do, you'll be surprised.
HVM2 is finally a correct algorithm that scales. Now we'll optimize it for the actual low-level performance.
It is "only" 50x because a single GPU core is 100x weaker than a CPU core!
Within CUDA cores, it is actually a linear speedup! It does 2k MIPS with 1 CUDA core, and ~28000 MIPS with 16k CUDA cores. If we double the performance of single-core GPU evaluation, we almost double the performance with 16k cores!
1. I didn't accuse you of doing something wrong, just that your claim was wrong! It has been proven that Interaction Combinators are an optimal model of concurrent computation. I also pointed cases where it also achieves practical efficiency, over-performing GHC's highest optimization level.
2. The performance scaling claimed been indeed been achieved, and the code is open for anyone to replicate our results. The machines used are listed on the repository and paper. If you find any trouble replicating, please let me know!
3. We're not selling any product. Bend is Apache-licensed.
Dude we're running unrestricted recursion and closures on GPUs! If that's not cool to you, I apologize, but that mind-blowingly cool to me, and I wanted to share it, even though the codegen is still initial. Hell I was actually going to publish it with the interpreters only, but I still coded an initial compiler because I thought people would like to see where it could go :(
So use a metric that makes absolutely no sense on given domain, instead of one that is completely correct, sensible, accurate, stablished on the literature, and vastly superior in context? What even is a FLOPS in the context of Interaction Net evaluation? These things aren't even interchangeable.
Thanks, and I apologize if I got defensive, it is just that I put so much effort on being truthful, double-checking, putting disclaimers everywhere about every possible misinterpretation. Hell this is behind install instructions:
> our code gen is still on its infancy, and is nowhere as mature as SOTA compilers like GCC and GHC
Yet people still misinterpret. It is frustrating because I don't know what I could've done better
Right below install instructions, on Bend's README.md:
> But keep in mind our code gen is still on its infancy, and is nowhere as mature as SOTA compilers like GCC and GHC.
Second paragraph of Bend's GUIDE.md:
> While cool, Bend is far from perfect. In absolute terms it is still not so fast. Compared to SOTA compilers like GCC or GHC, our code gen is still embarrassingly bad, and there is a lot to improve. That said, it does what it promises: scaling horizontally with cores.
Limitations session on HVM2's paper:
> While HVM2 achieves near-linear speedup, its compiler is still extremely immature, and not nearly as fast as state-of-art alternatives like GCC of GHC. In single-thread CPU evaluation, HVM2, is still about 5x slower than GHC, and this number can grow to 100x on programs that involve loops and mutable arrays, since HVM2 doesn’t feature these yet.
You're wrong. The Haskell code is compiled to a loop, which we didn't optimize for yet. I've edited the README to use the Bitonic Sort instead, on which allocations are unavoidable. Past N=20, HVM2 performs 4x faster than GHC -O2.
And that's comparing a first-version interpreter against a SOTA runtime deployed in all browsers around the world and optimized by all major companies over 20+ years. If that's not useful to you, that's useful to me, which is why I wanted to share so it can be useful to more people.