In the end, HN comments fall prey to this truth and you see a handful of positive comments, with the majority being criticisms or "I wish this did X". No one person is to blame. Its just the culture of technologists today.
If you explain why, they either still don't understand, or don't agree.
If the first iPhone had been presented on HN/Reddit/Twitter, everyone would criticize the lack of physical keyboard.
Difficult to understand is often threatening.
Criticism is a popular response to threat and is the form of reply that requires the least understanding.
P.s. it seems rather likely the op is Victor Taelin, they mostly submit his tweets and gists.
Who are you rooting for, exactly, newcomer?
P.p.s. Victor Taelin just happens to be the most recent committer on this submission, imagine that.
I just wrote a simple loop in C++ to sum up 0 to 2^30. With a single thread without any optimizations it runs in 1.7s on my laptop -- matching Bend's performance on an RTX 4090! With -O3 it vectorizes the loop to run in less than 80ms.
#include <iostream>
int main() {
int sum = 0;
for (int i = 0; i < 1024*1024*1024; i++) {
sum += i;
}
std::cout << sum << "\n";
return 0;
}Bend's codegen is still abysmal, but these are all low-hanging fruits. Most of the work went into making the parallel evaluator correct (which is extremely hard!). I know that sounds "trust me", but the single-thread performance will get much better once we start compiling procedures, generating loops, etc. It just hasn't been done.
(I wonder if I should have waited a little bit more before actually posting it)
No. You built something that’s pretty cool. It’s not done yet, but you’ve accomplished a lot! I’m glad you posted it. Thank you. Ignore the noise and keep cooking!
I've never understood the fascination with tail calls and recursion among computer science folks. Just write a loop, it's what it optimises to anyway.
Good point about the signed integer overflow, though!
def sum(depth, x):
if depth == 0:
return x
else:
fst = sum(depth-1, x*2+0) # adds the fst half
snd = sum(depth-1, x*2+1) # adds the snd half
return fst + snd
print(sum(30, 0))
CPU, Apple M3 Max, 1 thread: 3.5 minutes
CPU, Apple M3 Max, 16 threads: 10.26 seconds
GPU, NVIDIA RTX 4090, 32k threads: 1.88 seconds
I appreciate this is early days, but it's hard to get excited about what seems to be incredibly slow performance from a really simple example you give. If the simple stuff is slow, what does that mean for the complicated stuff?
If I get a chance tonight, I'll re-run it with `-s` argument, see if I get anything helpful.
For the `sum` example, Bend has a huge disadvantage, because it is allocating 2 IC nodes for each numeric operation, while Python is not. This is obviously terribly inefficient. We'll avoid that soon (just like HVM1 did it). It just wasn't implemented in HVM2 yet.
Note most of the work behind Bend went into making the parallel evaluator correct. Running closures and unrestricted recursion on GPUs is extremely hard. We've just finished that part, so, there was basically 0 effort into micro-optimizations. HVM2's codegen is still abysmal. (And I was very clear about it on the docs!)
That said, please try comparing the Bitonic Sort example, where both are doing the same amount of allocations. I think it will give a much fairer idea of how Bend will perform in practice. HVM1 used to be 3x slower than GHC in a single core, which isn't bad. HVM2 should get to that point not far in the future.
Now, I totally acknowledge these "this is still bad but we promise it will get better!!" can be underwhelming, and I understand if you don't believe on my words. But I actually believe that, with the foundation set, these micro optimizations will be the easiest part, and performance will skyrocket from here. In any case, we'll keep working on making it better, and reporting the progress as milestones are reached.
While that's true, Python would be using big integers (PyLongObject) for most of the computations, meaning every number gets allocated on the heap.
If we use a Python implementation that would avoid this, like PyPy or Cython, the results change significantly:
% cat sum.py
def sum(depth, x):
if depth == 0:
return x
else:
fst = sum(depth-1, x*2+0) # adds the fst half
snd = sum(depth-1, x*2+1) # adds the snd half
return fst + snd
if __name__ == '__main__':
print(sum(30, 0))
% time pypy sum.py
576460751766552576
pypy sum.py 4.26s user 0.06s system 96% cpu 4.464 total
[Edit: just noticed the previous comment had already mentioned pypy]
> I'm aware it is 2x slower on non-Apple CPUs.
Do you know why? As far as I can tell, HVM has no aarch64/Apple-specific code. Could it be because Apple Silicon has wider decode blocks?
> can be underwhelming, and I understand if you don't believe on my words
I don't think anyone wants to rain on your parade, but extraordinary claims require extraordinary evidence.
The work you've done in Bend and HVM sounds impressive, but I feel the benchmarks need more evaluation/scrutiny. Since your main competitor would be Mojo and not Python, comparisons to Mojo would be nice as well.
that sum example, transpiled to C and compiled takes 1m12.704s, so it looks like it's just the VM case that is having serious issues of some description!
Clearly this language is positioned at using the gpu for compute-heavy applications and it's still in its early stages. Recursion is not the target application and should not be a relevant benchmark.
A Pythonic implementation would use loops and mutation.
"Hey, I'm accessing the 0th element here, just want to make that clear"
Without the +0, that statement looks disconnected from the +1 even though conceptually its the same.
Say somebody adds some special marker/tombstone/whatever into element 0 and now all those additions need to be bumped up by one. Someone else may go and see the +1, +2, +3 and just change them to +2, +3 +4, etc while completely missing the lone variable by itself as its visually dissimilar.
Ive usually seen it used in longer lists of statements. It also keeps everything lined up formatting wise.
I was actually looking forward to seeing someone from Bend to make a comment like this
I would say that the play on words that gives the language its name ("Bend") doesn't really make sense...
https://github.com/HigherOrderCO/bend/blob/main/GUIDE.md
> Bending is the opposite of folding. Whatever fold consumes, bend creates.
But in everyday language bending is not the opposite of folding, they are more or less the same thing. Why not "unfold", which also has a connotation of "the process of happening" as well as merely the opposite of folding?
I have a question about the example code and output for bending:
type Tree:
Node { ~lft, ~rgt }
Leaf { val }
def main():
bend x = 0:
when x < 3:
tree = Tree/Node { lft: fork(x + 1), rgt: fork(x + 1) }
else:
tree = Tree/Leaf { val: 7 }
return tree
tree = fork(0)
tree = ![fork(1), fork(1)]
tree = ![![fork(2),fork(2)], ![fork(2),fork(2)]]
tree = ![![![fork(3),fork(3)], ![fork(3),fork(3)]], ![![fork(3),fork(3)], ![fork(3),fork(3)]]]
tree = ![![![7,7], ![7,7]], ![![7,7], ![7,7]]]
But contrary to this, I think explicitly separating function declaration and function calling, in the following kind of syntax, would make it much clearer and less complected where the initial condition `tree = fork(0)` comes from. In the original example it came from `bend x = 0`, but here the function declaration is separate and the call more explicit: so it more obviously comes from `createTree(0)`:
type Tree
Branch { left, right }
Leaf { value }
def main():
createTree(x):
x < 3 ?
Tree.Branch { left: createTree(x+1), right: createTree(x+1) }
Tree.Leaf { value: 7 }
createTree(0)
I wonder if bend has to do with how we manipulate the computation's interaction graph while evaluating a bend. There might be some bending of wires!
Re: code example
In the code example, x=0 is the seed value. tree = fork(0) must mean "fork off to evaluate the bend at the seed value". In that first fork, we fork twice with the value x=1, to get the left and right subtrees of the top level node. We then fork four instances of x=2, eight instances of x = 3, and finally get our balanced binary tree with eight 7s.
Note this is guesswork. I don't know what the ![a, b] syntax means, and I haven't read much of the guide.
Appendix: Notes on Fold Vs Bend
I wrote these for an earlier draft while reminding myself about these operations. I include them more for my benefit, and in case they help you or the audience.
Fold and bend are categorical duals, aka catamorphisms and anamorphisms. One takes a monadic value and reduces it into an ordinary value. The other takes an ordinary value and expands it into a comonadic value.
Fold starts with a value in an inductive data type, and then replaces its constructors with a function. For example it takes a list (1:2):3, and replaces the constructor : with the function `+`, to get (1+2)+3 = 6
Bend starts with a seed value and a function taking values into constructor expressions for a conductive data type. It then grows the seed into a potentially infinite AST. For example the seed value 1 and the function f(x:xs) = (x+1) : (x:xs) gives us the infinite lazy list [1, 2, 3, ...]
Seems like probably not, there doesn't seem to be enough information in the 'argument' to this 'function' to do anything useful without the implicit context of the bend construct.
For that reason I think I'd prefer it if fork was a keyword (like 'bend' and 'when') rather than a 'function', just at the surface syntax level to give a clue it is something special.
I guess fork is a kind of 'magic' function that represents the body of the bend. It's a bit like a 'self' or 'this'.
At the moment this syntax is in a weird half-way point ...the underlying concept is necessarily functional but it's trying to look kind of like an imperative for-loop still.
I wonder if we couldn't just explicitly create a 'bendable' recursive function that can be 'bent' by calling it. But I guess it's like this because it needs to be tightly constrained by the 'when' and 'else' forms.
TBH the more I look at this example the more confusing it is. The other part I wonder about is the assigning of new values to tree var... can I set other local vars from outside the bend scope? I don't think so, I guess it'd be a syntax error if the var names assigned in the 'when' and 'else' clauses didn't match?
Again it's sort of overloading an imperative-looking syntax to implicitly do the 'return' from the implicit recursive function.
Later on there is this example:
def render(depth, shader):
bend d = 0, i = 0:
when d < depth:
color = (fork(d+1, i*2+0), fork(d+1, i*2+1))
else:
width = depth / 2
color = shader(i % width, i / width)
return color
That's an odd mix in a language which otherwise has explicit returns like Python.
If so I wonder if a syntax something like this might be clearer:
def render(depth, shader):
bend color with d = 0, i = 0:
when d < depth:
yield (fork(d+1, i*2+0), fork(d+1, i*2+1))
else:
width = depth / 2
return shader(i % width, i / width)
return color
The program above will initialize a state (`x = 0`), and then, for as long as `x < 3`,
it will "fork" that state in two, creating a `Tree/Node`, and continuing with `x + 1`.
When `x >= 3`, it will halt and return a `Tree/Leaf` with `7`.
When all is done, the result will be assigned to the `tree` variable:all the other "fork"s in the output are produced explicitly by:
Tree/Node { lft: fork(x + 1), rgt: fork(x + 1) }> That's a 111x speedup by doing nothing. No thread spawning, no explicit management of locks, mutexes. We just asked bend to run our program on RTX, and it did. Simple as that. Note that, for now, Bend only supports 24-bit machine ints (u24), thus, results are always mod 2^24.
Ahh, not even 32bit? Hmm, that seems pretty arbitrary for someone not accustomed to gpu's and wanting to solve some problems requiring 64 bits (gravitational simulation of solar system at millimeter resolution could use ~58bit ints for position).
12x for 16x threads
51x for 16.000x threads
Can someone point me to a website where it explains that this is the "ideal speedup"? Is there a formula?
1. Some potentially useful perspectives:
* Weak scaling vs strong scaling: https://www.kth.se/blogs/pdc/2018/11/scalability-strong-and-... ?
* ... Strong scaling, especially comparing to a modern sequential baseline, seems to be where folks are noting the author still has some work to do wrt getting to ideal speedups for what performance people care about
* There are parallel models of computation like PRAM for describing asymptomatically idealized speedups of trickier aspects of parallel code like heap usage . Bend currently seems to do a lot of stack allocations that someone writing in most parallel systems wouldn't, and the asymptomatic slowdowns would show up in these models, eg, asymptotically many unnecessary heap/stack data movements. There are a lot of these models, which are useful for being precise when making ideal speedups claims. NUMA, network topology, etc. ("Assume everyone is a sphere and...")
2. The comparisons I'd really like to see are:
* cudf, heavy.ai: how does it compare to high-level python dataframe and SQL that already run in GPUs? How is perf, and what programs do you want people to be able to write and they cannot?
* Halide and other more general purpose languages that compile to GPUs that seem closer to where Bend is going
FWIW, it's totally fine to compare to other languages.
Instead of showing it is beating everywhere, or saying ideal speedups and no comparisons, show where it is strong vs weak compared to others and diff tasks, especially progression across different releases (bend1 vs 2 vs ...), and let folks decide. There is some subset of tasks you already care about, so separate those out and show the quality you get in them, so people know what it looks like when you care + happy path. The rest becomes 'if you need these others, stay clear for now, check in again later as we know and are tracking.' Being clear that wall clock time can be slow and performance per watt can be wasteful is OK, you are looking for early adopters, not OpenAI core engineers.
A GPU core (shading unit) is 100x weaker than a CPU core, thus the difference.
ON the GPU, HVM's performance scales almost 16000x with 16000x cores. Thus the "near ideal speedup".
Not everyone knows how GPUs work, so we should have been more clear about that!
210 seconds (3.5 minutes) to 10.5 seconds is a 20x speedup, which isn't really expected.
I will give bend a shot on some radar signal processing algorithms.
I thought you was talking about the DEMO example, which ran ~30% slower than expected. Instead, you were talking about the README, which was actually incorrect. I noticed the error and edited it. I explained the issue in another comment.
Short example: https://github.com/m4rs-mt/ILGPU/blob/master/Samples/SimpleM...
Supports even advanced bits like inline PTX assembly: https://github.com/m4rs-mt/ILGPU/blob/master/Samples/InlineP...
Also NVidia has sponsored variants of Haskell, .NET, Java, Julia on CUDA, have a Python JIT and are collaborating with Mojo folks.
Made by the designer for Ada since 1995, Tucker Taft. Some of the parallel features of ParaSail made it into Ada 2022.
Here are some notes from my first impressions and after skimming through the paper. And yes, I am aware that this is very very early software.
1. Bend looks like an extremely limited DSL. No FFI. No way of interacting with raw buffers. Weird 24bit floating point format.
2. There's a reason why ICs are not relevant: performance is and will always be terrible. There is no other way to put it, graph traversal simply doesn't map well on hardware.
3. The premise of optimal reduction is valid. However, you still need to write the kernels in a way that can be parallelized (ie. no data dependencies, use of recursion).
4. There are no serious examples that directly compare Bend/HVM code with it's equivalent OMP/CUDA program. How am I suppose to evaluate the reduction in implementation complexity and what to expect on performance. So many claims, so little actual comparisons.
5. In the real world of high performance parallel computing, tree-like structures are non-existent. Arrays are king. And that's because of the physical nature of how memory works on a hardware level. And do you know what works best on mutable contiguous memory buffers ? Loops. We'll see when HVM will implement this.
In the end, what we currently have is half-baked language that is (almost) fully isolated from external data, extremely slow, a massive abstraction on the underlying hardware (unutilised features: multilevel caches, tensor cores, simd, atomics).
I apologize if this comes out as harsh, I still find the technical implementation and the theoretical background to be very interesting. I'm simply not (yet) convinced of its usefulness in the real world.
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.
This, I think, is the key thing people are missing.
Maybe your low level performance will never be as good as hoped, but for this sort of task, "the parallelisation part works and produces correct results" might not be sufficient but is absolutely necessary, and any optimisation work done before that has such a high probability of having to be thrown away that under similar circumstances I wouldn't bother in advance either.
Since then, I was able to make some experiments with multithreading (thanks Rust) and getting very creative with shaders (thanks Shadertoy). But a general parallel language on the GPU? I'm super excited to play with this!
If you liked 210, you might also like https://futhark-lang.org/ which is an ML-family language that compiles to GPU with good performance.
https://github.com/HigherOrderCO/bend/blob/main/GUIDE.md#par...
Running the equivalent C code takes ~2.3 seconds on my machine. Same order of magnitude as bend on the beefy GPU.
I ask because a while back I was messing around with compiling interaction nets to C after reducing as much of the program as possible (without reducing the inputs), as a form of whole program optimization. Wouldn't be too much harder to target a shader language.
Edit: Oh I see...
> This repository provides a low-level IR language for specifying the HVM2 nets, and a compiler from that language to C and CUDA HVM
Will have to look at the code then!
https://github.com/HigherOrderCO/HVM
Edit: Wait nvm, it looks like the HVM2 cuda runtime is an interpreter, that traverses an in-memory graph and applies reductions.
https://github.com/HigherOrderCO/HVM/blob/5de3e7ed8f1fcee6f2...
I was talking about traversing an interaction net to recover a lambda-calculus-like term, which can be lowered to C a la lisp in small pieces with minimal runtime overhead.
Honestly the motivation is, you are unlikely to outperform a hand-written GPU kernel for like ML workloads using Bend. In theory, HVM could act as glue, stitching together and parallelizing the dispatch order of compute kernels, but you need a good FFI to do that. Interaction nets are hard to translate across FFI boundaries. But, if you compile nets to C, keeping track of FFI compute kernel nodes embedded in the interaction network, you can recover a sensible FFI with no translation overhead.
The other option is implementing HVM in hardware, which I've been messing around with on a spare FPGA.
Edit: nvm, I read through the rest of the codebase. I see that HVM compiles the inet to a large static term and then links against the runtime.
https://github.com/HigherOrderCO/HVM/blob/5de3e7ed8f1fcee6f2...
Will have to play around with this and look at the generated assembly, see how much of the runtime a modern c/cu compiler can inline.
Btw, nice code, very compact and clean, well-organized easy to read. Rooting for you!
As a whole, the speedups claimed are not actually that great. Going from 1 core to 16k cores increases performance by 50x. That's not actually very good.
Like, I really truly love what the author has contributed to functional languages and Interaction Nets. He has good ideas, but while it's cool that this can be done, things like LLMs require very practical tuning.
Finally, the author has a history of making fantastical claims. Again, it's true there is a speedup, but in my view, this is like making an extremely slow language and then optimizing it and then announcing that you've figure out how to improve your language's performance by 50x. While true, it neglects the fact it was very slow to begin with.
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!
Back then everyone thought it was doomed to get stuck at local minima, but it turns out that has a lower probability of happening if the search space has enough dimensions. It works well enough to make the sand talk back to us and now that particular design has sucked all the air out of the room.
Nobody has tried EC at anywhere near the scale of GPTs/LLMs because that amount of compute is expensive and at this point we know those will at least work.
I still think EC is fascinating and would love to play with it some more at some point, maybe trying it combined with back propagation in novel ways. Compute only gets cheaper.
> That's a 111x speedup by doing nothing. No thread spawning, no explicit management of locks, mutexes. We just asked bend to run our program on RTX, and it did. Simple as that. Note that, for now, Bend only supports 24-bit machine ints (u24), thus, results are always mod 2^24.
Ahh, not even 32bit? Hmm, that seems pretty arbitrary for someone not accustomed to gpu's and wanting to solve some problems requiring 64 bits (gravitational simulation of solar system at millimeter resolution could use ~58bit ints for position).
> CPU, Apple M3 Max, 16 threads: 10.26 seconds
Surprised to see a more than linear speedup in CPU threads. What’s going on here?
- surely for `3 x 3 = 9`, there is some concept of primitive operations?
- I get that replacement of patterns in a graph can be done in parallel, but (a) identifying when a rewrite rule should apply and (b) communicating the state of the updated graph to worker threads and (c) organizing worker threads to agree on which does each task all take some effort. When is this more work than the original computation (as in the 3x3 example)?
Before you reply "these are things we can address in the future": that doesn't matter. Everyone can address everything in the future. They are currently hard technical barriers to it's use, with no way of knowing the level of effort that will require or the knock-on effects, especially since some of these issues have been "we can fix that later" for ten years.
I also highly recommend changing your benchmark numbers from "interactions per second" to a standard measurement like FLOPS. No one else on earth knows how many of those interactions are pure overhead from your evaluation semantics, and not doing useful work. They come across as attempting to wow an audience with high numbers and not communicating an apples to apples comparison with other languages.
People want to be able to ground your work—which you are claiming is the “parallel future of computation”—in something familiar. Insulting them and telling them their concerns are irrelevant just isn’t going to work.
I would urge you to think about what a standard comparison versus Haskell would look like. Presumably it would be something that dealt with a large state space, but also top down computation (something you couldn’t easily do with matrices). Big examples might include simply taking a giant Haskell benchmark (given the setting of inets it seems like a natural fit) that is implemented in a fairly optimal way—-both algorithmically and also wrt performance—-and compare directly on large inputs.
Sorry to trash on you here, not trying to come across as insulting, but I agree that “reductions per second” is meaningless without a nuanced understanding of the potentially massive encoding blowup that compilation introduces.
We want to believe, but the claims here are big
module Main where
sum' :: Int -> Int -> Int
sum' 0 x = x
sum' depth x = sum' (depth - 1) ((x \* 2) + 0) + sum' (depth - 1) ((x \* 2) + 1)
main = print $ sum' 30 0
If you're going to promise amazing performance from a high level language, I'd want to see a comparison against JAX.
It's an improvement over traditional interaction nets, sure! But interaction nets have always been a failure performance-wise. Interaction nets are PL equivalent of genetic algorithms in ML, they sound like a cool idea and have a nice story, but then they always seem to be a dead end.
Interaction nets optimize parallelism at the cost of everything else. Including single-threaded performance. You're just warming up the planet by wasting massive amounts of parallel GPU cores to do what a single CPU core could do more easily. They're just the wrong answer to this problem.
This is exactly what a scammer would say.
I guess that's the point here. Scam people who don't know anything about parallel computing by never comparing against any other method?
We still see this today in how languages go out of their way to implement higher order method libraries (map/reduce/filter) but then under the hood there is no multithreading, they just expect the developer to annotate their loops to be parallel because the languages aren't formal enough to know about side effects in the innermost logic, and don't support immutability or performant pass-by-value semantics with copy-on-write anyway. So we end up with handwavy languages like Rust that put all of that mental load onto the developer for basically no gain, they just save memory by performing computation in-place imperatively.
I also like how Bend sidesteps the nonexistence of highly scaled symmetric multiprocessing CPUs by supporting GPUs. It makes the argument moot that GPUs can't be stopped because they're too big to fail. Julia is the only other language I've seen that tries this. I wish Clojure did, although it's been a long time since I followed it so maybe it has some parallelism?
I would have dearly loved to work on something like Bend, had someone solved the funding issue. Nobody wants to pay for pure research, and nobody sees the need for languages that do what everyone else is doing except easier. We have Kickstarter for widgets and Patreon for influencers, but makers have to bootstrap themselves or learn everything about finance or live in the right city or have a large network to hopefully meet an angel investor or work in academia and lose all rights to what they invent while spending the majority of their time hustling for grants anyway. So it just never happens and we're stuck with the same old busted techniques. Like how Hollywood only has money for sequels and reboots or the recording industry only has money for canned corporate music and hits from already famous artists and yet another cover that yanks the original better song off the radio.
A quarter of a century can go by in the blink of an eye if you get suckered into building other people's dreams as a people-pleaser. Be careful what you work on.
well said! i find myself reflecting the same sentiment when away from the computer (and i've avoided the people-pleaser thing, but what you said resonates as i watch the world)
Tried to see what the language is like beyond hello world and found the guide[1]. It looks like a Python and quacks like a Haskell? For instance, variables are immutable, and tree-like divide and conquer data structures/algorithms are promoted for getting good results. That makes sense I guess! I’m not surprised to see a functional core, but I’m surprised to see the pythonic frontend, not that it matters much. I must say I highly doubt that it will make it much easier for Python devs to learn Bend though, although I don’t know if that’s the goal.
What are some challenges in programming with these kind of restrictions in practice? Also, is there good FFI options?
[1]: https://github.com/HigherOrderCO/bend/blob/main/GUIDE.md
Vendor support:
- https://www.intel.com/content/www/us/en/developer/articles/g...
- https://rocm.blogs.amd.com/software-tools-optimization/hipst...
- https://docs.nvidia.com/hpc-sdk/archive/20.7/pdf/hpc207c++_p...
I know the docs say this will be fixed soon, but what is the main reason for restricting number types to 24 bits? I saw in the code that they are wrapper around the 32-bit system number types, so what prevents Bend from changing them to U32(u32) right now?
Short answer: GPU
Long answer: CUDA
Seriously though. Implementing a full high-level lang in parallel is HARD, so, to simplify it greatly, we made IC nodes 64-bit, which allows us to use native 64-bit atomic operations in many parts of the implementation. Since each 64-bit node has 2 ports, that gives us 32 bits per port. And since we use 3 bits for the tag, that leaves us with 29 bit payloads. We used that space to easily implement unboxed numbers (f24, u24, i24).
That said, we will have (boxed) 64-bit numbers soon! With this foundation in place, adding them is a matter of coding. I just want to have some time to let people use the limited version, find bugs, etc., before I add more stuff.
You could get some sense of the parallelism by using `/usr/bin/time` and dividing the wall time with the user time.
You could look at the Task Manager / Activity Monitor / htop and see if it's using 800% CPU or whatever.
You could use psrecord (https://pypi.org/project/psrecord/) to get a relatively finegrained CPU+mem usage graph across the duration of the program.
But it would probably still be best to record some sort of stats in the Bend/HVM itself, enabled via a CLI flag. Reductions per ms, sampled across the program duration, or something like that.
I'd be interested in anybody's ideas of what a good metric would be here!
EDIT: CLI flag, not CPU flag
Every time I try to write shaders, or even peek through my fingers at CUDA C(++) code, I recoil in disbelief that we don't have high level programming yet on the GPU. I can't wait until we do. The more great projects attacking it the better in my book.
tested on: CPU - Apple M3 Max, GPU - NVIDIA RTX 4090
But how? I thought eGPUs don’t work on apple silicon and the pci-e having Mac Pro is still M2 based, no?
I’d also love to find an example of writing a small interpreter in Bend - which runs on the GPU.
I'm excited to see how this project progresses.
function sum(depth, x)
if depth == 0
return x
else
fst = sum(depth-1, x*2+0)
snd = sum(depth-1, x*2+1)
end
return fst + snd
end
https://docs.google.com/spreadsheets/d/1V_DZPpc7_BP3bmOR8Ees...
It's magical how the GPU version is basically flat (although with a high runtime init cost).
But the demo gif is probably the best I have seen in a Github readme. I watched it till the end. It was instantly engaging. I wanted to see the whole story unfold.
It seems like this is actually an elegant typed functional language but the Python syntax looks ugly and verbose and like it's trying to hide that compared to something more ML/F# or Haskell inspired.
I'll try and get past that though as it does look like there's something pretty interesting here.
On the CPU, there's typically a threshold where dividing and coordinating the parallel work takes more time than simply doing the work on a single thread. Thus you can make the overall runtime much faster by not dividing the work all the way, but rather stop at that optimal threshold and then just loop over the remaining work in the worker threads.
How does this work on the GPU using Bend? Been too long since I did any GPU programming.
It's magical how the GPU version is basically flat (although with a high runtime init cost)
I think this is a much more practically approach and i hope this will give some inspiration to this possibility.
As I understand, it's a lot of work because there's no common way to target these different GPUs. Is this correctly understood?
I’ve been watching HVM for a while and think it’s extremely cool.
My intuition is that this will eventually be a really big deal.
Question: Does this take into account memory bandwidth and caches between cores? Because getting them wrong can easily make parallel programs slower than sequential ones.
Now I just need a Common Lisp implemented using it!
I am really enjoying this implementation :)
Python-like + High-performance.
And, Different from Mojo, its Fully Open-Source.
Eeek.
Okay, I'll have what you're having.