legion VS bun

I work on a distributed runtime system for heterogeneous supercomputers [1].

As an example of the sort of bug we regularly deal with, I am at this exact moment tracking down a freeze that occurs on 8,192 nodes of a supercomputer [2]. That means I'm using about 64,000 GPUs and about half a million CPU cores. The smallest node count I've seen my issue is 2,048 nodes and at that scale it only happens about 10% of the time.

We've been debating internally whether Antithesis could help us or not. On the one hand, the fuzzing to explore the state space, and deterministic reproduction, are exactly what we want. On the other hand, we believe our state space is much larger than what you see in a typical distributed database. (And not just because of the sheer scale of things, but even on a single node we have state machines with order hundreds to thousands of states in them.) Based on the post here and the "scenario" count explored in CouchDB, I'm not convinced you'd be able to handle us. :-)

I'd be curious what you think. Happy to discuss here, or contact info in profile.

[1]: https://legion.stanford.edu/

[2]: https://www.olcf.ornl.gov/frontier/

Parallelism in CS is a bit like security in CS. People know it matters in the abstract senses but you really only get into it if you look for the training specifically. We're getting better at both over time: just as more languages/libraries/etc. are secure by default, more now are parallel by default. There's a ways to go, but I'm glad we didn't do this prematurely, because the technology has improved a lot in the last decade. Look for example at what we can do (safely!) with Rayon in Rust vs (unsafely!) with OpenMP in C++.

And there are things even further afield like what I work on [1][2][3].

[1]: https://legion.stanford.edu/

[2]: https://regent-lang.org/

[3]: https://github.com/nv-legate/cunumeric

Chapel has at least several full-time developers at Cray/HPE and (I think) the US national labs, and has had some for almost two decades. That's much more than $100k.

Chapel is also just one of many other projects broadly interested in developing new programming languages for "high performance" programming. Out of that large field, Chapel is not especially related to the specific ideas or design goals of Mojo. Much more related are things like Codon (https://exaloop.io), and the metaprogramming models in Terra (https://terralang.org), Nim (https://nim-lang.org), and Zig (https://ziglang.org).

But Chapel is great! It has a lot of good ideas, especially for distributed-memory programming, which is its historical focus. It is more related to Legion (https://legion.stanford.edu, https://regent-lang.org), parallel & distributed Fortran, ZPL, etc.

I should also note that there is Pygion if you want to use Python. Not a lot of great reference material right now, but there's the paper:

https://legion.stanford.edu/pdfs/pygion2019.pdf

And code samples:

https://github.com/StanfordLegion/legion/tree/stable/binding...

We use PyPy for performing verification of our software stack [1], and also for profiling tools [2]. The verification tool is basically a complete reimplementation of our main product, and therefore encodes a massive amount of business logic (and therefore difficult to impossible to rewrite in another language). As with other users, we found the switch to PyPy was seamless and provides us with something like a 2.5x speedup out of the box, with (I think) higher speedups in some specific cases.

We eventually rewrote the profiler tool in Rust for additional speedups, but as mentioned for the verification engine, it's probably too complicated to ever do that so we really appreciate drop-in tools like PyPy that can speed up our code.

[1]: https://github.com/StanfordLegion/legion/blob/master/tools/l...

[2]: https://github.com/StanfordLegion/legion/blob/master/tools/l...

Legion is also doing something like that: https://legion.stanford.edu/

If you really want to dig into it you can read up on the tutorials and/or papers from the Legion project: https://legion.stanford.edu/

But briefly, these task-based programs preserve sequential semantics. That means (whatever the system actually does when running your program), as long as you follow the rules, the parallelism should be invisible to the execution of the program.

Computer Science Research Dept., SLAC National Accelerator Laboratory | Research Scientist / Engineer | Menlo Park, CA or REMOTE, VISA | Full Time

We're a research group within SLAC, headed by Alex Aiken (https://theory.stanford.edu/~aiken/). We focus on fundamental CS research that has the potential to impact science, mainly in the areas of high-performance and distributed computing, programming languages, compilers, networks, operating systems, etc. One of our major projects is Legion, a forward-looking programming system for distributed computing (https://legion.stanford.edu/). Legion has been used to create new programming languages (https://regent-lang.org/), seamless distributed NumPy (https://developer.nvidia.com/cunumeric), and a drop-in replacement for Keras and PyTorch (https://flexflow.ai/), among many other things.

We are looking for strong scientists and engineers to join our group. For clarity (because these terms vary by industry/company), scientists mainly focus on producing research results (e.g., papers and research software) while engineers mainly focus on software development and deliverables (e.g., system or application implementation). For scientist positions please expect to provide a CV with relevant publications.

The official application links are below, but please feel free to contact me directly if you have questions. (My HN username @slac.stanford.edu)

Scientist (Computer Science):

https://erp-hprdext.erp.slac.stanford.edu/psp/hprdext/EMPLOY...

Engineer (Computer Science):

https://erp-hprdext.erp.slac.stanford.edu/psp/hprdext/EMPLOY...

We've had some reports that the application site doesn't work well in Google Chrome. You might want to apply in Firefox.

There are some points in the middle, but it's rare. I worked on one of these [1]. We've been building the system for just over ten years, and are starting to see some truly killer apps being built on top of it [2, 3].

While it has some great benefits once you arrive, the upfront costs are enormous. You basically need to find a funding source (or sources) that will pay for this product while you're building it. Also, in order for the research payoff to be worth it, you need both the product itself, and subsequent innovations it enables, to be research-worthy. Not all areas of research can support this. On top of it all, even when you do this, you'll still spend years of effort in activities that are essentially not research. You're basically responsible for all of your own customer support, sales, marketing, etc.---like a startup, but without the financial upside if you succeed. Yes there is recognition and so on, but the payoffs aren't as dramatic. Most people aren't ready to commit to this path.

Keep in mind that you can't build this in 5 years either. So a single generation of PhD students can't get it done. The only reason we were successful is because the key staff on the project stuck around for 5+ years after their PhDs because we all believed in doing the work.

Given all that, I don't hold it against people at all who just want to build prototypes and then move on to the next thing. It's way less risky and higher reward relative to the costs.

[1]: https://legion.stanford.edu/

[2]: https://flexflow.ai/

[3]: https://developer.nvidia.com/cunumeric

It has a decent compatibility with both Jest and Vitest's APIs (you can track progress here so you can use it as almost a drop-in replacement for either. Just as Node's, it has describe/it, mock, test and others, but with the expect syntax (which I find more readable). For example:

In this third and final article in the series on HTML Streaming, we will explore the practical implementation of the Diff DOM Streaming library in web browsing. This approach will allow any website using web components to retain its state during browsing. We will discuss in detail how to achieve this step by step using VanillaJS and Bun.

At Node Conference 2023, Jarred Sumner (creator of Bun) showed a demo of server components in Bun, so there is at least partial support in that ecosystem. The Bun repo provides bun-plugin-server-components as the official plugin for server components. And while I haven’t looked at it in-depth, Marz claims to be a “React Server Components Framework for Bun”.

Continuously evolving, Bun is currently optimized for MacOS and Linux, with ongoing efforts towards Windows compatibility. Tailored for resource-constrained environments like serverless functions, it emerges as an ideal solution. The Bun team is committed to achieving comprehensive Node.js compatibility and seamless integration with prevalent frameworks. For those intrigued by Bun's potential and want to give it a try, more information is available on its website at https://bun.sh/.

Let’s say you are interested in learning more about Bun and probably give it a try. Bun has a website, where you can learn more about Bun and its features (including all the benchmark data captured in this issue), and here is the link.

Looks like it, it seems the 2% are mostly odd platform specific issues that the authors' did not deem very important (my assumption for the release happening anyway). AFAIK this[1] PR tries to fix them.

[1]: https://github.com/oven-sh/bun/pull/9729

Bun has a solution for it. First of all, it already has a list of trusted dependencies. For them, Bun will execute all necessary scripts by default. Otherwise, you can add it to trustedDependecies in your package.json file. In Bun community usage of trustedDependencies is a hot topic. There are several suggestions on how to improve it.

Install Node.js (or Bun, or Deno, or whatever JS runtime you prefer) if it's not there

I think maybe I was unclear. I'm talking about writing libraries that abstract across these differences and provide a single API, as sibling describes. I already know it's possible. I made a simple filesystem abstraction here[0] and a very simple HTTP library that uses it here[1]. They both work in Node/Deno and the browser. Unfortunately I ran into issues with Bun's slice implementation[2]. But I suspect there's a much better way of detecting and using the different backends.

[0]: https://github.com/waygate-io/fs-js

[1]: https://github.com/waygate-io/http-js

[2]: https://github.com/oven-sh/bun/issues/7057