psycopg2cffi VS legion

The only compatibility issue I've run into is database drivers.

For PostgreSQL, psycopg2 is not supported. psycopg2cffi is largely unmaintained, and the 2.9.0 version in PyPI lacks some newer features of psycopg2: the `psycopg2.sql` module and empty result sets raise a RuntimeError in Python 3.7+. The latest commit in on Github does have these changes [1]. Psycopg 3 [2] and pg8000 [3] (as user tlocke mentioned elsewhere) are viable alternates provided you aren't stuck with older versions of PostgreSQL. I'm going to continue to use psycopg2cffi until I can upgrade an old PostgreSQL 9.4 database.

For Microsoft SQL Server, pymssql does not support PyPy [4]. It's under new maintainership so it might gain support in the future. pypyodbc hasn't had any activity since 2022, and no new PyPI release since 2021 [5]. The datatypes returned can differ between libodbc1 versions. On Ubuntu 18.04 in particular: empty string columns are returned as a single space, integer columns are returned as a Decimal. Also, if you encounter a mysterious HY010 error ("Function sequence error"), you may need to upgrade libodbc1 to v2.3.7+ from v2.3.4 using the Microsoft repos.

[1]: https://github.com/chtd/psycopg2cffi

From time to time, I use pgcopy coupled with psycopg2cffi to feed large volumes of data processed by custom parsers written in Python for several formats. The whole process is 4-5x faster with PyPy.

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