This just kind of sounds like a random idea that sounds good in your head but not based in reality; the point of a PhD has always been one thing, and one thing alone: train someone who can publish influential papers in top-tier venues.
Anyone who says otherwise is just either uninformed or selling a dream.
> The US used to be the most desirable place for immigrants and the US higher education system used to be the envy of the world and now for both of these it is not any more.
That is a nice sound bite for TV--the reality is that the government has been systematically rejecting visas in a bid to kill universities. Yes, it's true that universities counted too much on rich MS students and the like. But the reason for the failures isn't that "it is not any more," as you say--it's a very calculated move by the current administration to eat universities' bottom line.
This is very cool to hear. Please get in touch with me, I would love to learn more. By the way, I am recruiting participants for an upcoming seminar in which I am soliciting industrial participation: https://kmicinski.com/minnowbrook-26. Please get in contact with me if this is relevant to anyone at your company.
I am the author of this paper, and I do not agree with Dr. Smaradgakis' comments. As far as I can tell, the root of his concern is that that paper did not target Souffle Datalog, a specific Datalog language in which his group writes. The criticism is totally fair in a sense, but I do not agree with you that these are "pretty big caveats" in our paper, for the reasons I address in my rebuttal to his comment. I will say however, that his very engaging comments have pushed us to do significant follow-on work, which has now pushed our engines to scale to the kind of code he writes in Datalog, yielding very exciting results, and I am hoping that he will be satisfies when he sees it :-)
I will also mention that our group has follow-on work from this (I cannot share this widely due to reviewing reasons but a preprint is available if you would like to search) which significantly addresses Yiannais' concerns. In the engine cited here, we scale to small programs (tens of lines): our engine does not support large, tricky queries for interesting, asymptotic reasons (which are also shared by other Datalog engines based upon binary joins, not unique to our engines). Our new engines port a significantly more complex class of join algorithms to the GPU, and we have used these new algorithms (and our novel GPU-based implementation) to run 500-1000-line Datalog programs which beat all existing state-of-the-art program analysis engines by 20-50x.
In sum, I strongly disagree with the "pretty big caveats" remark. Dr. Smaradgakis' comments are quite firm in nature and I very much respect them. But I encourage you to check out my rebuttal and also (regarding scaling to larger subsets of Datalog and "real" programs) our recent follow-on work.
If you would like proof, please email me, we are happy to help you evaluate for yourself. My email is always open: [email protected].
It's true that Ascent does not index ADTs either, but there are some tricks that you can use when you control the container type to get similar performance by, e.g., storing a pre-computed hash. I believe Arash, the main author of Ascent, was exploiting this trick for Rc<...> members and seeing good performance gains. It is a bit nuanced, you're right that Ascent doesn't pervasively index ADTs out of the box for sure.
I think the basic issue is that ADTs are simply not indexed--so to the degree that you write a query that would necessitate an index on a subtree of an ADT, you will face asymptotic blowup, as the way ADTs work will force you to scan-then-test across all ADTs (associated with that top-level tag). The issue is discussed in Section 5.2 of this paper here: https://arxiv.org/pdf/2411.14330
Anyone who says otherwise is just either uninformed or selling a dream.