If you’ve got an existing paragraph written that you just know could be rephrased more eloquently, and can describe the type of rephrasing/restructuring you want… LLMs absolutely slap at that.
I finished this article in February this year, just before joining NVIDIA. It didn't get officially published then for... reasons. Posting now despite some of the information being a little out of date as I still think the content might be useful to others.
Tried to make the article as readable as possible on mobile, tablet, and desktop. Mobile necessitated a smaller font size for the code to obviate the need for horizontal scrolling.
Light/dark mode is supported, and the images are even cognizant of the selected mode!
I am doing a talk at PyData Seattle this year (Nov 7-9) focused on this topic, so any feedback regarding additional areas of interest would be appreciated.
Looks like ‘97. Unfortunately I can’t find the receipts! If I had to guess, I think I’d say somewhere around $2000 in all. That computer lasted me a LONG time. When it was done being a desktop, it became a Linux server until I sold it around 2009. Sometimes I wish I’d kept it: the Pentium 2 was such an important CPU and I smile every time I see the one on display in the Computer History Museum.
I remember my first job in 2000, straight out of 1.5 years of college, getting to play directly with Digital UNIX and Alpha processors! The Alpha 21264 was a beast at the time.
It’s insane how hard hovering is. I had about 35 hours of fixed wing time, and treated myself to a helicopter lesson for my birthday.
Hovering was so humbling! You’d be stable for a few seconds and then oops now we’re suddenly crabbing backwards whilst rolling laterally whilst exacerbating everything with pilot-induced oscillations in every conceivable axis of movement.
Having to constantly enter three inputs whenever the external environment changes (ie wind, gust), or any time any one of the three inputs change… it absolutely requires some new neural pathways to be forged!
I flew with Patty Wagstaff many years later and even she admitted hovering was so hard, to the point it looked like she wasn’t going to be able proceed with her rotor license (before it all clicked).
> so hard, to the point it looked like she wasn’t going to be able proceed with her rotor license (before it all clicked)
Yeah, I think we were all convinced that we were going to wash out of flight school in the first few weeks. Hovering was not something that you could see yourself gradually getting better at, so it felt impossible right up until it wasn't. It really did just "click" one day. Almost two decades later, I still have a vivid memory of the very moment that I realized I had full control of the aircraft when picking it up from the ground.
When my buddy and I were telling an instructor pilot one day how we felt (like we'd never be able to hover), he wisely pointed out that the flight school syllabus had a certain number of hours for a reason. It had been refined over the past 50 years, so they knew exactly how many hours were needed, and if things "clicked" for us ahead of that schedule it would mean that time and money were being wasted.
Pretty good - r=2 is a regular graph afaict, and basically anything that maps to a frontier-based pattern works well. Ex: level synchronous bfs during topological sort.
For the 'easy' way we do in gfql, which is basically vector ops on bulk wavefronts, we can do massive cypher traversals like you're asking, like 100M edges touched in a result substep, and on a tiny GPU. There are other such bulk patterns we want to add such as Pregel style, which open other algorithms here. In practice we can often just call cudf/cugraph as building blocks so haven't had the pressure to do so yet.
The weak spot I find is more like small OLTP lookups. Ex: Imagine a taxi routing traffic service pinging for one car to do a couple hops out, where you just want a KV store in cheap RAM. But if you are batching those queries, like in a heavy city, and going deeper on them, maybe more interesting.
None of the UNIXes have the notion of WriteFile with an OVERLAPPED structure, that’s the key to NT’s asynchronous I/O.
Nor do they have anything like IOCP, where the kernel is aware of the number of threads servicing a completion port, and can make sure you only have as many threads running as there are underlying cores, avoiding context switches. If you write your programs to leverage these facilities (which are very unique to NT), you can max perform your hardware very nicely.
I should do an updated version of that deck with io_uring and sans the PyParallel element. I still think it’s a good resource for depicting the differences in I/O between NT & UNIX.
And yeah, IOCP has implicit awareness of concurrency, and can schedule optimal threads to service a port automatically. There hasn’t been a way to do that on UNIX until io_uring.
Yeah I’d definitely include RegisteredIO and IoRing. When I was interviewing at Microsoft a few years back, I was actually interviewed by the chap that wrote RegisteredIO! Thought that was neat.
