Found a $100 bill in the grass. No-one nearby to think it might belong to them.

Cray tried to build the T3E (iirc) out of Alphas. DEC bragged how good Alpha was for parallel computing, big memory etc etc.

But Cray publicly denounced Alpha as unusable for parallel processing (the T3E was a bunch of Alphas in some kind of NUMA shared memory.) It was so difficult to make the chips work together.

This was in the Cray Connect or some such glossy publication. Wish I'd kept a copy.

Plus of course the usual DEC marketing incompetence. They feared Alpha undoing their large expensive machine momentum. Small workstation boxes significantly faster than big iron.

Slang for BC. It's a joke, because (coastal) BC is mostly wet. And BC is the westernmost province.

When ordering breakfast, such as eggs and toast. "You want white or brown toast?"

Since most toast is toasted to a brown tone, the question confuses Americans (west-coast, anyways).

It's really the question: white bread toast or whole-wheat bread toast?

In BC.

Why, you ask?

How do you point it? Where do you point it?

You have a "telescope" with a field of view of one-planets worth of pixels. But the planet is in orbit, so it drifts away from the imaged field of view within minutes.

Meanwhile your sensor is travelling away from the "lens" so transverse velocity would be needed to track the orbit at a delta-v and direction that is unknowable. Unknowable, because you have to know where the planet is, within a radius, to put your "sensor" in the right place in the first place.

Imagine taking a straw, place it in a tree, walk away a few km and focus a telescope on the straw and hope to look through the straw to see an airplane flying past. You have the same set of unknowables.

https://www.netlib.org/tex/bib/prng.pdf

758 pages and counting....

https://allendowney.com/research/rand/

And Lemire in 2017 asked the question

https://lemire.me/blog/2017/02/28/how-many-floating-point-nu...

The basic approach: collect top 60 bits of a 64-bit PRNG. (Assume the LSBs are corrupt or zero or nonrandom). Set exponent zero. If the top mantissa bit is zero, shift left, subtract one from exponent. Repeat. When you run out of bits, collect another PRNG from your generator and resume until you have 56 bits of mantissa. When done, your floating-point PRNG is 2^exponent * mantissa.

My short explanation: Suppose you want a random distance. Multiplying a PR integer is the same as selecting a random tile in the distance, and measuring the distance to the (far) edge of the tile. This is the multiply method.

But if you want a real-valued distance even for very near distances, you need to scale your random number and ensure you have random bits throughout the mantissa. So reduce the exponent for every leading zero in your PR integer and shift. Add more bits if needed.

Test: the histogram of exponents for a large-enough set of samples is linear, give or take. Very small floating numbers (less than say 1/32768) are 1/16 as likely as numbers in [0.5,1).

- your test codes are not reproducible: running twice generates different sets of numbers because they have unknown seeds. As a result, if you change (a) compilers or compiler switches, (b) operating system versions, (c) host processors or (d) architectures, the question arises: what is wrong? What is different? This is known as a regression test.

- try shishua as another speedy PRNG. See https://espadrine.github.io/blog/posts/shishua-the-fastest-p...

- You only test a few times? I think one hundred BigCrush tests, using a set of 100 seeds, would be suitable. Takes a few days on an RPI 4 (with cooler). Run the same 100 tests on a Ryzen and Xeon, just to be sure. They should be bit-for-bit identical.

- 100 BigCrush tests should show only a handful (4 or fewer) duplicate test failures.

- your seeds are almost great: too many people think "42" is random in a space of 0 through 2^64. But 0xdeadbeef is so 1990s...

- you don't need different seeds per PRNG; you can generate reproducible ones (2x to 4x 64bit) from a single good 64-bit seed and your favourite PRNG. Your test code should read a seed or set from the command line (see first item).

- warmups? Really?

- Remember that BigCrush and other tests are created by mathematical people not practical people. Do they test for equal numbers of odd and even results? Hmmmm....

- try Collatz, see https://news.ycombinator.com/item?id=39733685

- the tests are very cache-friendly; nobody does this. It's true that everybody compares against this unrealistic scenario, however.

- if you've spent a month in twisty little PRNG passages, all alike, you are on the first steps of your journey. Take a towel.

J

They estimated $200k for a single NVIDIA GPU-based CPU complete with RAM and networking. That's where my number came from. (RAM and especially very-high-speed networking is very expensive at these scales.)

Their paper (arxiv 2412:1947) explains they used 2048 H800s. A computer cluster based on 2048 GPUs would have cost around $400M about two years ago when they built it. (Give or take, feel free to post corrections.)

The point is they got it done cheaper than OpenAI/Google/Meta/... etc.

But not cheaply.

I believe the markets are overreacting. Time to buy (tinfa).

OpenBSD has an installation ramdisk. Just boot that.

See https://raby.sh/installing-openbsd-on-ovhs-vps-2016-kvm-mach...

I've also seen "dd if=path/to/install/ramdisk /dev/sd0" and reboot; depends on being classic BIOS not uefi. Can't find the link tho.

This article is about absolutes and fear. To conflate the two is an obvious rhetorical trick that amounts to clickbait, approximately.

"It's rare for a reentering object to hit a structure..." which is an example of the probability and the hazard. So the risk by most people's definition is "low".

So what's the problem? "According to the European Space Agency, the annual risk..." is the problem. Misusing (or misunderstanding) terminology is typical. Unfortunately, typical for Ars.

I had tried pgvector 0.6.2 on an OCI free node (2cpu 64GB) and noticed a few things:

- pgvector build environment does NOT use -O3

- cosine indexing with/without -03 was 1h:6h elapsed time (10M 128 x fp64 table)

- memory consumption for indexing is huge, I estimated 2x table size

- you can do parts of tables (maintenance_work_mem=) substituting disk io for memory and this only doubles elapsed time

My general comment would be: prospective users need effective guidance (beyond the great advice already on the pgvector website) about memory, cpu, and disk.

I really like the pgvectorscale possibilities for faster lookups; some great ideas there.

There will be operating system noise that can be in the multi-percent range. This is defined as various OS services that run "in the background" taking up cpu time, emptying cache lines (which may be most important), and flushing a few translate lookaside entries.

Once you recognize the variability from run to run, claiming "1%" becomes less credible. Depending on the noise level, of course.

Linux benchmarks like SPECcpu tend to be run in "single-user mode" meaning almost no background processes are running.

https://marc.info/?l=openbsd-tech&w=2&r=1&s=coverity&q=b

https://marc.info/?l=openbsd-tech&m=164303498026116&w=2

https://github.com/flame/blis/pull/737

4x4 matrix: you are challenged with letter/number for row/column and must respond with the matrix entry. Not much better than totp; try to find it on your phone.

photo array: given an array of challenge photographs, select the one you know is your secret photo.

not-so-secret Q&A: what is your mother's maiden name? Name of your first car? Your second wife's third brother's cousin's name?