you may find my improved table design of interest, which avoids the need for mirroring: https://outerproduct.net/trivial/2022-10-06_hash.html

i suppose you can probably do anything with dependent types, but i'm not sure this is a useful perspective. i commend you to read my comments on the red website https://lobste.rs/s/xkcrvn/

(i do think it is a valid question whether abstract interpretation is a good idea)

i am not cheating; you are cheating by trying to do integer arithmetic instead of float arithmetic. in particular: 99 * progress is a (potentially big) integer; then the quotient, from my understanding of python, is equal to the mathematical quantity rn(99 * progress / total), which is not trivial to compute. (although cpython does tend to do a particularly bad job of this sort of thing.) (compare with c or with my version of the python, where it would be rn(rn(99 * progress) / rn(total)), rounding twice, which is very easy to compute. i'm not saying the c semantics is better, mind.) when you scale up by 10x, the numerator is <1/2 'ulp' away from the denominator, and so the quotient rounds up to 99 exactly; there is still double rounding (would have been triple rounding in c and my python), because we got rn(rn(99 * progress / total) + 0.5) where what we wanted was rn(99 * progress / total + 0.5) (which is mathematically always the correct result)

i agree it is not common to have so many steps. but if i were providing a routine that was intended to be robust where others were not, i would try to be comprehensive. and i would not try to do int math with floats unless i could show it to be robust (i have sketched such a stunt! https://gist.github.com/moon-chilled/60bd2ba687dc197d93a9d22...). the integer routine is simpler and more honest anyway, and it is obvious that it works uniformly for the entire range

note also that, with x floating, the python expressions 10 * x and 10 * x - 1 are equivalent, meaning the error is on input to the percent function. (if we set progress to 10 * x - 8, the immediately preceding fp number, we do get 99, but there is no deep reason for this, and it differs for different values of 10.)

> if your username is named after the band, good taste :)

my username comes from a book: the neverending story. i am more using the german version of the name these days but i do not feel like making a new account on this godforesaken website

yeah—idk graph algos really, but have heard parallelising combinatorial search in general (eg sat) is hard because forks and joins happen heterogeneously and erratically. this 2001 vintage has a bunch of completely sequential graph colouring algorithms in j https://dl.acm.org/doi/pdf/10.1145/570406.570416 (and j at least has sparse matrices!)

> Constant lifting within the compiler is pretty cool, I'll have to look into that.

hrm, it seems to refer to 2 things: 1) constants allocated in a special space; 2) interning. 2 is obviously worthwhile; 1 i would guess is related to memory being weird on the gpu?

slug builds acceleration structures ahead of time. the structures are overfit to the algorithm in a way that ttf should be but which is economical for video games. that doesn't seem like an interesting concern and nothing about it is specific to the gpu

i don't think that's right. it's just 1/t. after all, after t time, one task must have made progress; since there are t tasks, the probability that i'm the task that made progress is just 1/t

the primary point of confusion, i think, is that getting interrupted does not mean that you are necessarily going to lose the cas

hm ...

  >>> def percent(progress, total):
  ...     return round(99 * progress / total + 0.5)
  ...
  >>> x = 9007199254740990.0
  >>> x - 1 < x
  True
  >>> percent(x - 1, x)
  100

if you have metadata to identify object starts then you can do 1 bit per min object size (which can be bigger than the alignment granule)

> Any sufficiently complicated C or Fortran program contains an ad hoc, informally-specified, bug-ridden, slow implementation of half of Common Lisp.

if you have an expression with n terms, then you will end up with O(n) terms each taking up O(n) space, so the overall space usage is quadratic. (that's the fair way to compare to polyhedra since they're always global)