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Why does Fully Utilized Thread Change over time?

While I was running a simple program which needed to run for a couple minutes, I made an observation I can’t find an answer for on google.

Here is a snip of my system monitor which demonstrates my computer switching which processor is at full capacity while running my program. my system monitor

This isn’t causing me any trouble, but I find it interesting, because I would assume that switching between units would take more time than just running at 100% capacity the whole time on one unit. Is there any simple explanation for this behavior?

Thanks!

Is there a known way to make an efficient, compact, and fully persistent stack or queue?

In the world of mutable/ephemeral data structures and imperative programming languages, one of the classic ways to implement a stack or queue is to use array doubling: use mutation to fill up or empty an array, doubling or halving to expand/contract. Such stacks/queues have several nice properties:

  1. They use at most twice as much memory as strictly necessary.
  2. They involve minimal indirection.
  3. They use cache efficiently.
  4. They have amortized $ O(1)$ insertion and deletion operations.

In a purely functional system, this approach falls down quite flat, because "mutate the array to fill/empty" becomes very expensive: the array has to be copied each time. I was wondering if there might be some reasonable compromise approach, making something more compact than classical approaches (a la Okasaki) but still with constant amortized time operations. Stacks are simpler, so I started thinking about those. My first attempt (in Haskell notation) was

data Queue a   = Shallow !(Array a)   | Deep !(Array a) (Queue a) 

with the rule that the array at depth $ n$ must have either $ 0$ or $ 2^n$ elements. Unfortunately, this doesn’t look like it’s nearly good enough. It appears that insertions impose an $ O(\log n)$ amortized cost, since flipping from a 1 digit to a 0 digit gets more expensive the deeper it happens in the tree. My next attempt was the same, but using skew binary numbers instead of binary numbers. Same deal. Is there some trick I’m missing, or am I asking to have my cake and eat it too?

What rule system handles fully automatic firearms the best?

I know it is a subjective matter, so let me specify a bit:

  • Full automatic fire encompasses all modern and future firearms with some form of recoil (lasers out of scope)
  • The attacker is sending multiple projectiles downrange. This includes 3 round burst, long burst, suppressive fire, etc
  • the focus is on infantry sized weapons. The combat dynamics of i.e. fighter jets is so much different it makes no sense to try to squeeze them in under the same set of rules

How I define good:

  • it encourages cooperation it the team. Covering fire, bounded overwatch, etc. are the primary tools of small unit tactics
  • it is reasonably elegant. Rolling a skill check for all the bullets in a 30 round burst is too complicated, but some extra rolls or bookkeeping is acceptable
  • it captures the key compromises an infantryman should consider (i.e. long burst makes no sense on a longer range, but devastating from up close), but not all the physical factors of muzzle rise, recoil, etc

How to fully view cross-signed certificate signatures

Long story short: Security scan raised a flag about a cert, found that it was an expired root on a cross-signed cert. Yadda yadda, updated intermediates, fixed issue.

However, I noticed that openssl x509 -noout -text intermediate.crt only lists a single signature on the certificate, even though there are two. I’ve looked over the man page and I can’t find an option that prints additional signatures, and google searches are even less useful.

Is there a parser out there somewhere that will list all of the signatures on a certificate? I notice that Qualys enumerates and follows the various cert paths, but I’d prefer not to have to make prod changes to view certificate metadata.