measurement

Six things decide whether a kernel measurement means anything, and a script that ignores them can report a number that is wrong by a factor of two in either direction. What to control, which statistic to report, and the six ways a large speedup turns out to be a measurement artifact.

A single throughput number describes a configuration nobody runs. The four variables that have to be fixed, the curve that is the actual deliverable, and the bandwidth bound that turns the result from a description into a diagnosis.

A benchmark that replays one prompt measures the cache and reports a number production will never see. What the hit rate actually depends on, the eviction behaviour that erodes it under load, and the measurement that predicts the real gain.

Availability is the wrong promise for a platform whose jobs run for weeks. Four objectives that a researcher can actually plan against, how each is measured from data you already collect, the error budget that makes them enforceable, and the one number that should never be an objective.

The number is easy to state and hard to make honest, because every minute has to be classified and the classification is where the value is. The definition that survives scrutiny, the five categories the missing time falls into, and the property that turns a metric into a work list.

The tiling is ten lines and the theory is a working-set calculation. The interesting part is that the obvious benchmark does not measure what it appears to, because the library underneath is already tiled, and knowing which effect your numbers contain is the skill being tested.

A speedup number with no method behind it is worth nothing, and experienced interviewers stop listening at the number and start asking about the baseline. What makes a performance claim survive scrutiny, and the four ways a real one turns out to be smaller than it looked.

Any engineer can cut cost. The question is whether you measured what it cost you elsewhere, and whether you knew that before you shipped it. The three mechanisms that produce most of the savings, and the metric you have to watch on the way down.

Most platform tools are built for the platform team's model of the work rather than the work. What makes a tool get adopted, the measurement that proves it helped, and why the fast path for small jobs beats almost anything else you could build.