CS 452/652 Fall 2019 - Lecture 19

Debugging (addendum)

• context-switch stress test?
  • computation / loop with easily verifiable results
  • register coverage
  • use subroutine, recursion (to a limit)
  • trigger frequent timer interrupt
    • randomized timeout
    • interrupt handler (kernel): perform similar computation
    • clobber as much state as possible
  • test with and without optimization

Kinematics

• area of Mechanics (and thus Physics)
• studies "motion of objects without reference to the forces which cause the motion."

Velocity

• distance / time
• automatic measurement, as discussed earlier in class

Uncertainty

• measurement error: start & end times uniformly distributed
  • use average and std-dev?
    • but velocity is non-linear in time!
    • estimate velocity distribution with statistical methods?
  • use min and max?
    • use experiment mean/variance to estimate distribution min/max
    • compute estimated min/max velocity
    • compute estimated mean as midpoint: (min + max) / 2
    • keep it simple!
  • objective?
    • hit exact location → use average
    • no overshoot/undershoot → use max/min bounds
• real-world variations (wear and tear): difficult to model
  • repeated or continuous recalibration
  • data import/export - better than terminal copy-and-paste?

Aside: Average Latency?

• using an average to characterize or estimate latencies is often not a good idea
• latency utility curves usually have an S-shape
• when using an average, outliers can offset many values close to the mean
• however, outliers do not increase or decrease aggregate utility significantly!
• how often a train enters a critical section of the track without permission matters more than how much it overshoots

Acceleration/Deceleration

• model trains emulate real-world trains
  • we model the model trains...
  • most likely: time-dependent acceleration
  • approximate as constant acceleration!
• measure similar to velocity
  • measure time between two sensors
  • change speed level at first sensor
  • compute acceleration based on known estimates for velocities
    • first detect whether train has reached target velocity at 2nd sensor?
    • constant acceleration → average velocity during acceleration = (v₁ + v₂) / 2

Stop Distance

• manual experiment
  • send stop command when sensor is triggered
  • manually measure stop distance

Stop Time

• compute using acceleration model
• experiment by trying to stop right after sensor
  • use search algorithm to find right time: could be automated
  • stop time + velocity → compute stop distance?
  • search algorithm (e.g., binary search) might be brittle and need many experiments

Start

• based on known estimate for stop time/distance
• stop at known location, then start
• measure time to sensor
• can compute acceleration
  • similar caveat as acceleration measurement: did train reach target velocity?

Short Moves

• stop before reaching target velocity
• manual experiments with different (short) times between start and stop

Experiments and Data

• how many speed levels to investigate?
  • pairs of levels for acceleration/deceleration
• determine minimum speed (per segment?) to avoid getting stuck
• stop from lower speed is more accurate
• how much data can you feasibly collect?
• document and justify decisions!