Benchmarking with a Stopwatch

Benchmarking with a Stopwatch

When people imagine benchmarking, they picture automated harnesses, high-resolution timers, and dashboards with percentile charts. Useful tools, absolutely. But many core lessons of performance engineering can be learned with much humbler methods, including one old trick from retro workflows: benchmarking with a stopwatch and disciplined procedure.

On vintage systems, instrumentation was often limited, intrusive, or unavailable. So users built practical measurement habits with what they had:

  • fixed test scenarios
  • fixed machine state
  • repeated runs
  • manual timing
  • written logs

It sounds primitive until you realize it enforces the exact thing modern teams often skip: experimental discipline.

The first rule was baseline control. Before measuring anything, define the environment:

  • cold boot or warm boot?
  • which TSRs loaded?
  • cache settings?
  • storage medium and fragmentation status?
  • background noise sources?

Without this, numbers are stories, not data.

Retro benchmark notes were often simple tables in paper notebooks:

  • date/time
  • test ID
  • config profile
  • run duration
  • anomalies observed

Crude format, high value. The notebook gave context that raw timing never carries alone.

A useful retro-style method still works today:

  1. Define one narrow task.
  2. Freeze variables you can control.
  3. Predict expected change before tuning.
  4. Run at least five times.
  5. Record median, min, max, and odd behavior.
  6. Change one variable only.
  7. Repeat.

This method is slow compared to one-click benchmarks. It is also far less vulnerable to self-deception.

On old DOS systems, examples were concrete:

  • compile a known source tree
  • load/save a fixed data file
  • render a known scene
  • execute a scripted file operation loop

The key was repeatability, not synthetic hero numbers.

Stopwatch timing also trained observational awareness. While timing a run, people noticed things automated tools might not flag immediately:

  • intermittent disk spin-up delays
  • occasional UI stalls
  • audible seeks indicating poor locality
  • thermal behavior after repeated runs

These qualitative observations often explained quantitative outliers.

Outliers are where learning happens. Many teams throw them away too quickly. In retro workflows, outliers were investigated because they were expensive and visible. Was the disk retrying? Did memory managers conflict? Did a TSR wake unexpectedly? Outlier analysis taught root-cause thinking.

Modern equivalent: if your p99 spikes, do not call it “noise” by default.

Another underrated benefit of manual benchmarking is forced hypothesis writing. If timing is laborious, you naturally ask, “What exactly am I trying to prove?” That question removes random optimization churn.

A strong benchmark note has:

  • hypothesis
  • method
  • expected outcome
  • observed outcome
  • interpretation

If interpretation comes without explicit expectation, confirmation bias sneaks in.

Retro systems also made tradeoffs obvious. You might optimize disk cache and gain load speed but lose conventional memory needed by a tool. You might tune for compile throughput and reduce game compatibility in the same boot profile. Measuring one axis while ignoring others produced bad local wins.

That tradeoff awareness is still essential:

  • lower latency at cost of CPU headroom
  • higher throughput at cost of tail behavior
  • better cache hit rate at cost of stale data risk

All optimization is policy.

The stopwatch method encouraged another good habit: “benchmark the user task, not the subsystem vanity metric.” Faster block IO means little if perceived workflow time is unchanged. In retro terms: if startup is faster but menu interaction is still laggy, users still feel it is slow.

Many optimization projects fail because they optimize what is easy to measure, not what users experience.

The historical constraints are gone, but the pattern remains useful for quick field analysis:

  • no profiler on locked-down machine
  • no tracing in production-like lab
  • no permission for invasive instrumentation

In those cases, controlled manual timing plus careful notes can still produce actionable decisions.

There is a social benefit too. Manual benchmark logs are readable by non-specialists. Product, support, and ops can review the same sheet and understand what changed. Shared understanding improves prioritization.

This does not replace modern telemetry. It complements it. Think of stopwatch benchmarking as a low-tech integrity check:

  • Does automated telemetry align with observed behavior?
  • Do optimization claims survive controlled reruns?
  • Do gains persist after reboot and load variance?

If yes, confidence increases.

If no, investigate before celebrating.

A practical retro-inspired template for teams:

  • keep one canonical benchmark scenario per critical user flow
  • run it before and after risky performance changes
  • require expected-vs-actual notes
  • archive results alongside release notes

This creates performance memory. Without memory, teams repeat old mistakes with new tooling.

Performance culture improves when measurement is treated as craft, not ceremony. Retro workflows learned that under hardware limits. We can keep the lesson without the limits.

The stopwatch is symbolic, not sacred. Use any timer you like. What matters is disciplined comparison, clear expectations, and honest interpretation. Those traits produce reliable performance improvements on 486-era systems and cloud-native stacks alike.

In the end, benchmarking quality is less about timer precision than about thinking precision. A clean method beats a noisy toolchain every time.

2026-02-22