Benchmark Your VM Against CPython

Benchmarking VM Performance

You cannot optimize what you do not measure. Comparing your interpreter against CPython on the same workload reveals whether dispatch, allocation, or call overhead dominates.

Methodology

Sound micro-benchmarks follow a few rules:

• Warmup: run several iterations so caches and branch predictors stabilize
• Multiple samples: average several timed runs, discard outliers
• Heavy workload: recursive Fibonacci or a prime sieve stresses the interpreter loop

Dispatch styles matter:

• `switch` dispatch: portable, predictable branch mispredicts
• Computed goto: GCC extension building a direct jump table (CPython 3.11's specializing interpreter pushed further)

For example, `fib(35)` in bytecode versus the same algorithm in native C often shows a 50x or larger gap, mostly from per-opcode dispatch.

Use `clock()` from `<time.h>` and report milliseconds per iteration.

Design Notes

Report at least median and spread, not a single timing sample. OS jitter and cache warmth dominate short runs; three warmup iterations plus five measured runs is a minimal credible methodology for comparing interpreter variants.

For This Exercise

This exercise asks you to benchmark recursive Fibonacci in your VM against a C baseline. You will implement warmup runs, timed iterations, and printed comparison so you know where cycles go before optimizing.