One thing worth noting if you come from a programming background: the Python in the early chapters will feel basic, but the real payoff is in the exercises. The later chapters on PDEs and Monte Carlo have some genuinely meaty problems. The Laplace equation solver via relaxation methods is one of those exercises where you feel the underlying physics in a way pure analytic work doesnt give you.

The Numerical Recipes recommendation above is solid if you want more rigorous algorithm coverage. Alot of computational physicists are now moving toward JAX or Julia, where differentiable simulations are essentially free and hot loops can be JIT compiled. But for building foundations and physical intuition, a course structured like this is hard to beat.

For profiling before you get the compiler instrumentation working, `perf record` + `perf report` will get you pretty far on Linux. Wont give you per-allocation-site data but its more than enough to see where time is going inside the collector itself. Valgrind's massif tool is also useful if you want heap snapshot data rather than CPU time.

Worth looking at MPS (Memory Pool System from Ravenbrook) if you havent already. They deal with the same ambiguous reference problem and their approach is basically what you described, conservatively referenced objects get pinned and dont move during compaction. They have pretty detailed writing on the trade-offs between conservative scanning and precise enumeration that might be useful for your next article.

One thing id be curious about is how your stack depth reduction after removing the recursive evaluator affects pause time. Conservative GC pause time is often dominated by how much stack there is to scan, so getting rid of recursive eval might have already improved your worst-case pauses more than you realize.

For local/offline Qwen 2.5 Coder 32B is probably your strongest option if you have the VRAM (or can run it quantized). Handles C better than most other local models in my experience.