Source: point3d_refactor.py
Simulated Instance Population: 500,000 instances
[INFO] CPython 3.12 64-bit ABI simulator initialized.
[INFO] Ready for refactoring analysis. Select a preset or edit code.
CPython Memory Layout & Heap Offsets Standard PyObject + Dynamic __dict__
Per-Instance RAM
152 B
Total Heap RAM
72.48 MB
Attr Lookup Time
38.4 ns
GC Tracking Head
16 B (Tracked)
Instance Struct Physical Slices (64-bit Architecture) 152 bytes total per instance
Hardware Cache & Pointer Indirection Overhead 3 pointer dereferences per read
Object pointer -> PyObject base -> tp_dictoffset -> PyDictObject -> hash table lookup -> PyFloatObject value dereference. High L1 cache miss probability on vector iterations.
QUORA ARCHITECTURAL LESSON:

"‘self.values’ does not need to be an attribute of ‘self’... The most important programming knowledge is understanding how the entire system works across all its layers." In standard Python classes, unused attributes waste 8 bytes for a pointer plus dictionary entries, forcing multiple cache-line misses per point.

Tracing how a single Python statement p = Point3D(1.0, 2.0, 3.0); val = p.x executes across every hardware and runtime boundary:

LAYER 1: COMPILER & BYTECODE Bytecode Dispatch
LOAD_FAST 0 (p)
LOAD_ATTR 1 (x)
STORE_FAST 2 (val)

In standard classes, LOAD_ATTR invokes _PyObject_GetAttr() looking through descriptors, then falling back to dictionary lookup.

LAYER 2: CPYTHON C-RUNTIME Evaluation Loop
PyObject_GenericGetAttr()
PyDict_GetItemRef()

With __slots__, CPython replaces hash queries with immediate struct offset arithmetic: *(PyObject**)((char*)obj + slot_offset).

LAYER 3: OS & VIRTUAL MEMORY Allocators (pymalloc / glibc)
pymalloc arenas: 256KB
pools: 4KB / blocks: 16-512B

Small Python objects are served from internal pymalloc pools. Dynamic dict resizing triggers fragmented OS virtual allocations.

LAYER 4: HARDWARE & CACHE CPU Cache Line Alignment
Cache lines: 64 Bytes
Packed: 2.6 pts/line

Slotted or packed representations fit neatly into CPU L1/L2 cache lines, enabling SIMD vectorization and avoiding pipeline stalls.