Go

Starting from the von Neumann architecture and instruction execution, this article follows count++ down to the hardware-level problems of atomicity, visibility, and ordering.

Moves from hardware memory models back to the language layer: why languages need their own concurrency semantics, and what Java, Go, and CPython guarantee to concurrent programs.

Defines the scope as concurrency within a single process on a single machine, then connects shared variables, shared memory, message passing, language concurrency semantics, and hardware implementation.

Uses the same counter and counter + ready examples to trace read-write locks from Java language-level rules through the JDK, HotSpot, and x86-64, explaining Atomicity, Visibility, Ordering, and the boundary with Mutex.

Uses the counter + ready example to trace Java volatile from the Java Memory Model through HotSpot to x86-64, showing how visibility and ordering are defined and implemented, and how volatile differs from atomics and mutexes.

Continues with counter and ready to explain the language-level rules behind Java AtomicInteger, Go sync/atomic, and CPython's application-level Atomic boundary.

Continues with counter++ to explain how mutexes provide atomicity, visibility, and ordering, then compares Java synchronized, Go sync.Mutex, and CPython threading.Lock.

Traces the implementation paths of Java AtomicInteger, Go sync/atomic, and CPython's internal atomic operations to show how atomic RMW, CAS, and memory ordering reach the CPU.