Concurrency Programming

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.

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

Following Java platform and virtual threads, Go G-M-P, and CPython threads from their runtime representations to OS threads and ultimately CPU execution.

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.

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 actual implementation paths of Java synchronized, Go sync.Mutex, and CPython threading.Lock to show how mutexes rely on atomic operations, memory ordering, and waiting and wake-up mechanisms.

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

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.

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.

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.