Using std::shared_ptr in a performance-sensitive context (e.g. after startup completes) is code smell.

Using pointers as important elements of a data structure, such that cycles are possible at all, is itself code smell. A general graph is usually better kept as elements in a vector, deque, or even hash table, compactly, with indices instead of pointers, and favoring keeping elements used near one another in the same cache line. Overuse of pointers to organize data tends to pointer-chasing, among the slowest of operations on modern systems.

Typical GC passes consist of little else but pointer chasing.

But the original article is completely, laughably wrong about one thing: an atomic increment or decrement is a remarkably slow operation on modern hardware, second only to pointer chasing.

Systems are made fast by avoiding expensive operations not dictated by the actual problem. Reference counting, or any other sort of GC, counts as overhead: wasting time on secondary activity in preference to making forward progress on the actual reason for the computation.

Almost invariably neglected or concealed in promotion of non-RC GC schemes is overhead imposed by touching large parts of otherwise idle data, cycling it all through CPU caches. This overhead is hard to see in profiles, because it is imposed incrementally throughout the runtime, showing up as 200-cycle pauses waiting on memory bus transactions that could have been satisfied from cache if caches had not been trashed.

If a core is devoted to GC, then sweeps would seem to cycle everything through just that core's cache, avoiding trashing other cores' caches. But the L3 cache used by that core is typically shared with 3 or 7 other cores', so it is hard to isolate that activity to one core without wastefully idling those others. Furthermore, that memory bus activity competes with algorithmic use of the bus, slowing those operations.

Another way GC-dependence slows programs is by making it harder, or even impossible, to localize cost to specific operations, so that reasoning about perforce becomes arbitrarily hard. You lose the ability to count and thus minimize expensive operations, because the cost is dispersed throughout everything else.