Add internal docs about the free-threaded GC. (gh-132562)

InternalDocs/garbage_collector.md

@@ -477,17 +477,24 @@ specifically in a generation by calling `gc.collect(generation=NUM)`.
 ```
 
 
-Optimization: visiting reachable objects
-========================================
+Optimization: excluding reachable objects
+=========================================
 
-An object cannot be garbage if it can be reached.
+An object cannot be garbage if it can be reached. To avoid having to identify
+reference cycles across the whole heap, we can reduce the amount of work done
+considerably by first identifying objects reachable from objects known to be
+alive.  These objects are excluded from the normal cyclic detection process.
 
-To avoid having to identify reference cycles across the whole heap, we can
-reduce the amount of work done considerably by first moving most reachable objects
-to the `visited` space. Empirically, most reachable objects can be reached from a
-small set of global objects and local variables.
-This step does much less work per object, so reduces the time spent
-performing garbage collection by at least half.
+The default and free-threaded build both implement this optimization but in
+slightly different ways.
+
+Finding reachable objects for the default build GC
+--------------------------------------------------
+
+This works by first moving most reachable objects to the `visited` space.
+Empirically, most reachable objects can be reached from a small set of global
+objects and local variables. This step does much less work per object, so
+reduces the time spent performing garbage collection by at least half.
 
 > [!NOTE]
 > Objects that are not determined to be reachable by this pass are not necessarily
@@ -515,6 +522,171 @@ increment. All objects directly referred to from those stack frames are
 added to the working set.
 Then the above algorithm is repeated, starting from step 2.
 
+
+Finding reachable objects for the free-threaded GC
+--------------------------------------------------
+
+Within the `gc_free_threading.c` implementation, this is known as the "mark
+alive" pass or phase.  It is similar in concept to what is done for the default
+build GC.  Rather than moving objects between double-linked lists, the
+free-threaded GC uses a flag in `ob_gc_bits` to track if an object is
+found to be definitely alive (not garbage).
+
+To find objects reachable from known alive objects, known as the "roots", the
+`gc_mark_alive_from_roots()` function is used.  Root objects include
+`interp->sysdict` (the `sys` module dictionary), `interp->builtins`, and
+`interp->types`.  Also included are all objects referred to by active Python
+frames.  These objects and the transitive closure of objects reachable from
+them have `_PyGC_BITS_ALIVE` set.  Any object with that bit set is excluded
+from the rest of the cyclic garbage detection process, since we know it cannot
+be unreachable.
+
+> [!NOTE]
+> If the `gc.freeze()` function has been used, this phase of the collector is
+> skipped. That is done for two reasons.  First, it is unlikely to be a
+> performance win if most of the objects have been marked as frozen.  As such,
+> they would be excluded for the cyclic garbage detection, without this extra
+> work anyhow.  Second, one of the purposes of using `gc.freeze()` is to avoid
+> dirtying the memory pages holding frozen objects.  If this phase was executed,
+> the toggling of the `ob_gc_bits` flags would dirty pages and defeat that.
+
+Software prefetch hinting
+-------------------------
+
+To speed up the "mark alive" phase of the free-threaded GC, an additional
+optimization, known as software prefetching, is used.  The GC will execute
+explicit prefetch CPU instructions in order to reduce the latency due to
+loading data from main memory.  This added complexity can pay off since main
+memory is so much slower compared to accessing data in the CPU cache.  This
+is enabled only if the number of long-lived objects exceeds a threshold.  If
+the set of objects being garbage collected is small, the accessed memory is
+likely to fit entirely in the CPU cache and software prefetch is not helpful.
+
+The details of this optimization are intricate, with the source code being the
+best reference.  However, the rest of this section gives a high level
+description of how it works and explains some design decisions.
+
+Software prefetching is only used during the "mark alive" phase of the GC.
+Specifically, when we are performing the transitive closure of the "alive"
+status of objects (i.e. objects reachable from known alive objects, known as
+roots).  For each object we find, we need to traverse all objects directly
+reachable from that object.  If that set of referred objects is in scattered
+locations of memory, the hardware prefetch is unlikely to predict the next
+accessed memory location.
+
+Making software prefetch work well hinges on a key principle: allow enough time
+between issuing the prefetch instruction for a memory location and actually
+accessing that location's data.  We can call that time difference the prefetch
+window.  If the window is too large, we fill up the CPU caches with data that's
+not needed yet.  Worse, the data might be discarded from the cache before we
+actually use it.  If the window is too small then the memory system does not
+have enough time to finish loading the memory and the CPU will have to wait.
+The window is indirectly tuned by the prefetch buffer parameters.  The buffer
+will be explained next.
+
+The prefetch buffer is a FIFO queue of fixed size.  When we enqueue an object
+reference into the buffer, we also issue a software prefetch instruction for
+that memory location.   When we dequeue an object reference from the buffer, we
+assume or hope that enough time has elapsed so that the memory has been loaded
+into the cache.  This is the mechanism that provides the window.
+
+When performing the transitive closure of "alive" status, the set of objects
+yet to visit are stored in one of two places.  First, they can be stored in the
+prefech buffer. Second, there is a LIFO stack, of unlimited size.  When object
+references are found using `tp_traverse`, they are enqueued in the buffer if
+it is not full, otherwise they are pushed to the stack.
+
+We must take special care not to access the memory referred to by an object
+pointer until we take that reference out of the prefetch buffer.  That means we
+cannot check if an object is a "container" (if the `PyObject_IS_GC()` test is
+true) or if the object already has the "alive" flag set.  Both of those tests
+would require that the object memory is accessed.  There are some additional
+elaborations that try to keep the buffer filled to the optimal size but to keep
+things simple we will omit those details here.  Not discussed in details are
+"spans", which help reduce the amount of stack used and make it easier to
+control the size of the buffer.
+
+As mentioned, the prefetch window is the time delay between the issue of the
+prefetch instruction, on buffer enqueue, and the memory access, after buffer
+dequeue.  It is tuned by adjusting some buffer parameters.  If processing time
+were equal for every object then the buffer length would be proportional to the
+window.  Since processing each object can actually take a variable amount of
+time, the relation between the buffer length and the prefetch window is only
+approximate. However, this proportional relationship is assumed to hold true
+and we avoid the overhead of actually measuring object processing times.
+
+The relevant parameters are the maximum buffer size and the low and high
+thresholds for filling. The buffer parameters are set as follows: maximum
+length is 256, low threshold is 8, high threshold is 16.  These parameters are
+used as follows.  If the buffer has reached the maximum size, new object
+pointers found while following references are pushed to the stack, rather than
+put in the buffer.  When dequeuing objects from the buffer, we will "prime" the
+buffer if the current length drops below the low threshold.  Priming means
+popping objects from the stack and enqueing them into the buffer.  While
+priming, we will fill it only until the high threshold is reached.
+
+To measure the effectiveness of the buffer, some benchmark programs were run
+with a trace log of memory location prefetch and access instructions.  The
+prefetch window for each object processed was computed from the trace log.
+Each enqueue and dequeue operation were treated as taking one unit of time.
+Time to actually process the object was assumed to be zero.  A histogram of the
+windows is shown below.  These traces suggest the buffer length is mostly being
+kept between the low and high thresholds, which is what we want.  Variations of
+the buffer parameters were benchmarked and the best performing parameters were
+chosen.  Obviously it is unlikely these parameters will be optimal for all
+hardware and programs.
+
+```
+Prefetch window stats
+mean 52.1
+median 14.0
+max 256
+   25.60 |65,304  | ******************************
+   51.20 |5,590   | **
+   76.80 |3,562   | *
+  102.40 |2,683   | *
+  128.00 |2,278   | *
+  153.60 |2,285   | *
+  179.20 |2,377   | *
+  204.80 |2,238   | *
+  230.40 |2,753   | *
+  256.00 |5,930   | **
+-------- |------- | -------
+      N= |95,000
+```
+
+Using software prefetch instructions is only a win if the set of objects being
+examined by the GC does not fit into CPU caches.  Otherwise, using the buffer
+and prefetch instructions is just overhead.  Using the long lived object count
+seems a good estimate of if things will fit in the cache.  On 64-bit platforms,
+the minimum object size is 32 bytes.  A 4MB L2 cache would hold about 130,000
+objects.
+
+The current threshold for enabling prefetch is that the number of long-lived
+objects must exceed 200,000.  Based on benchmarking, this seems in the range
+where prefetch becomes a net gain.  Obviously it depends on hardware details
+and also the "shape" of the object graph.  For example, your object graph may
+be constructed by linearly allocating objects in memory.  Then, traversing the
+object graph might naturally result in mostly ordered memory access.  In that
+case, the hardware prefetch is likely to do a nearly perfect job without any
+software prefetch hints.
+
+This optimization, as of March 2025, was tuned on the following hardware
+platforms:
+
+- Apple M3 Pro, 32 GB RAM, 192+128 KB L1, 16 MB L2, compiled with Clang 19
+- AMD Ryzen 5 7600X, 64 GB RAM, 384 KB L1, 6 GB L2, 32 MB L3, compiled with GCC 12.2.0
+
+Benchmarking the effectiveness of this optimization is particularly difficult.
+It depends both on hardware details, like CPU cache sizes and memory latencies,
+and the specifics of the program's memory access patterns, where objects are
+located in memory and in what order they are accessed during the mark alive
+phase.  When the program's memory access patterns are favourable, working set
+of data larger than the CPU cache, objects allocated in such a way that access
+order is not linear, then the speedup from using software prefetching is in the
+range of 20% to 40% faster for the entire full GC collection.
+
+
 Optimization: reusing fields to save memory
 ===========================================