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bpo-30860: Consolidate stateful runtime globals. (#3397)

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* group the (stateful) runtime globals into various topical structs
* consolidate the topical structs under a single top-level _PyRuntimeState struct
* add a check-c-globals.py script that helps identify runtime globals

Other globals are excluded (see globals.txt and check-c-globals.py).
This commit is contained in:
Eric Snow 2017-09-07 23:51:28 -06:00 • committed by GitHub
parent bab21faded
commit 2ebc5ce42a
72 changed files with 2746 additions and 1312 deletions
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5
Include/ceval.h
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@ -93,6 +93,11 @@ PyAPI_FUNC(int) Py_GetRecursionLimit(void);
PyThreadState_GET()->overflowed = 0; \
} while(0)
PyAPI_FUNC(int) _Py_CheckRecursiveCall(const char *where);
/* XXX _Py_CheckRecursionLimit should be changed to
_PyRuntime.ceval.check_recursion_limit. However, due to the macros
in which it's used, _Py_CheckRecursionLimit is stuck in the stable
ABI. It should be removed therefrom when possible.
*/
PyAPI_DATA(int) _Py_CheckRecursionLimit;
#ifdef USE_STACKCHECK

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#ifndef Py_INTERNAL_CEVAL_H
#define Py_INTERNAL_CEVAL_H
#ifdef __cplusplus
extern "C" {
#endif
#include "pyatomic.h"
#include "pythread.h"
struct _pending_calls {
unsigned long main_thread;
PyThread_type_lock lock;
/* Request for running pending calls. */
_Py_atomic_int calls_to_do;
/* Request for looking at the `async_exc` field of the current
thread state.
Guarded by the GIL. */
int async_exc;
#define NPENDINGCALLS 32
struct {
int (*func)(void *);
void *arg;
} calls[NPENDINGCALLS];
int first;
int last;
};
#include "internal/gil.h"
struct _ceval_runtime_state {
int recursion_limit;
int check_recursion_limit;
/* Records whether tracing is on for any thread. Counts the number
of threads for which tstate->c_tracefunc is non-NULL, so if the
value is 0, we know we don't have to check this thread's
c_tracefunc. This speeds up the if statement in
PyEval_EvalFrameEx() after fast_next_opcode. */
int tracing_possible;
/* This single variable consolidates all requests to break out of
the fast path in the eval loop. */
_Py_atomic_int eval_breaker;
/* Request for dropping the GIL */
_Py_atomic_int gil_drop_request;
struct _pending_calls pending;
struct _gil_runtime_state gil;
};
PyAPI_FUNC(void) _PyEval_Initialize(struct _ceval_runtime_state *);
#ifdef __cplusplus
}
#endif
#endif /* !Py_INTERNAL_CEVAL_H */

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#ifndef Py_INTERNAL_CONDVAR_H
#define Py_INTERNAL_CONDVAR_H
#ifndef _POSIX_THREADS
/* This means pthreads are not implemented in libc headers, hence the macro
not present in unistd.h. But they still can be implemented as an external
library (e.g. gnu pth in pthread emulation) */
# ifdef HAVE_PTHREAD_H
# include <pthread.h> /* _POSIX_THREADS */
# endif
#endif
#ifdef _POSIX_THREADS
/*
* POSIX support
*/
#define Py_HAVE_CONDVAR
#include <pthread.h>
#define PyMUTEX_T pthread_mutex_t
#define PyCOND_T pthread_cond_t
#elif defined(NT_THREADS)
/*
* Windows (XP, 2003 server and later, as well as (hopefully) CE) support
*
* Emulated condition variables ones that work with XP and later, plus
* example native support on VISTA and onwards.
*/
#define Py_HAVE_CONDVAR
/* include windows if it hasn't been done before */
#define WIN32_LEAN_AND_MEAN
#include <windows.h>
/* options */
/* non-emulated condition variables are provided for those that want
* to target Windows Vista. Modify this macro to enable them.
*/
#ifndef _PY_EMULATED_WIN_CV
#define _PY_EMULATED_WIN_CV 1 /* use emulated condition variables */
#endif
/* fall back to emulation if not targeting Vista */
#if !defined NTDDI_VISTA || NTDDI_VERSION < NTDDI_VISTA
#undef _PY_EMULATED_WIN_CV
#define _PY_EMULATED_WIN_CV 1
#endif
#if _PY_EMULATED_WIN_CV
typedef CRITICAL_SECTION PyMUTEX_T;
/* The ConditionVariable object. From XP onwards it is easily emulated
with a Semaphore.
Semaphores are available on Windows XP (2003 server) and later.
We use a Semaphore rather than an auto-reset event, because although
an auto-resent event might appear to solve the lost-wakeup bug (race
condition between releasing the outer lock and waiting) because it
maintains state even though a wait hasn't happened, there is still
a lost wakeup problem if more than one thread are interrupted in the
critical place. A semaphore solves that, because its state is
counted, not Boolean.
Because it is ok to signal a condition variable with no one
waiting, we need to keep track of the number of
waiting threads. Otherwise, the semaphore's state could rise
without bound. This also helps reduce the number of "spurious wakeups"
that would otherwise happen.
*/
typedef struct _PyCOND_T
{
HANDLE sem;
int waiting; /* to allow PyCOND_SIGNAL to be a no-op */
} PyCOND_T;
#else /* !_PY_EMULATED_WIN_CV */
/* Use native Win7 primitives if build target is Win7 or higher */
/* SRWLOCK is faster and better than CriticalSection */
typedef SRWLOCK PyMUTEX_T;
typedef CONDITION_VARIABLE PyCOND_T;
#endif /* _PY_EMULATED_WIN_CV */
#endif /* _POSIX_THREADS, NT_THREADS */
#endif /* Py_INTERNAL_CONDVAR_H */

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#ifndef Py_INTERNAL_GIL_H
#define Py_INTERNAL_GIL_H
#ifdef __cplusplus
extern "C" {
#endif
#include "pyatomic.h"
#include "internal/condvar.h"
#ifndef Py_HAVE_CONDVAR
#error You need either a POSIX-compatible or a Windows system!
#endif
/* Enable if you want to force the switching of threads at least
every `interval`. */
#undef FORCE_SWITCHING
#define FORCE_SWITCHING
struct _gil_runtime_state {
/* microseconds (the Python API uses seconds, though) */
unsigned long interval;
/* Last PyThreadState holding / having held the GIL. This helps us
know whether anyone else was scheduled after we dropped the GIL. */
_Py_atomic_address last_holder;
/* Whether the GIL is already taken (-1 if uninitialized). This is
atomic because it can be read without any lock taken in ceval.c. */
_Py_atomic_int locked;
/* Number of GIL switches since the beginning. */
unsigned long switch_number;
/* This condition variable allows one or several threads to wait
until the GIL is released. In addition, the mutex also protects
the above variables. */
PyCOND_T cond;
PyMUTEX_T mutex;
#ifdef FORCE_SWITCHING
/* This condition variable helps the GIL-releasing thread wait for
a GIL-awaiting thread to be scheduled and take the GIL. */
PyCOND_T switch_cond;
PyMUTEX_T switch_mutex;
#endif
};
#ifdef __cplusplus
}
#endif
#endif /* !Py_INTERNAL_GIL_H */

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#ifndef Py_INTERNAL_MEM_H
#define Py_INTERNAL_MEM_H
#ifdef __cplusplus
extern "C" {
#endif
#include "objimpl.h"
#include "pymem.h"
#ifdef WITH_PYMALLOC
#include "internal/pymalloc.h"
#endif
/* Low-level memory runtime state */
struct _pymem_runtime_state {
struct _allocator_runtime_state {
PyMemAllocatorEx mem;
PyMemAllocatorEx obj;
PyMemAllocatorEx raw;
} allocators;
#ifdef WITH_PYMALLOC
/* Array of objects used to track chunks of memory (arenas). */
struct arena_object* arenas;
/* The head of the singly-linked, NULL-terminated list of available
arena_objects. */
struct arena_object* unused_arena_objects;
/* The head of the doubly-linked, NULL-terminated at each end,
list of arena_objects associated with arenas that have pools
available. */
struct arena_object* usable_arenas;
/* Number of slots currently allocated in the `arenas` vector. */
unsigned int maxarenas;
/* Number of arenas allocated that haven't been free()'d. */
size_t narenas_currently_allocated;
/* High water mark (max value ever seen) for
* narenas_currently_allocated. */
size_t narenas_highwater;
/* Total number of times malloc() called to allocate an arena. */
size_t ntimes_arena_allocated;
poolp usedpools[MAX_POOLS];
Py_ssize_t num_allocated_blocks;
size_t serialno; /* incremented on each debug {m,re}alloc */
#endif /* WITH_PYMALLOC */
};
PyAPI_FUNC(void) _PyMem_Initialize(struct _pymem_runtime_state *);
/* High-level memory runtime state */
struct _pyobj_runtime_state {
PyObjectArenaAllocator allocator_arenas;
};
PyAPI_FUNC(void) _PyObject_Initialize(struct _pyobj_runtime_state *);
/* GC runtime state */
/* If we change this, we need to change the default value in the
signature of gc.collect. */
#define NUM_GENERATIONS 3
/*
NOTE: about the counting of long-lived objects.
To limit the cost of garbage collection, there are two strategies;
- make each collection faster, e.g. by scanning fewer objects
- do less collections
This heuristic is about the latter strategy.
In addition to the various configurable thresholds, we only trigger a
full collection if the ratio
long_lived_pending / long_lived_total
is above a given value (hardwired to 25%).
The reason is that, while "non-full" collections (i.e., collections of
the young and middle generations) will always examine roughly the same
number of objects -- determined by the aforementioned thresholds --,
the cost of a full collection is proportional to the total number of
long-lived objects, which is virtually unbounded.
Indeed, it has been remarked that doing a full collection every
<constant number> of object creations entails a dramatic performance
degradation in workloads which consist in creating and storing lots of
long-lived objects (e.g. building a large list of GC-tracked objects would
show quadratic performance, instead of linear as expected: see issue #4074).
Using the above ratio, instead, yields amortized linear performance in
the total number of objects (the effect of which can be summarized
thusly: "each full garbage collection is more and more costly as the
number of objects grows, but we do fewer and fewer of them").
This heuristic was suggested by Martin von Löwis on python-dev in
June 2008. His original analysis and proposal can be found at:
http://mail.python.org/pipermail/python-dev/2008-June/080579.html
*/
/*
NOTE: about untracking of mutable objects.
Certain types of container cannot participate in a reference cycle, and
so do not need to be tracked by the garbage collector. Untracking these
objects reduces the cost of garbage collections. However, determining
which objects may be untracked is not free, and the costs must be
weighed against the benefits for garbage collection.
There are two possible strategies for when to untrack a container:
i) When the container is created.
ii) When the container is examined by the garbage collector.
Tuples containing only immutable objects (integers, strings etc, and
recursively, tuples of immutable objects) do not need to be tracked.
The interpreter creates a large number of tuples, many of which will
not survive until garbage collection. It is therefore not worthwhile
to untrack eligible tuples at creation time.
Instead, all tuples except the empty tuple are tracked when created.
During garbage collection it is determined whether any surviving tuples
can be untracked. A tuple can be untracked if all of its contents are
already not tracked. Tuples are examined for untracking in all garbage
collection cycles. It may take more than one cycle to untrack a tuple.
Dictionaries containing only immutable objects also do not need to be
tracked. Dictionaries are untracked when created. If a tracked item is
inserted into a dictionary (either as a key or value), the dictionary
becomes tracked. During a full garbage collection (all generations),
the collector will untrack any dictionaries whose contents are not
tracked.
The module provides the python function is_tracked(obj), which returns
the CURRENT tracking status of the object. Subsequent garbage
collections may change the tracking status of the object.
Untracking of certain containers was introduced in issue #4688, and
the algorithm was refined in response to issue #14775.
*/
struct gc_generation {
PyGC_Head head;
int threshold; /* collection threshold */
int count; /* count of allocations or collections of younger
generations */
};
/* Running stats per generation */
struct gc_generation_stats {
/* total number of collections */
Py_ssize_t collections;
/* total number of collected objects */
Py_ssize_t collected;
/* total number of uncollectable objects (put into gc.garbage) */
Py_ssize_t uncollectable;
};
struct _gc_runtime_state {
/* List of objects that still need to be cleaned up, singly linked
* via their gc headers' gc_prev pointers. */
PyObject *trash_delete_later;
/* Current call-stack depth of tp_dealloc calls. */
int trash_delete_nesting;
int enabled;
int debug;
/* linked lists of container objects */
struct gc_generation generations[NUM_GENERATIONS];
PyGC_Head *generation0;
struct gc_generation_stats generation_stats[NUM_GENERATIONS];
/* true if we are currently running the collector */
int collecting;
/* list of uncollectable objects */
PyObject *garbage;
/* a list of callbacks to be invoked when collection is performed */
PyObject *callbacks;
/* This is the number of objects that survived the last full
collection. It approximates the number of long lived objects
tracked by the GC.
(by "full collection", we mean a collection of the oldest
generation). */
Py_ssize_t long_lived_total;
/* This is the number of objects that survived all "non-full"
collections, and are awaiting to undergo a full collection for
the first time. */
Py_ssize_t long_lived_pending;
};
PyAPI_FUNC(void) _PyGC_Initialize(struct _gc_runtime_state *);
#define _PyGC_generation0 _PyRuntime.gc.generation0
#ifdef __cplusplus
}
#endif
#endif /* !Py_INTERNAL_MEM_H */

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/* An object allocator for Python.
Here is an introduction to the layers of the Python memory architecture,
showing where the object allocator is actually used (layer +2), It is
called for every object allocation and deallocation (PyObject_New/Del),
unless the object-specific allocators implement a proprietary allocation
scheme (ex.: ints use a simple free list). This is also the place where
the cyclic garbage collector operates selectively on container objects.
Object-specific allocators
_____ ______ ______ ________
[ int ] [ dict ] [ list ] ... [ string ] Python core |
+3 | <----- Object-specific memory -----> | <-- Non-object memory --> |
_______________________________ | |
[ Python's object allocator ] | |
+2 | ####### Object memory ####### | <------ Internal buffers ------> |
______________________________________________________________ |
[ Python's raw memory allocator (PyMem_ API) ] |
+1 | <----- Python memory (under PyMem manager's control) ------> | |
__________________________________________________________________
[ Underlying general-purpose allocator (ex: C library malloc) ]
0 | <------ Virtual memory allocated for the python process -------> |
=========================================================================
_______________________________________________________________________
[ OS-specific Virtual Memory Manager (VMM) ]
-1 | <--- Kernel dynamic storage allocation & management (page-based) ---> |
__________________________________ __________________________________
[ ] [ ]
-2 | <-- Physical memory: ROM/RAM --> | | <-- Secondary storage (swap) --> |
*/
/*==========================================================================*/
/* A fast, special-purpose memory allocator for small blocks, to be used
on top of a general-purpose malloc -- heavily based on previous art. */
/* Vladimir Marangozov -- August 2000 */
/*
* "Memory management is where the rubber meets the road -- if we do the wrong
* thing at any level, the results will not be good. And if we don't make the
* levels work well together, we are in serious trouble." (1)
*
* (1) Paul R. Wilson, Mark S. Johnstone, Michael Neely, and David Boles,
* "Dynamic Storage Allocation: A Survey and Critical Review",
* in Proc. 1995 Int'l. Workshop on Memory Management, September 1995.
*/
#ifndef Py_INTERNAL_PYMALLOC_H
#define Py_INTERNAL_PYMALLOC_H
/* #undef WITH_MEMORY_LIMITS */ /* disable mem limit checks */
/*==========================================================================*/
/*
* Allocation strategy abstract:
*
* For small requests, the allocator sub-allocates <Big> blocks of memory.
* Requests greater than SMALL_REQUEST_THRESHOLD bytes are routed to the
* system's allocator.
*
* Small requests are grouped in size classes spaced 8 bytes apart, due
* to the required valid alignment of the returned address. Requests of
* a particular size are serviced from memory pools of 4K (one VMM page).
* Pools are fragmented on demand and contain free lists of blocks of one
* particular size class. In other words, there is a fixed-size allocator
* for each size class. Free pools are shared by the different allocators
* thus minimizing the space reserved for a particular size class.
*
* This allocation strategy is a variant of what is known as "simple
* segregated storage based on array of free lists". The main drawback of
* simple segregated storage is that we might end up with lot of reserved
* memory for the different free lists, which degenerate in time. To avoid
* this, we partition each free list in pools and we share dynamically the
* reserved space between all free lists. This technique is quite efficient
* for memory intensive programs which allocate mainly small-sized blocks.
*
* For small requests we have the following table:
*
* Request in bytes Size of allocated block Size class idx
* ----------------------------------------------------------------
* 1-8 8 0
* 9-16 16 1
* 17-24 24 2
* 25-32 32 3
* 33-40 40 4
* 41-48 48 5
* 49-56 56 6
* 57-64 64 7
* 65-72 72 8
* ... ... ...
* 497-504 504 62
* 505-512 512 63
*
* 0, SMALL_REQUEST_THRESHOLD + 1 and up: routed to the underlying
* allocator.
*/
/*==========================================================================*/
/*
* -- Main tunable settings section --
*/
/*
* Alignment of addresses returned to the user. 8-bytes alignment works
* on most current architectures (with 32-bit or 64-bit address busses).
* The alignment value is also used for grouping small requests in size
* classes spaced ALIGNMENT bytes apart.
*
* You shouldn't change this unless you know what you are doing.
*/
#define ALIGNMENT 8 /* must be 2^N */
#define ALIGNMENT_SHIFT 3
/* Return the number of bytes in size class I, as a uint. */
#define INDEX2SIZE(I) (((unsigned int)(I) + 1) << ALIGNMENT_SHIFT)
/*
* Max size threshold below which malloc requests are considered to be
* small enough in order to use preallocated memory pools. You can tune
* this value according to your application behaviour and memory needs.
*
* Note: a size threshold of 512 guarantees that newly created dictionaries
* will be allocated from preallocated memory pools on 64-bit.
*
* The following invariants must hold:
* 1) ALIGNMENT <= SMALL_REQUEST_THRESHOLD <= 512
* 2) SMALL_REQUEST_THRESHOLD is evenly divisible by ALIGNMENT
*
* Although not required, for better performance and space efficiency,
* it is recommended that SMALL_REQUEST_THRESHOLD is set to a power of 2.
*/
#define SMALL_REQUEST_THRESHOLD 512
#define NB_SMALL_SIZE_CLASSES (SMALL_REQUEST_THRESHOLD / ALIGNMENT)
#if NB_SMALL_SIZE_CLASSES > 64
#error "NB_SMALL_SIZE_CLASSES should be less than 64"
#endif /* NB_SMALL_SIZE_CLASSES > 64 */
/*
* The system's VMM page size can be obtained on most unices with a
* getpagesize() call or deduced from various header files. To make
* things simpler, we assume that it is 4K, which is OK for most systems.
* It is probably better if this is the native page size, but it doesn't
* have to be. In theory, if SYSTEM_PAGE_SIZE is larger than the native page
* size, then `POOL_ADDR(p)->arenaindex' could rarely cause a segmentation
* violation fault. 4K is apparently OK for all the platforms that python
* currently targets.
*/
#define SYSTEM_PAGE_SIZE (4 * 1024)
#define SYSTEM_PAGE_SIZE_MASK (SYSTEM_PAGE_SIZE - 1)
/*
* Maximum amount of memory managed by the allocator for small requests.
*/
#ifdef WITH_MEMORY_LIMITS
#ifndef SMALL_MEMORY_LIMIT
#define SMALL_MEMORY_LIMIT (64 * 1024 * 1024) /* 64 MB -- more? */
#endif
#endif
/*
* The allocator sub-allocates <Big> blocks of memory (called arenas) aligned
* on a page boundary. This is a reserved virtual address space for the
* current process (obtained through a malloc()/mmap() call). In no way this
* means that the memory arenas will be used entirely. A malloc(<Big>) is
* usually an address range reservation for <Big> bytes, unless all pages within
* this space are referenced subsequently. So malloc'ing big blocks and not
* using them does not mean "wasting memory". It's an addressable range
* wastage...
*
* Arenas are allocated with mmap() on systems supporting anonymous memory
* mappings to reduce heap fragmentation.
*/
#define ARENA_SIZE (256 << 10) /* 256KB */
#ifdef WITH_MEMORY_LIMITS
#define MAX_ARENAS (SMALL_MEMORY_LIMIT / ARENA_SIZE)
#endif
/*
* Size of the pools used for small blocks. Should be a power of 2,
* between 1K and SYSTEM_PAGE_SIZE, that is: 1k, 2k, 4k.
*/
#define POOL_SIZE SYSTEM_PAGE_SIZE /* must be 2^N */
#define POOL_SIZE_MASK SYSTEM_PAGE_SIZE_MASK
/*
* -- End of tunable settings section --
*/
/*==========================================================================*/
/*
* Locking
*
* To reduce lock contention, it would probably be better to refine the
* crude function locking with per size class locking. I'm not positive
* however, whether it's worth switching to such locking policy because
* of the performance penalty it might introduce.
*
* The following macros describe the simplest (should also be the fastest)
* lock object on a particular platform and the init/fini/lock/unlock
* operations on it. The locks defined here are not expected to be recursive
* because it is assumed that they will always be called in the order:
* INIT, [LOCK, UNLOCK]*, FINI.
*/
/*
* Python's threads are serialized, so object malloc locking is disabled.
*/
#define SIMPLELOCK_DECL(lock) /* simple lock declaration */
#define SIMPLELOCK_INIT(lock) /* allocate (if needed) and initialize */
#define SIMPLELOCK_FINI(lock) /* free/destroy an existing lock */
#define SIMPLELOCK_LOCK(lock) /* acquire released lock */
#define SIMPLELOCK_UNLOCK(lock) /* release acquired lock */
/* When you say memory, my mind reasons in terms of (pointers to) blocks */
typedef uint8_t pyblock;
/* Pool for small blocks. */
struct pool_header {
union { pyblock *_padding;
unsigned int count; } ref; /* number of allocated blocks */
pyblock *freeblock; /* pool's free list head */
struct pool_header *nextpool; /* next pool of this size class */
struct pool_header *prevpool; /* previous pool "" */
unsigned int arenaindex; /* index into arenas of base adr */
unsigned int szidx; /* block size class index */
unsigned int nextoffset; /* bytes to virgin block */
unsigned int maxnextoffset; /* largest valid nextoffset */
};
typedef struct pool_header *poolp;
/* Record keeping for arenas. */
struct arena_object {
/* The address of the arena, as returned by malloc. Note that 0
* will never be returned by a successful malloc, and is used
* here to mark an arena_object that doesn't correspond to an
* allocated arena.
*/
uintptr_t address;
/* Pool-aligned pointer to the next pool to be carved off. */
pyblock* pool_address;
/* The number of available pools in the arena: free pools + never-
* allocated pools.
*/
unsigned int nfreepools;
/* The total number of pools in the arena, whether or not available. */
unsigned int ntotalpools;
/* Singly-linked list of available pools. */
struct pool_header* freepools;
/* Whenever this arena_object is not associated with an allocated
* arena, the nextarena member is used to link all unassociated
* arena_objects in the singly-linked `unused_arena_objects` list.
* The prevarena member is unused in this case.
*
* When this arena_object is associated with an allocated arena
* with at least one available pool, both members are used in the
* doubly-linked `usable_arenas` list, which is maintained in
* increasing order of `nfreepools` values.
*
* Else this arena_object is associated with an allocated arena
* all of whose pools are in use. `nextarena` and `prevarena`
* are both meaningless in this case.
*/
struct arena_object* nextarena;
struct arena_object* prevarena;
};
#define POOL_OVERHEAD _Py_SIZE_ROUND_UP(sizeof(struct pool_header), ALIGNMENT)
#define DUMMY_SIZE_IDX 0xffff /* size class of newly cached pools */
/* Round pointer P down to the closest pool-aligned address <= P, as a poolp */
#define POOL_ADDR(P) ((poolp)_Py_ALIGN_DOWN((P), POOL_SIZE))
/* Return total number of blocks in pool of size index I, as a uint. */
#define NUMBLOCKS(I) \
((unsigned int)(POOL_SIZE - POOL_OVERHEAD) / INDEX2SIZE(I))
/*==========================================================================*/
/*
* This malloc lock
*/
SIMPLELOCK_DECL(_malloc_lock)
#define LOCK() SIMPLELOCK_LOCK(_malloc_lock)
#define UNLOCK() SIMPLELOCK_UNLOCK(_malloc_lock)
#define LOCK_INIT() SIMPLELOCK_INIT(_malloc_lock)
#define LOCK_FINI() SIMPLELOCK_FINI(_malloc_lock)
/*
* Pool table -- headed, circular, doubly-linked lists of partially used pools.
This is involved. For an index i, usedpools[i+i] is the header for a list of
all partially used pools holding small blocks with "size class idx" i. So
usedpools[0] corresponds to blocks of size 8, usedpools[2] to blocks of size
16, and so on: index 2*i <-> blocks of size (i+1)<<ALIGNMENT_SHIFT.
Pools are carved off an arena's highwater mark (an arena_object's pool_address
member) as needed. Once carved off, a pool is in one of three states forever
after:
used == partially used, neither empty nor full
At least one block in the pool is currently allocated, and at least one
block in the pool is not currently allocated (note this implies a pool
has room for at least two blocks).
This is a pool's initial state, as a pool is created only when malloc
needs space.
The pool holds blocks of a fixed size, and is in the circular list headed
at usedpools[i] (see above). It's linked to the other used pools of the
same size class via the pool_header's nextpool and prevpool members.
If all but one block is currently allocated, a malloc can cause a
transition to the full state. If all but one block is not currently
allocated, a free can cause a transition to the empty state.
full == all the pool's blocks are currently allocated
On transition to full, a pool is unlinked from its usedpools[] list.
It's not linked to from anything then anymore, and its nextpool and
prevpool members are meaningless until it transitions back to used.
A free of a block in a full pool puts the pool back in the used state.
Then it's linked in at the front of the appropriate usedpools[] list, so
that the next allocation for its size class will reuse the freed block.
empty == all the pool's blocks are currently available for allocation
On transition to empty, a pool is unlinked from its usedpools[] list,
and linked to the front of its arena_object's singly-linked freepools list,
via its nextpool member. The prevpool member has no meaning in this case.
Empty pools have no inherent size class: the next time a malloc finds
an empty list in usedpools[], it takes the first pool off of freepools.
If the size class needed happens to be the same as the size class the pool
last had, some pool initialization can be skipped.
Block Management
Blocks within pools are again carved out as needed. pool->freeblock points to
the start of a singly-linked list of free blocks within the pool. When a
block is freed, it's inserted at the front of its pool's freeblock list. Note
that the available blocks in a pool are *not* linked all together when a pool
is initialized. Instead only "the first two" (lowest addresses) blocks are
set up, returning the first such block, and setting pool->freeblock to a
one-block list holding the second such block. This is consistent with that
pymalloc strives at all levels (arena, pool, and block) never to touch a piece
of memory until it's actually needed.
So long as a pool is in the used state, we're certain there *is* a block
available for allocating, and pool->freeblock is not NULL. If pool->freeblock
points to the end of the free list before we've carved the entire pool into
blocks, that means we simply haven't yet gotten to one of the higher-address
blocks. The offset from the pool_header to the start of "the next" virgin
block is stored in the pool_header nextoffset member, and the largest value
of nextoffset that makes sense is stored in the maxnextoffset member when a
pool is initialized. All the blocks in a pool have been passed out at least
once when and only when nextoffset > maxnextoffset.
Major obscurity: While the usedpools vector is declared to have poolp
entries, it doesn't really. It really contains two pointers per (conceptual)
poolp entry, the nextpool and prevpool members of a pool_header. The
excruciating initialization code below fools C so that
usedpool[i+i]
"acts like" a genuine poolp, but only so long as you only reference its
nextpool and prevpool members. The "- 2*sizeof(block *)" gibberish is
compensating for that a pool_header's nextpool and prevpool members
immediately follow a pool_header's first two members:
union { block *_padding;
uint count; } ref;
block *freeblock;
each of which consume sizeof(block *) bytes. So what usedpools[i+i] really
contains is a fudged-up pointer p such that *if* C believes it's a poolp
pointer, then p->nextpool and p->prevpool are both p (meaning that the headed
circular list is empty).
It's unclear why the usedpools setup is so convoluted. It could be to
minimize the amount of cache required to hold this heavily-referenced table
(which only *needs* the two interpool pointer members of a pool_header). OTOH,
referencing code has to remember to "double the index" and doing so isn't
free, usedpools[0] isn't a strictly legal pointer, and we're crucially relying
on that C doesn't insert any padding anywhere in a pool_header at or before
the prevpool member.
**************************************************************************** */
#define MAX_POOLS (2 * ((NB_SMALL_SIZE_CLASSES + 7) / 8) * 8)
/*==========================================================================
Arena management.
`arenas` is a vector of arena_objects. It contains maxarenas entries, some of
which may not be currently used (== they're arena_objects that aren't
currently associated with an allocated arena). Note that arenas proper are
separately malloc'ed.
Prior to Python 2.5, arenas were never free()'ed. Starting with Python 2.5,
we do try to free() arenas, and use some mild heuristic strategies to increase
the likelihood that arenas eventually can be freed.
unused_arena_objects
This is a singly-linked list of the arena_objects that are currently not
being used (no arena is associated with them). Objects are taken off the
head of the list in new_arena(), and are pushed on the head of the list in
PyObject_Free() when the arena is empty. Key invariant: an arena_object
is on this list if and only if its .address member is 0.
usable_arenas
This is a doubly-linked list of the arena_objects associated with arenas
that have pools available. These pools are either waiting to be reused,
or have not been used before. The list is sorted to have the most-
allocated arenas first (ascending order based on the nfreepools member).
This means that the next allocation will come from a heavily used arena,
which gives the nearly empty arenas a chance to be returned to the system.
In my unscientific tests this dramatically improved the number of arenas
that could be freed.
Note that an arena_object associated with an arena all of whose pools are
currently in use isn't on either list.
*/
/* How many arena_objects do we initially allocate?
* 16 = can allocate 16 arenas = 16 * ARENA_SIZE = 4MB before growing the
* `arenas` vector.
*/
#define INITIAL_ARENA_OBJECTS 16
#endif /* Py_INTERNAL_PYMALLOC_H */

92
Include/internal/pystate.h Normal file
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@ -0,0 +1,92 @@
#ifndef Py_INTERNAL_PYSTATE_H
#define Py_INTERNAL_PYSTATE_H
#ifdef __cplusplus
extern "C" {
#endif
#include "pystate.h"
#include "pyatomic.h"
#include "pythread.h"
#include "internal/mem.h"
#include "internal/ceval.h"
#include "internal/warnings.h"
/* GIL state */
struct _gilstate_runtime_state {
int check_enabled;
/* Assuming the current thread holds the GIL, this is the
PyThreadState for the current thread. */
_Py_atomic_address tstate_current;
PyThreadFrameGetter getframe;
/* The single PyInterpreterState used by this process'
GILState implementation
*/
/* TODO: Given interp_main, it may be possible to kill this ref */
PyInterpreterState *autoInterpreterState;
int autoTLSkey;
};
/* hook for PyEval_GetFrame(), requested for Psyco */
#define _PyThreadState_GetFrame _PyRuntime.gilstate.getframe
/* Issue #26558: Flag to disable PyGILState_Check().
If set to non-zero, PyGILState_Check() always return 1. */
#define _PyGILState_check_enabled _PyRuntime.gilstate.check_enabled
/* Full Python runtime state */
typedef struct pyruntimestate {
int initialized;
int core_initialized;
PyThreadState *finalizing;
struct pyinterpreters {
PyThread_type_lock mutex;
PyInterpreterState *head;
PyInterpreterState *main;
/* _next_interp_id is an auto-numbered sequence of small
integers. It gets initialized in _PyInterpreterState_Init(),
which is called in Py_Initialize(), and used in
PyInterpreterState_New(). A negative interpreter ID
indicates an error occurred. The main interpreter will
always have an ID of 0. Overflow results in a RuntimeError.
If that becomes a problem later then we can adjust, e.g. by
using a Python int. */
int64_t next_id;
} interpreters;
#define NEXITFUNCS 32
void (*exitfuncs[NEXITFUNCS])(void);
int nexitfuncs;
void (*pyexitfunc)(void);
struct _pyobj_runtime_state obj;
struct _gc_runtime_state gc;
struct _pymem_runtime_state mem;
struct _warnings_runtime_state warnings;
struct _ceval_runtime_state ceval;
struct _gilstate_runtime_state gilstate;
// XXX Consolidate globals found via the check-c-globals script.
} _PyRuntimeState;
PyAPI_DATA(_PyRuntimeState) _PyRuntime;
PyAPI_FUNC(void) _PyRuntimeState_Init(_PyRuntimeState *);
PyAPI_FUNC(void) _PyRuntimeState_Fini(_PyRuntimeState *);
#define _Py_CURRENTLY_FINALIZING(tstate) \
(_PyRuntime.finalizing == tstate)
/* Other */
PyAPI_FUNC(void) _PyInterpreterState_Enable(_PyRuntimeState *);
#ifdef __cplusplus
}
#endif
#endif /* !Py_INTERNAL_PYSTATE_H */

21
Include/internal/warnings.h Normal file
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@ -0,0 +1,21 @@
#ifndef Py_INTERNAL_WARNINGS_H
#define Py_INTERNAL_WARNINGS_H
#ifdef __cplusplus
extern "C" {
#endif
#include "object.h"
struct _warnings_runtime_state {
/* Both 'filters' and 'onceregistry' can be set in warnings.py;
get_warnings_attr() will reset these variables accordingly. */
PyObject *filters; /* List */
PyObject *once_registry; /* Dict */
PyObject *default_action; /* String */
long filters_version;
};
#ifdef __cplusplus
}
#endif
#endif /* !Py_INTERNAL_WARNINGS_H */

2
Include/object.h
View file

@ -1038,8 +1038,6 @@ with the call stack never exceeding a depth of PyTrash_UNWIND_LEVEL.
Kept for binary compatibility of extensions using the stable ABI. */
PyAPI_FUNC(void) _PyTrash_deposit_object(PyObject*);
PyAPI_FUNC(void) _PyTrash_destroy_chain(void);
PyAPI_DATA(int) _PyTrash_delete_nesting;
PyAPI_DATA(PyObject *) _PyTrash_delete_later;
#endif /* !Py_LIMITED_API */
/* The new thread-safe private API, invoked by the macros below. */

2
Include/pylifecycle.h
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@ -119,7 +119,7 @@ PyAPI_FUNC(void) _PyType_Fini(void);
PyAPI_FUNC(void) _Py_HashRandomization_Fini(void);
PyAPI_FUNC(void) PyAsyncGen_Fini(void);
PyAPI_DATA(PyThreadState *) _Py_Finalizing;
PyAPI_FUNC(int) _Py_IsFinalizing(void);
#endif
/* Signals */

30
Include/pystate.h
View file

@ -29,9 +29,10 @@ typedef struct {
int use_hash_seed;
unsigned long hash_seed;
int _disable_importlib; /* Needed by freeze_importlib */
char *allocator;
} _PyCoreConfig;
#define _PyCoreConfig_INIT {0, -1, 0, 0}
#define _PyCoreConfig_INIT {0, -1, 0, 0, NULL}
/* Placeholders while working on the new configuration API
*
@ -57,6 +58,19 @@ typedef struct _is {
PyObject *builtins;
PyObject *importlib;
/* Used in Python/sysmodule.c. */
int check_interval;
PyObject *warnoptions;
PyObject *xoptions;
/* Used in Modules/_threadmodule.c. */
long num_threads;
/* Support for runtime thread stack size tuning.
A value of 0 means using the platform's default stack size
or the size specified by the THREAD_STACK_SIZE macro. */
/* Used in Python/thread.c. */
size_t pythread_stacksize;
PyObject *codec_search_path;
PyObject *codec_search_cache;
PyObject *codec_error_registry;
@ -190,9 +204,6 @@ typedef struct _ts {
#endif
#ifndef Py_LIMITED_API
PyAPI_FUNC(void) _PyInterpreterState_Init(void);
#endif /* !Py_LIMITED_API */
PyAPI_FUNC(PyInterpreterState *) PyInterpreterState_New(void);
PyAPI_FUNC(void) PyInterpreterState_Clear(PyInterpreterState *);
PyAPI_FUNC(void) PyInterpreterState_Delete(PyInterpreterState *);
@ -249,7 +260,7 @@ PyAPI_FUNC(int) PyThreadState_SetAsyncExc(unsigned long, PyObject *);
/* Assuming the current thread holds the GIL, this is the
PyThreadState for the current thread. */
#ifdef Py_BUILD_CORE
PyAPI_DATA(_Py_atomic_address) _PyThreadState_Current;
# define _PyThreadState_Current _PyRuntime.gilstate.tstate_current
# define PyThreadState_GET() \
((PyThreadState*)_Py_atomic_load_relaxed(&_PyThreadState_Current))
#else
@ -303,10 +314,6 @@ PyAPI_FUNC(void) PyGILState_Release(PyGILState_STATE);
PyAPI_FUNC(PyThreadState *) PyGILState_GetThisThreadState(void);
#ifndef Py_LIMITED_API
/* Issue #26558: Flag to disable PyGILState_Check().
If set to non-zero, PyGILState_Check() always return 1. */
PyAPI_DATA(int) _PyGILState_check_enabled;
/* Helper/diagnostic function - return 1 if the current thread
currently holds the GIL, 0 otherwise.
@ -341,11 +348,6 @@ PyAPI_FUNC(PyThreadState *) PyThreadState_Next(PyThreadState *);
typedef struct _frame *(*PyThreadFrameGetter)(PyThreadState *self_);
#endif
/* hook for PyEval_GetFrame(), requested for Psyco */
#ifndef Py_LIMITED_API
PyAPI_DATA(PyThreadFrameGetter) _PyThreadState_GetFrame;
#endif
#ifdef __cplusplus
}
#endif