PEP 445
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pep-0445.txt
218
pep-0445.txt
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@ -25,10 +25,10 @@ Use cases:
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optimized for its Python usage
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optimized for its Python usage
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* Python running on embedded devices with low memory and slow CPU.
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* Python running on embedded devices with low memory and slow CPU.
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A custom memory allocator may be required to use efficiently the memory
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A custom memory allocator may be required to use efficiently the memory
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and/or to be able to use all memory of the device.
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and/or to be able to use all the memory of the device.
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* Debug tool to:
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* Debug tool to:
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- track memory leaks
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- track memory usage (memory leaks)
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- get the Python filename and line number where an object was allocated
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- get the Python filename and line number where an object was allocated
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- detect buffer underflow, buffer overflow and detect misuse of Python
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- detect buffer underflow, buffer overflow and detect misuse of Python
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allocator APIs (builtin Python debug hooks)
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allocator APIs (builtin Python debug hooks)
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@ -41,7 +41,7 @@ Proposal
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API changes
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API changes
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-----------
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-----------
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* Add new GIL-free memory allocator functions:
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* Add new GIL-free (no need to hold the GIL) memory allocator functions:
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- ``void* PyMem_RawMalloc(size_t size)``
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- ``void* PyMem_RawMalloc(size_t size)``
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- ``void* PyMem_RawRealloc(void *ptr, size_t new_size)``
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- ``void* PyMem_RawRealloc(void *ptr, size_t new_size)``
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@ -65,27 +65,27 @@ API changes
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void (*free) (void *ctx, void *ptr);
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void (*free) (void *ctx, void *ptr);
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} PyMemBlockAllocator;
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} PyMemBlockAllocator;
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* Add new functions to get and set memory block allocators:
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* Add new functions to get and set internal functions of ``PyMem_RawMalloc()``,
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``PyMem_RawRealloc()`` and ``PyMem_RawFree()``:
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- Get/Set internal functions of ``PyMem_RawMalloc()``,
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- ``void PyMem_GetRawAllocator(PyMemBlockAllocator *allocator)``
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``PyMem_RawRealloc()`` and ``PyMem_RawFree()``:
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- ``void PyMem_SetRawAllocator(PyMemBlockAllocator *allocator)``
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* ``void PyMem_GetRawAllocator(PyMemBlockAllocator *allocator)``
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* Add new functions to get and set internal functions of ``PyMem_Malloc()``,
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* ``void PyMem_SetRawAllocator(PyMemBlockAllocator *allocator)``
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``PyMem_Realloc()`` and ``PyMem_Free()``:
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- Get/Set internal functions of ``PyMem_Malloc()``,
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- ``void PyMem_GetAllocator(PyMemBlockAllocator *allocator)``
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``PyMem_Realloc()`` and ``PyMem_Free()``:
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- ``void PyMem_SetAllocator(PyMemBlockAllocator *allocator)``
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- ``malloc(ctx, 0)`` and ``realloc(ctx, ptr, 0)`` must not return *NULL*:
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it would be treated as an error.
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* ``void PyMem_GetAllocator(PyMemBlockAllocator *allocator)``
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* Add new functions to get and set internal functions of
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* ``void PyMem_SetAllocator(PyMemBlockAllocator *allocator)``
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``PyObject_Malloc()``,, ``PyObject_Realloc()`` and ``PyObject_Free()``:
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* ``malloc(ctx, 0)`` and ``realloc(ctx, ptr, 0)`` must not return *NULL*:
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it would be treated as an error.
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- Get/Set internal functions of ``PyObject_Malloc()``,,
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- ``void PyObject_GetAllocator(PyMemBlockAllocator *allocator)``
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``PyObject_Realloc()`` and ``PyObject_Free()``:
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- ``void PyObject_SetAllocator(PyMemBlockAllocator *allocator)``
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- ``malloc(ctx, 0)`` and ``realloc(ctx, ptr, 0)`` must not return *NULL*:
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* ``void PyObject_GetAllocator(PyMemBlockAllocator *allocator)``
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it would be treated as an error.
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* ``void PyObject_SetAllocator(PyMemBlockAllocator *allocator)``
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* Add a new ``PyMemMappingAllocator`` structure::
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* Add a new ``PyMemMappingAllocator`` structure::
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@ -94,7 +94,7 @@ API changes
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void *ctx;
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void *ctx;
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/* allocate a memory mapping */
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/* allocate a memory mapping */
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void* (*malloc) (void *ctx, size_t size);
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void* (*alloc) (void *ctx, size_t size);
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/* release a memory mapping */
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/* release a memory mapping */
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void (*free) (void *ctx, void *ptr, size_t size);
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void (*free) (void *ctx, void *ptr, size_t size);
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@ -112,6 +112,11 @@ API changes
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- ``void PyMem_SetupDebugHooks(void)``
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- ``void PyMem_SetupDebugHooks(void)``
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* The following memory allocators always returns *NULL* if size is greater
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than ``PY_SSIZE_T_MAX``: ``PyMem_RawMalloc()``, ``PyMem_RawRealloc()``,
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``PyMem_Malloc()``, ``PyMem_Realloc()``, ``PyObject_Malloc()``,
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``PyObject_Realloc()``.
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The builtin Python debug hooks were introduced in Python 2.3 and implement the
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The builtin Python debug hooks were introduced in Python 2.3 and implement the
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following checks:
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following checks:
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@ -151,8 +156,7 @@ Examples
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Use case 1: Replace Memory Allocator, keep pymalloc
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Use case 1: Replace Memory Allocator, keep pymalloc
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----------------------------------------------------
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----------------------------------------------------
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Setup your custom memory allocator, keeping pymalloc. Dummy example wasting 2
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Dummy example wasting 2 bytes per allocation, and 10 bytes per arena::
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bytes per allocation, and 10 bytes per arena::
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#include <stdlib.h>
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#include <stdlib.h>
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@ -189,18 +193,21 @@ bytes per allocation, and 10 bytes per arena::
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void setup_custom_allocator(void)
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void setup_custom_allocator(void)
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{
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{
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PyMemBlockAllocator alloc;
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PyMemBlockAllocator block;
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PyMemMappingAllocator mapping;
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alloc.ctx = &alloc_padding;
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block.ctx = &alloc_padding;
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alloc.malloc = my_malloc;
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block.malloc = my_malloc;
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alloc.realloc = my_realloc;
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block.realloc = my_realloc;
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alloc.free = my_free;
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block.free = my_free;
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PyMem_SetRawAllocator(&alloc);
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PyMem_SetRawAllocator(&block);
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PyMem_SetAllocator(&alloc);
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PyMem_SetAllocator(&block);
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_PyObject_SetArenaAllocator(&arena_padding,
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mapping.ctx = &arena_padding;
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my_alloc_arena, my_free_arena);
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mapping.alloc = my_alloc_arena;
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mapping.free = my_free_arena;
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PyMem_SetMappingAllocator(mapping);
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PyMem_SetupDebugHooks();
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PyMem_SetupDebugHooks();
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}
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}
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@ -267,9 +274,9 @@ Use case 3: Setup Allocator Hooks
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Example to setup hooks on all memory allocators::
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Example to setup hooks on all memory allocators::
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struct {
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struct {
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PyMemBlockAllocator pymem;
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PyMemBlockAllocator raw;
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PyMemBlockAllocator pymem_raw;
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PyMemBlockAllocator mem;
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PyMemBlockAllocator pyobj;
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PyMemBlockAllocator obj;
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/* ... */
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/* ... */
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} hook;
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} hook;
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@ -313,16 +320,16 @@ Example to setup hooks on all memory allocators::
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alloc.realloc = hook_realloc;
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alloc.realloc = hook_realloc;
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alloc.free = hook_free;
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alloc.free = hook_free;
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PyMem_GetRawAllocator(&hook.pymem_raw);
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PyMem_GetRawAllocator(&hook.raw);
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alloc.ctx = &hook.pymem_raw;
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alloc.ctx = &hook.raw;
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PyMem_SetRawAllocator(&alloc);
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PyMem_SetRawAllocator(&alloc);
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PyMem_GetAllocator(&hook.pymem);
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PyMem_GetAllocator(&hook.mem);
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alloc.ctx = &hook.pymem;
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alloc.ctx = &hook.mem;
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PyMem_SetAllocator(&alloc);
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PyMem_SetAllocator(&alloc);
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PyObject_GetAllocator(&hook.pyobj);
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PyObject_GetAllocator(&hook.obj);
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alloc.ctx = &hook.pyobj;
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alloc.ctx = &hook.obj;
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PyObject_SetAllocator(&alloc);
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PyObject_SetAllocator(&alloc);
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}
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}
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@ -366,8 +373,8 @@ Replace the 6 functions:
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with 2 functions with an additional *domain* argument:
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with 2 functions with an additional *domain* argument:
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* ``int Py_GetAllocator(int domain, PyMemBlockAllocator *allocator)``
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* ``int PyMem_GetBlockAllocator(int domain, PyMemBlockAllocator *allocator)``
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* ``int Py_SetAllocator(int domain, PyMemBlockAllocator *allocator)``
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* ``int PyMem_SetBlockAllocator(int domain, PyMemBlockAllocator *allocator)``
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These functions return 0 on success, or -1 if the domain is unknown.
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These functions return 0 on success, or -1 if the domain is unknown.
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@ -377,6 +384,8 @@ where domain is one of these values:
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* ``PYALLOC_PYMEM_RAW``
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* ``PYALLOC_PYMEM_RAW``
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* ``PYALLOC_PYOBJECT``
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* ``PYALLOC_PYOBJECT``
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Drawback: the caller has to check if the result is 0, or handle the error.
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PyMem_Malloc() reuses PyMem_RawMalloc() by default
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PyMem_Malloc() reuses PyMem_RawMalloc() by default
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--------------------------------------------------
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--------------------------------------------------
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@ -385,12 +394,11 @@ PyMem_Malloc() reuses PyMem_RawMalloc() by default
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``PyMem_SetRawAllocator()`` would also also patch ``PyMem_Malloc()``
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``PyMem_SetRawAllocator()`` would also also patch ``PyMem_Malloc()``
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indirectly.
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indirectly.
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Such change is less optimal, it adds another level of indirection.
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.. note::
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In the proposed implementation of this PEP (issue #3329), ``PyMem_RawMalloc()``
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In the implementation of this PEP (issue #3329),
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calls directly ``malloc()``, whereas ``PyMem_Malloc()`` returns ``NULL`` if
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``PyMem_RawMalloc(0)`` calls ``malloc(0)``,
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size is larger than ``PY_SSIZE_T_MAX``, and the default allocator of
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whereas ``PyMem_Malloc(0)`` calls ``malloc(1)``.
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``PyMem_Malloc()`` calls ``malloc(1)`` if the size is zero.
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Add a new PYDEBUGMALLOC environment variable
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Add a new PYDEBUGMALLOC environment variable
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@ -422,30 +430,58 @@ Extensions modules don't have to be recompiled with macros.
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Pass the C filename and line number
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Pass the C filename and line number
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-----------------------------------
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-----------------------------------
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Use C macros using ``__FILE__`` and ``__LINE__`` to get the C filename
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Define allocator functions using macros and use ``__FILE__`` and ``__LINE__``
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and line number of a memory allocation.
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to get the C filename and line number of a memory allocation.
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Example::
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typedef struct {
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/* user context passed as the first argument to the 3 functions */
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void *ctx;
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/* allocate a memory block */
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void* (*malloc) (void *ctx, const char *filename, int lineno,
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size_t size);
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/* allocate or resize a memory block */
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void* (*realloc) (void *ctx, const char *filename, int lineno,
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void *ptr, size_t new_size);
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/* release a memory block */
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void (*free) (void *ctx, const char *filename, int lineno,
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void *ptr);
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} PyMemBlockAllocator;
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void* _PyMem_MallocTrace(const char *filename, int lineno, size_t size);
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/* need also a function for the Python stable ABI */
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void* PyMem_Malloc(size_t size);
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#define PyMem_Malloc(size) _PyMem_MallocTrace(__FILE__, __LINE__, size)
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Passing a filename and a line number to each allocator makes the API more
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Passing a filename and a line number to each allocator makes the API more
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complex: pass 3 new arguments, instead of just a context argument, to each
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complex: pass 3 new arguments, instead of just a context argument, to each
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allocator function. The GC allocator functions should also be patched.
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allocator function. The GC allocator functions should also be patched.
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``_PyObject_GC_Malloc()`` is used in many C functions for example and so
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For example, ``_PyObject_GC_Malloc()`` is used in many C functions and so
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objects of differenet types would have the same allocation location. Such
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objects of differenet types would have the same allocation location. Such
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changes add too much complexity for a little gain.
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changes add too much complexity for a little gain.
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PyMem_Malloc() GIL-free
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GIL-free PyMem_Malloc()
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-----------------------
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-----------------------
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``PyMem_Malloc()`` must be called with the GIL held because in debug mode, it
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When Python is compiled in debug mode, ``PyMem_Malloc()`` calls indirectly ``PyObject_Malloc()`` which requires the GIL to be held.
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calls indirectly ``PyObject_Malloc()`` which requires the GIL to be held. This
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That's why ``PyMem_Malloc()`` must be called with the GIL held.
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PEP proposes to "fix" ``PyMem_Malloc()`` to make it always call ``malloc()``.
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So the "GIL must be held" restriction may be removed no ``PyMem_Malloc()``.
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This PEP proposes to "fix" ``PyMem_Malloc()`` to make it always call
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``malloc()``. So the "GIL must be held" restriction may be removed from
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``PyMem_Malloc()``.
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Allowing to call ``PyMem_Malloc()`` without holding the GIL might break
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Allowing to call ``PyMem_Malloc()`` without holding the GIL might break
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applications which setup their own allocator or their allocator hooks. Holding
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applications which setup their own allocators or allocator hooks. Holding the
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the GIL is very convinient to develop a custom allocator: no need to care of
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GIL is convinient to develop a custom allocator: no need to care of other
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other threads nor mutexes. It is also convinient for an allocator hook: Python
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threads. It is also convinient for a debug allocator hook: Python internal
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internals can be safetly inspected.
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objects can be safetly inspected.
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Calling ``PyGILState_Ensure()`` in a memory allocator may have unexpected
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Calling ``PyGILState_Ensure()`` in a memory allocator may have unexpected
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behaviour, especially at Python startup and at creation of a new Python thread
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behaviour, especially at Python startup and at creation of a new Python thread
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@ -458,10 +494,11 @@ Don't add PyMem_RawMalloc()
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Replace ``malloc()`` with ``PyMem_Malloc()``, but only if the GIL is held.
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Replace ``malloc()`` with ``PyMem_Malloc()``, but only if the GIL is held.
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Otherwise, keep ``malloc()`` unchanged.
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Otherwise, keep ``malloc()`` unchanged.
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The ``PyMem_Malloc()`` is sometimes already misused. For example, the
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The ``PyMem_Malloc()`` is used without the GIL held in some Python functions.
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``main()`` and ``Py_Main()`` functions of Python call ``PyMem_Malloc()``
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For example, the ``main()`` and ``Py_Main()`` functions of Python call
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whereas the GIL do not exist yet. In this case, ``PyMem_Malloc()`` should
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``PyMem_Malloc()`` whereas the GIL do not exist yet. In this case,
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be replaced with ``malloc()`` (or ``PyMem_RawMalloc()``).
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``PyMem_Malloc()`` should be replaced with ``malloc()`` (or
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``PyMem_RawMalloc()``).
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If an hook is used to the track memory usage, the ``malloc()`` memory will not
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If an hook is used to the track memory usage, the ``malloc()`` memory will not
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be seen. Remaining ``malloc()`` may allocate a lot of memory and so would be
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be seen. Remaining ``malloc()`` may allocate a lot of memory and so would be
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@ -478,15 +515,15 @@ There are many existing debug tools to analyze the memory. Some examples:
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`failmalloc <http://www.nongnu.org/failmalloc/>`_,
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`failmalloc <http://www.nongnu.org/failmalloc/>`_,
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etc.
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etc.
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The problem is retrieve the Python object related to a memory pointer to read
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The problem is to retrieve the Python object related to a memory pointer to read
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its type and/or content. Another issue is to retrieve the location of the
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its type and/or content. Another issue is to retrieve the location of the
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memory allocation: the C backtrace is usually useless (same reasoning than
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memory allocation: the C backtrace is usually useless (same reasoning than
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macros using ``__FILE__`` and ``__LINE__``), the Python filename and line
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macros using ``__FILE__`` and ``__LINE__``), the Python filename and line
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number (or even the Python traceback) is more useful.
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number (or even the Python traceback) is more useful.
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Classic tools are unable to introspect the Python internal to collect such
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Classic tools are unable to introspect Python internals to collect such
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information. Being able to setup a hook on allocators called with the GIL held
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information. Being able to setup a hook on allocators called with the GIL held
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allow to read a lot of useful data from Python internals.
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allow to collect a lot of useful data from Python internals.
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Add msize()
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Add msize()
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@ -514,22 +551,31 @@ Simplify the signature of allocator functions, remove the context argument:
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* ``void free(void *ptr)``
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* ``void free(void *ptr)``
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It is likely for an allocator hook to be reused for ``PyMem_SetAllocator()``
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It is likely for an allocator hook to be reused for ``PyMem_SetAllocator()``
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and ``PyObject_SetAllocator()``, but the hook must call a different function
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and ``PyObject_SetAllocator()``, or even ``PyMem_SetRawAllocator()``, but the
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depending on the allocator. The context is a convenient way to reuse the same
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hook must call a different function depending on the allocator. The context is
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allocator or hook for different APIs.
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a convenient way to reuse the same custom allocator or hook for different
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Python allocators.
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External libraries
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External libraries
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==================
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==================
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* glib: `g_mem_set_vtable()
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Python should try to reuse the same prototypes for allocator functions than
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<http://developer.gnome.org/glib/unstable/glib-Memory-Allocation.html#g-mem-set-vtable>`_
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other libraries.
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Libraries used by Python:
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* OpenSSL: `CRYPTO_set_mem_functions()
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* OpenSSL: `CRYPTO_set_mem_functions()
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<http://git.openssl.org/gitweb/?p=openssl.git;a=blob;f=crypto/mem.c;h=f7984fa958eb1edd6c61f6667f3f2b29753be662;hb=HEAD#l124>`_
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<http://git.openssl.org/gitweb/?p=openssl.git;a=blob;f=crypto/mem.c;h=f7984fa958eb1edd6c61f6667f3f2b29753be662;hb=HEAD#l124>`_
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to set memory management functions globally
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to set memory management functions globally
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||||||
* expat: `parserCreate()
|
* expat: `parserCreate()
|
||||||
<http://hg.python.org/cpython/file/cc27d50bd91a/Modules/expat/xmlparse.c#l724>`_
|
<http://hg.python.org/cpython/file/cc27d50bd91a/Modules/expat/xmlparse.c#l724>`_
|
||||||
has a per-instance memory handler
|
has a per-instance memory handler
|
||||||
|
|
||||||
|
Other libraries:
|
||||||
|
|
||||||
|
* glib: `g_mem_set_vtable()
|
||||||
|
<http://developer.gnome.org/glib/unstable/glib-Memory-Allocation.html#g-mem-set-vtable>`_
|
||||||
* libxml2: `xmlGcMemSetup() <http://xmlsoft.org/html/libxml-xmlmemory.html>`_,
|
* libxml2: `xmlGcMemSetup() <http://xmlsoft.org/html/libxml-xmlmemory.html>`_,
|
||||||
global
|
global
|
||||||
|
|
||||||
|
@ -555,8 +601,8 @@ and it is contiguous. On Windows, the heap is handled by ``HeapAlloc()`` and
|
||||||
may be discontiguous. Memory mappings are handled by ``mmap()`` on UNIX and
|
may be discontiguous. Memory mappings are handled by ``mmap()`` on UNIX and
|
||||||
``VirtualAlloc()`` on Windows, they may be discontiguous.
|
``VirtualAlloc()`` on Windows, they may be discontiguous.
|
||||||
|
|
||||||
Releasing a memory mapping gives back immediatly the memory to the system. For
|
Releasing a memory mapping gives back immediatly the memory to the system. On
|
||||||
the heap, memory is only given back to the system if it is at the end of the
|
UNIX, heap memory is only given back to the system if it is at the end of the
|
||||||
heap. Otherwise, the memory will only be given back to the system when all the
|
heap. Otherwise, the memory will only be given back to the system when all the
|
||||||
memory located after the released memory are also released.
|
memory located after the released memory are also released.
|
||||||
|
|
||||||
|
@ -564,24 +610,24 @@ To allocate memory in the heap, the allocator tries to reuse free space. If
|
||||||
there is no contiguous space big enough, the heap must be increased, even if we
|
there is no contiguous space big enough, the heap must be increased, even if we
|
||||||
have more free space than required size. This issue is called the "memory
|
have more free space than required size. This issue is called the "memory
|
||||||
fragmentation": the memory usage seen by the system may be much higher than
|
fragmentation": the memory usage seen by the system may be much higher than
|
||||||
real usage.
|
real usage. On Windows, ``HeapAlloc()`` creates a new memory mapping with
|
||||||
|
|
||||||
On Windows, ``HeapAlloc()`` creates a new memory mapping with
|
|
||||||
``VirtualAlloc()`` if there is not enough free contiguous memory.
|
``VirtualAlloc()`` if there is not enough free contiguous memory.
|
||||||
|
|
||||||
CPython has a pymalloc allocator using arenas of 256 KB for allocations smaller
|
CPython has a *pymalloc* allocator for allocations smaller than 512 bytes. This
|
||||||
than 512 bytes. This allocator is optimized for small objects with a short
|
allocator is optimized for small objects with a short lifetime. It uses memory
|
||||||
lifetime.
|
mappings called "arenas" with a fixed size of 256 KB.
|
||||||
|
|
||||||
Windows provides a `Low-fragmentation Heap
|
Other allocators:
|
||||||
<http://msdn.microsoft.com/en-us/library/windows/desktop/aa366750%28v=vs.85%29.aspx>`_.
|
|
||||||
|
|
||||||
The Linux kernel uses `slab allocation
|
* Windows provides a `Low-fragmentation Heap
|
||||||
<http://en.wikipedia.org/wiki/Slab_allocation>`_.
|
<http://msdn.microsoft.com/en-us/library/windows/desktop/aa366750%28v=vs.85%29.aspx>`_.
|
||||||
|
|
||||||
The glib library has a `Memory Slice API
|
* The Linux kernel uses `slab allocation
|
||||||
<https://developer.gnome.org/glib/unstable/glib-Memory-Slices.html>`_:
|
<http://en.wikipedia.org/wiki/Slab_allocation>`_.
|
||||||
efficient way to allocate groups of equal-sized chunks of memory
|
|
||||||
|
* The glib library has a `Memory Slice API
|
||||||
|
<https://developer.gnome.org/glib/unstable/glib-Memory-Slices.html>`_:
|
||||||
|
efficient way to allocate groups of equal-sized chunks of memory
|
||||||
|
|
||||||
|
|
||||||
Links
|
Links
|
||||||
|
|
Loading…
Reference in New Issue