This document describes some caveats about the use of Valgrind withPython.  Valgrind is used periodically by Python developers to tryto ensure there are no memory leaks or invalid memory reads/writes. If you don't want to read about the details of using Valgrind, thereare still two things you must do to suppress the warnings.  First,you must use a suppressions file.  One is supplied inMisc/valgrind-python.supp.  Second, you must do one of the following:   * Uncomment Py_USING_MEMORY_DEBUGGER in Objects/obmalloc.c,    then rebuild Python  * Uncomment the lines in Misc/valgrind-python.supp that    suppress the warnings for PyObject_Free and PyObject_Realloc If you want to use Valgrind more effectively and catch even morememory leaks, you will need to configure python --without-pymalloc.PyMalloc allocates a few blocks in big chunks and most objectallocations don't call malloc, they use chunks doled about by PyMallocfrom the big blocks.  This means Valgrind can't detectmany allocations (and frees), except for those that are forwardedto the system malloc.  Note: configuring python --without-pymallocmakes Python run much slower, especially when running under Valgrind.You may need to run the tests in batches under Valgrind to keepthe memory usage down to allow the tests to complete.  It seems to takeabout 5 times longer to run --without-pymalloc. Apr 15, 2006:  test_ctypes causes Valgrind 3.1.1 to fail (crash).  test_socket_ssl should be skipped when running valgrind.	The reason is that it purposely uses uninitialized memory.	This causes many spurious warnings, so it's easier to just skip it.  Details:--------Python uses its own small-object allocation scheme on top of malloc,called PyMalloc. Valgrind may show some unexpected results when PyMalloc is used.Starting with Python 2.3, PyMalloc is used by default.  You can disablePyMalloc when configuring python by adding the --without-pymalloc option.If you disable PyMalloc, most of the information in this document andthe supplied suppressions file will not be useful.  As discussed above,disabling PyMalloc can catch more problems. If you use valgrind on a default build of Python,  you will seemany errors like:         ==6399== Use of uninitialised value of size 4        ==6399== at 0x4A9BDE7E: PyObject_Free (obmalloc.c:711)        ==6399== by 0x4A9B8198: dictresize (dictobject.c:477) These are expected and not a problem.  Tim Peters explainsthe situation:         PyMalloc needs to know whether an arbitrary address is one	that's managed by it, or is managed by the system malloc.	The current scheme allows this to be determined in constant	time, regardless of how many memory areas are under pymalloc's	control.         The memory pymalloc manages itself is in one or more "arenas",	each a large contiguous memory area obtained from malloc.	The base address of each arena is saved by pymalloc	in a vector.  Each arena is carved into "pools", and a field at	the start of each pool contains the index of that pool's arena's	base address in that vector.         Given an arbitrary address, pymalloc computes the pool base	address corresponding to it, then looks at "the index" stored	near there.  If the index read up is out of bounds for the	vector of arena base addresses pymalloc maintains, then	pymalloc knows for certain that this address is not under	pymalloc's control.  Otherwise the index is in bounds, and	pymalloc compares             the arena base address stored at that index in the vector         to             the arbitrary address pymalloc is investigating         pymalloc controls this arbitrary address if and only if it lies        in the arena the address's pool's index claims it lies in.         It doesn't matter whether the memory pymalloc reads up ("the	index") is initialized.  If it's not initialized, then	whatever trash gets read up will lead pymalloc to conclude	(correctly) that the address isn't controlled by it, either	because the index is out of bounds, or the index is in bounds	but the arena it represents doesn't contain the address.         This determination has to be made on every call to one of	pymalloc's free/realloc entry points, so its speed is critical	(Python allocates and frees dynamic memory at a ferocious rate	-- everything in Python, from integers to "stack frames",	lives in the heap).