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										 |  |  | This document describes some caveats about the use of Valgrind with | 
					
						
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										 |  |  | Python.  Valgrind is used periodically by Python developers to try | 
					
						
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										 |  |  | to ensure there are no memory leaks or invalid memory reads/writes. | 
					
						
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 | 
					
						
							|  |  |  | If you don't want to read about the details of using Valgrind, there | 
					
						
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										 |  |  | are still two things you must do to suppress the warnings.  First, | 
					
						
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										 |  |  | you must use a suppressions file.  One is supplied in | 
					
						
							|  |  |  | Misc/valgrind-python.supp.  Second, you must do one of the following: | 
					
						
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 | 
					
						
							|  |  |  |   * Uncomment Py_USING_MEMORY_DEBUGGER in Objects/obmalloc.c, | 
					
						
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										 |  |  |     then rebuild Python | 
					
						
							|  |  |  |   * Uncomment the lines in Misc/valgrind-python.supp that | 
					
						
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										 |  |  |     suppress the warnings for PyObject_Free and PyObject_Realloc | 
					
						
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										 |  |  | If you want to use Valgrind more effectively and catch even more | 
					
						
							|  |  |  | memory leaks, you will need to configure python --without-pymalloc. | 
					
						
							|  |  |  | PyMalloc allocates a few blocks in big chunks and most object | 
					
						
							|  |  |  | allocations don't call malloc, they use chunks doled about by PyMalloc | 
					
						
							|  |  |  | from the big blocks.  This means Valgrind can't detect | 
					
						
							|  |  |  | many allocations (and frees), except for those that are forwarded | 
					
						
							|  |  |  | to the system malloc.  Note: configuring python --without-pymalloc | 
					
						
							|  |  |  | makes Python run much slower, especially when running under Valgrind. | 
					
						
							|  |  |  | You may need to run the tests in batches under Valgrind to keep | 
					
						
							|  |  |  | the memory usage down to allow the tests to complete.  It seems to take | 
					
						
							|  |  |  | about 5 times longer to run --without-pymalloc. | 
					
						
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										 |  |  | 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. | 
					
						
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										 |  |  | 
 | 
					
						
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										 |  |  | Details: | 
					
						
							|  |  |  | -------- | 
					
						
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										 |  |  | Python uses its own small-object allocation scheme on top of malloc, | 
					
						
							|  |  |  | called PyMalloc. | 
					
						
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 | 
					
						
							|  |  |  | Valgrind may show some unexpected results when PyMalloc is used. | 
					
						
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										 |  |  | Starting with Python 2.3, PyMalloc is used by default.  You can disable | 
					
						
							|  |  |  | PyMalloc when configuring python by adding the --without-pymalloc option. | 
					
						
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										 |  |  | If you disable PyMalloc, most of the information in this document and | 
					
						
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										 |  |  | the supplied suppressions file will not be useful.  As discussed above, | 
					
						
							|  |  |  | disabling PyMalloc can catch more problems. | 
					
						
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										 |  |  | 
 | 
					
						
							|  |  |  | If you use valgrind on a default build of Python,  you will see | 
					
						
							|  |  |  | many errors like: | 
					
						
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							|  |  |  |         ==6399== Use of uninitialised value of size 4 | 
					
						
							|  |  |  |         ==6399== at 0x4A9BDE7E: PyObject_Free (obmalloc.c:711) | 
					
						
							|  |  |  |         ==6399== by 0x4A9B8198: dictresize (dictobject.c:477) | 
					
						
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							|  |  |  | These are expected and not a problem.  Tim Peters explains | 
					
						
							|  |  |  | the situation: | 
					
						
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 | 
					
						
							|  |  |  |         PyMalloc needs to know whether an arbitrary address is one | 
					
						
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										 |  |  | 	that's managed by it, or is managed by the system malloc. | 
					
						
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										 |  |  | 	The current scheme allows this to be determined in constant | 
					
						
							|  |  |  | 	time, regardless of how many memory areas are under pymalloc's | 
					
						
							|  |  |  | 	control. | 
					
						
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 | 
					
						
							|  |  |  |         The memory pymalloc manages itself is in one or more "arenas", | 
					
						
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										 |  |  | 	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. | 
					
						
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										 |  |  | 
 | 
					
						
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										 |  |  |         Given an arbitrary address, pymalloc computes the pool base | 
					
						
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										 |  |  | 	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 | 
					
						
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 | 
					
						
							|  |  |  |             the arena base address stored at that index in the vector | 
					
						
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 | 
					
						
							|  |  |  |         to | 
					
						
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 | 
					
						
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										 |  |  |             the arbitrary address pymalloc is investigating | 
					
						
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										 |  |  | 
 | 
					
						
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										 |  |  |         pymalloc controls this arbitrary address if and only if it lies | 
					
						
							|  |  |  |         in the arena the address's pool's index claims it lies in. | 
					
						
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										 |  |  | 
 | 
					
						
							|  |  |  |         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 | 
					
						
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										 |  |  | 	(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. | 
					
						
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										 |  |  | 
 | 
					
						
							|  |  |  |         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). |