Python

1. Memory Handling
&
Garbage Collection in Python
Prepared by:
Ms. Pooja Mehta
ITSNS Branch,
GTU-CDAC-BISAG ME Program,
Gandhinagar
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2. Memory Management
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 The process of binding values to memory
locations
 Whether done automatically (as in
Python), or partially by the programmer
(as in C/C++), dynamic memory
management is an important part of
programming language design.

3. Three Categories of Memory
(for Data Storage)
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 Static: storage requirements are known
prior to run time; lifetime is the entire
program execution
 Run-time stack: memory associated with
active functions
Structured as stack frames
 Heap: dynamically allocated storage; the
least organized and most dynamic storage
area

4. Cont..
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5. Cont..
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6. Memory Leaks and Garbage
Collection
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 Three types of storage
 Static
 Stack
 Heap
 Problems with heap storage:
 Memory leaks (garbage): failure to free storage when
pointers (references) are reassigned
 Dangling pointers: when storage is freed, but
references to the storage still exist.

7. Garbage
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 Any block of heap memory that cannot be
accessed by the program; i.e., there is no
stack pointer to the block; but which the
runtime system thinks is in use.
 Garbage is created in several ways:
 A function ends without returning the space
allocated to a local array or other dynamic
variable.
 A node is deleted from a linked data structure, but
isn’t freed

8. Garbage Collection
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 All inaccessible blocks of storage are identified
and returned to the free list.
 The heap may also be compacted at this time:
allocated space is compressed into one end of
the heap, leaving all free space in a large
block at the other end.

9. Implementing Automated
Garbage Collection
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 If programmers were perfect, garbage
collection wouldn’t be needed.
 There are three major approaches to
automating the process:
 Reference counting
 Mark-sweep
 Copy collection

10. Reference Counting
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 Prior to Python version 2.0, the Python
interpreter only used reference counting for
memory management.
 Reference counting works by counting the
number of times an object is referenced by
other objects in the system.
 When references to an object are removed,
the reference count for an object is
decremented.
 When the reference count becomes zero the
object is de allocated.
3 March 2016By: Pooja Mehta

11. Mark and Sweep
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 Reference counting tries to find unreachable
objects by finding objects with no incoming
references.
 Imprecise because we forget which references
those are.

12. Cont..
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 The root set is the set of memory locations in
the program that are known to be reachable.
 Any objects reachable from the root set are
reachable.
 Any objects not reachable from the root set are
not reachable.

13. Cont..
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Marking phase: Find reachable objects.
 Add the root set to a worklist.
 While the worklist isn't empty:
 Remove an object from the worklist.
 If it is not marked, mark it and add to the worklist all
objects reachable from that object.
Sweeping phase: Reclaim free memory.
 For each allocated object:
 If that object isn't marked, reclaim its memory.
 If the object is marked, unmark it (so on the next
mark-and sweep iteration we have to mark it again).

14. Copy Collection
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 Divide heap into two “semispaces”.
 Allocate from one space (fromspace) till full.
 Copy live data into other space (tospace).
 Switch roles of the spaces.
 Requires fixing pointers to moved data
(forwarding).
 Eliminates fragmentation.
 DFS improves locality, while BFS does not
require any extra storage.

15. References
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 https://docs.python.org/2/c-api/memory.html
 http://foobarnbaz.com/2012/07/08/understanding-
python-variables/
 https://www.digi.com/wiki/developer/index.php/Pyt
hon_Garbage_Collection
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Thank
you...!