Garbage collection

1
Garbage collection

• David Walker
• CS 320

2
Where are we?

• Last time A survey of common garbage collection
  techniques
• Manual memory management
• Reference counting (Appel 13.2)
• Copying collection (Appel 13.3)
• Generational collection (Appel 13.4)
• Bakers algorithm (Appel 13.6)
• Today
• Mark-sweep collection (Appel 13.1)
• Conservative collection
• Compiler interface (13.7)

3
Recap Copying GC

• Organized with to-space from-space
• Chenys algorithm
• Traverse data breadth first, copying objects from
  from-space to to-space
• next pointer where to copy
• scan pointer what to copy next

root
next
scan
4
Recap Copying GC

• Characteristics
• Automatic compaction
• Requires precise pointer information
• supplied by compiler
• Fast allocation
• no searching for free block to allocate just
  limit check and pointer bump
• Only touch reachable data. Cost
• assume copying a block costs c instructions
• assume R reachable blocks gt Rc instructions
  per collection
• assume heap has size H
• number N of words allocated in between
  collections H/2 R
• cost per word allocated Rc / N
• What does the cost look like as a function of R
  and H?

5
Recap Generational GC

• Further split into new and old generations
• minor collections (frequent) collect new
  generation
• start from roots remembered set and do copy
  collection
• dont copy objects in old generation space
• major collections collect all generations
• ordinary copy collection

root
new
remembered set
oldSize 2newSize
old
root
6
Recap Generational GC

• Further split into new and old generations
• minor collections (frequent) collect new
  generation
• start from roots remembered set and do copy
  collection
• dont copy objects in old generation space
• major collections collect all generations
• ordinary copy collection

root
new
oldSize 2newSize
old
root
7
Recap Generational GC

• Cost
• cost per word allocated Rc / N where
• copying a block costs c instructions (say c 10)
• R reachable blocks
• heap has size H
• N H/2 R
• standard copying collection
• H 4R implies 75 wasted space
• cost R10 / (4R/2 R) R10/R 10
  instructions/allocation
• generational GC
• assume young generation is 10 reachable YH 10
  R
• cost R10/(10R/2 R) 2.5 instructions/alloca
  tion
• if 0.1 MB young generation data is reachable, 1
  MB wasted space in young generation.
• if multi-generation system has 50 MB total, 1 MB
  is not much
• use larger R-H ratio in older generations
• manipulating remembered set takes time too.
• sometimes old generation is mark-sweep

8
Mark-sweep

• A two-phase algorithm
• Mark phase Depth first traversal of object graph
  from the roots to mark live data
• Sweep phase iterate over entire heap, adding
  the unmarked data back onto the free list

9
Example
r1
Free list
In use
On free list
10
Example
Mark Phase mark nodes reachable from roots
r1
Free list
In use
On free list
Marked
11
Example
Mark Phase mark nodes reachable from roots
r1
Free list
In use
On free list
Marked
12
Example
Mark Phase mark nodes reachable from roots
r1
Free list
In use
On free list
Marked
13
Example
Sweep Phase set up sweep pointer begin sweep
p
Free list
r1
In use
On free list
Marked
14
Example
Sweep Phase add unmarked blocks to free list
p
Free list
r1
In use
On free list
Marked
15
Example
Sweep Phase
p
Free list
r1
In use
On free list
Marked
16
Example
Sweep Phase retain unmark marked blocks
p
Free list
r1
In use
On free list
Marked
17
Example
Sweep Phase
p
Free list
r1
In use
On free list
Marked
18
Example
Sweep Phase
p
Free list
r1
In use
On free list
Marked
19
Example
Sweep Phase
p
Free list
r1
In use
On free list
Marked
20
Example
Sweep Phase
p
Free list
r1
In use
On free list
Marked
21
Example
Sweep Phase GC complete when heap boundary
encountered resume program
p
Free list
r1
In use
On free list
Marked
22
Cost of Mark Sweep

• Cost of mark phase
• O(R) where R is the of reachable words
• Assume cost is c1 R (c1 may be 10 instrs)
• Cost of sweep phase
• O(H) where H is the of words in entire heap
• Assume cost is c2 H (c2 may be 3 instrs)
• Analysis
• Each collection returns H - R words
• For every allocated word, we have GC cost
• ((c1 R) (c2 H)) / (H - R)
• R / H must be sufficiently small or GC cost is
  high
• Eg if R / H is larger than .5, increase heap
  size
• Mark-sweep requires extra space like copying
  collection

23
A Hidden Cost

• Depth-first search is usually implemented as a
  recursive algorithm
• Uses stack space proportional to the longest path
  in the graph of reachable objects
• one activation record/node in the path
• activation records are big
• If the heap is one long linked list, the stack
  space used in the algorithm will be greater than
  the heap size!!
• What do we do?

24
A nifty trick

• Deutsch-Schorr-Waite pointer reversal
• Rather using a recursive algorithm, reuse the
  components of the graph you are traversing to
  build an explicit stack
• This implementation trick only demands a few
  extra bits/block rather than an entire activation
  record/block
• We already needed a few extra bits per block to
  hold the mark anyway

25
DSW Algorithm
back
next

26
DSW Algorithm
back
back
next
next


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DSW Algorithm
back
back
next
next



back
next
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DSW Algorithm
back
back
next
next




back
back
next
next
29
DSW Algorithm
back
back
next
next




back
back
next
next

• extra bits needed to keep track of which
• record fields we have processed so far

30
DSW Setup

• Extra space required for sweep
• 1 bit/record to keep track of whether the record
  has been seen (the mark bit)
• f log 2 bits/record where f is the number of
  fields in the record to keep track of how many
  fields have been processed
• assume a field of each record x x.done
• Functions
• mark x sets xs mark bit
• marked x true if xs mark bit is set
• pointer x true if x is a pointer
• fields x returns number of fields in the record
  x

31
DSW Algorithm
( depth-first search in constant space )
fun dfs(next) if (pointer next) not
(marked next) then ( initialization )
while true do i next.done if i lt
(fields next) then ( process ith field
) else ( back-track to previous
record )
( call dfs passing each root as next )
( next is object being processed )
( donenext is field being processed )
32
DSW Algorithm
( depth-first search in constant space )
fun dfs(next) if (pointer next) not
(marked next) then ( initialization )
while true do i next.done if i lt
(fields next) then ( process ith field
) else ( back-track to previous
record )
back nil mark next next.done
0
33
DSW Algorithm
( depth-first search in constant space )
fun dfs(next) if (pointer next) not
(marked next) then ( initialization )
while true do i next.done if i lt
(fields next) then ( process ith field
) else ( back-track to previous
record )
y next.i if (pointer y) not
(marked y) then next.i back
back next next y mark
next next.done 0 else
next.done i 1
reuse field to store back ptr
next

back
34
DSW Algorithm
( depth-first search in constant space )
fun dfs(next) if (pointer next) not
(marked next) then ( initialization )
while true do i next.done if i lt
(fields next) then ( process ith field
) else ( back-track to previous
record )
y next.i if (pointer y) not
(marked y) then next.i back
back next next y mark
next next.done 0 else
next.done i 1
reuse field to store back ptr

back
next
35
DSW Algorithm
( depth-first search in constant space )
fun dfs(next) if (pointer next) not
(marked next) then ( initialization )
while true do i next.done if i lt
(fields next) then ( process ith field
) else ( back-track to previous
record )
y next.i if (pointer y) not
(marked y) then next.i back
back next next y mark
next next.done 0 else
next.done i 1
initialize for next iteration
36
DSW Algorithm
( depth-first search in constant space )
fun dfs(next) if (pointer next) not
(marked next) then ( initialization )
while true do i next.done if i lt
(fields next) then ( process ith field
) else ( back-track to previous
record )
y next.i if (pointer y) not
(marked y) then next.i back
back next next y mark
next next.done 0 else
next.done i 1
field is done
37
DSW Algorithm
( depth-first search in constant space )
fun dfs(next) if (pointer next) not
(marked next) then ( initialization )
while true do i next.done if i lt
(fields next) then ( process ith field
) else ( back-track to previous
record )
dfs complete
temp next next back if next nil then
return i next.done back next.i next.i
temp next.done i 1
38
DSW Algorithm
( depth-first search in constant space )
fun dfs(next) if (pointer next) not
(marked next) then ( initialization )
while true do i next.done if i lt
(fields next) then ( process ith field
) else ( back-track to previous
record )
y next next back if next nil then
return i next.done back next.i next.i
y next.done i 1
advance to next field
39
More Mark-Sweep

• Mark-sweep collectors can benefit from the tricks
  used to implement malloc/free efficiently
• multiple free lists, one size of block/list
• Mark-sweep can suffer from fragmentation
• blocks not copied and compacted like in copying
  collection
• Mark-sweep doesnt require 2x live data size to
  operate
• but if the ratio of live data to heap size is too
  large then performance suffers

40
Conservative Collection

• Even languages like C can benefit from GC
• Boehm-Weiser-Demers conservative GC uses
  heuristics to determine which objects are
  pointers and which are integers without any
  language support
• last 2 bits are non-zero gt cant be a pointer
• integer is not in allocated heap range gt cant
  be a pointer
• mark phase traverses all possible pointers
• conservative because it may retain data that
  isnt reachable
• thinks an integer is actually a pointer
• since it does not copy objects (thereby changing
  pointer values), mistaking integers for pointers
  does not hurt
• all gc is conservative anyway so this is almost
  never an issue (despite what people say)
• sound if your program doesnt manufacture
  pointers from integers by, say, using xor (using
  normal pointer arithmetic is fine)

41
Compiler Interface

• The interface to the garbage collector involves
  two main parts
• allocation code
• languages can allocate up to approx 1 word/7
  instructions
• allocation code must be blazingly fast!
• should be inlined and optimized to avoid
  call-return overhead
• gc code
• to call gc code, the program must identify the
  roots
• to traverse data, heap layout must be specified
  somehow

42
Allocation Code

• Assume size of record allocated is N
• Call alloc code
• Test next N lt limit (call gc on failure)
• Move next into function result
• Clear Mnext, ..., Mnext N 1
• next next N
• Return from alloc code
• Move result into computationally useful place
• Store useful values into Mnext,....,Mnext N
  - 1

43
Allocation Code

• Assume size of record allocated is N
• Call alloc function
• Test next N lt limit (call gc on failure)
• Move next into function result
• Clear Mnext, ..., Mnext N 1
• next next N
• Return from alloc function
• Move result into computationally useful place
• Store useful values into Mnext,....,Mnext N
  - 1

useful computation not alloc overhead
44
Allocation Code

• Assume size of record allocated is N
• Call alloc function
• Test next N lt limit (call gc on failure)
• Move next into function result
• Clear Mnext, ..., Mnext N 1
• next next N
• Return from alloc function
• Move result into computationally useful place
• Store useful values into Mnext,....,Mnext N
  - 1

inline alloc code
45
Allocation Code

• Assume size of record allocated is N
• Call alloc function
• Test next N lt limit (call gc on failure)
• Move next into computationally useful place
• Clear Mnext, ..., Mnext N 1
• next next N
• Return from alloc function
• Move next into computationally useful place
• Store useful values into Mnext,....,Mnext N
  - 1

combine moves
46
Allocation Code

• Assume size of record allocated is N
• Call alloc function
• Test next N lt limit (call gc on failure)
• Move next into computationally useful place
• Clear Mnext, ..., Mnext N 1
• next next N
• Return from alloc function
• Move next into computationally useful place
• Store useful values into Mnext,....,Mnext N
  - 1

eliminate useless store
47
Allocation Code

• Assume size of record allocated is N
• Call alloc function
• Test next N lt limit (call gc on failure)
• Move next into computationally useful place
• Clear Mnext, ..., Mnext N 1
• next next N
• Return from alloc function
• Move next into computationally useful place
• Store useful values into Mnext,....,Mnext N
  - 1

total overhead for allocation on the order of 3
instructions/alloc
48
Calling GC code

• To call the GC, program must
• identify the roots
• a GC-point, is an control-flow point where the
  garbage collector may be called
• allocation point function call
• for any GC-point, compiler generates a pointer
  map that says which registers, stack locations in
  the current frame contain pointers
• a global table maps GC-points (code addresses) to
  pointer maps
• when program calls the GC, to find all roots
• GC scans down stack, one activation record at a
  time, looking up the current pointer map for that
  record

49
Calling GC code

• To call the GC, program must
• enable GC to determine data layout of all objects
  in the heap
• for ML, Tiger, Pascal
• every record has a header with size and pointer
  info
• for Java, Modula-3
• each object has an extra field that points to
  class definition
• gc uses class definition to determine object
  layout including size and pointer info

50
Summary

• Garbage collectors are a complex and fascinating
  part of any modern language implementation
• Different collection algs have pros/cons
• explicit MM, reference counting, copying,
  generational, mark-sweep
• all methods, including explicit MM have costs
• optimizations make allocation fast, GC time,
  space and latency requirements acceptable
• read Appel Chapter 13 and be able to analyze,
  compare and contrast different GC mechanisms