380C
• Where are we & where we are going
– Managed languages
•
•
•
•
Dynamic compilation
Inlining
Garbage collection
What else can you do when you examine the heap a
lot?
– Why you need to care about workloads
–
–
–
–
Alias analysis
Dependence analysis
Loop transformations
EDGE architectures
1
380C
lecture 18
• Garbage Collection
– Why use garbage collection?
– What is garbage?
• Reachable vs live, stack maps, etc.
– Allocators and their collection mechanisms
• Semispace
• Marksweep
• Performance comparisons
• Mark Region
– Incremental age based collection
• Write barriers: Friend or foe?
• Generational
• Beltway
2
Mark Region and Other
Advances in Garbage
Collection
PLDI’08:
Immix: A Mark-Region Collector With
Space Efficiency, Fast Collection, and Mutator Performance
Kathryn S. McKinley
University of Texas at Austin
Stephen M. Blackburn
Australian National University
Isn’t GC a bit retro?
Mark-Sweep
Mark-Compact
Semi-Space
McCarthy, 1960
Styger, 1967
Cheney, 1970
“Languages without automated garbage collection are getting
out of fashion. The chance of running into all kinds of
memory problems is gradually outweighing the performance
penalty you have to pay for garbage collection.”
Paul Jansen, managing director of TIOBE Software, in Dr Dobbs, April 2008
4
GC Fundamentals
The Time–Space Tradeoff
Time
SemiSpace
Space
5
GC Fundamentals
Time
The Time–Space Tradeoff
Space
Our Goal
6
GC Fundamentals
Algorithmic Components
Allocation
Identification
Reclamation
Sweep-to-Free
Free List
Tracing
(implicit)
Compact
`
Bump Allocation
Reference Counting
(explicit)
3
Evacuate
1
7
GC Fundamentals
Canonical Garbage Collectors
Mark-Sweep
[McCarthy 1960]
Sweep-to-Free
Free-list + trace + sweep-to-free
Mark-Compact
[Styger 1967]
Bump allocation + trace + compact
Compact
`
Evacuate
Semi-Space
[Cheney 1970]
Bump allocation + trace + evacuate
8
Mark-Sweep
Free List Allocation + Trace + Sweep-to-Free
Space
efficient
Poor locality
✓
Simple,
very fast
collection
✓
Actual data, taken from geomean of DaCapo, jvm98, and jbb2000 on 2.4GHz Core 2 Duo
9
Mark-Compact
Bump Allocation + Trace + Compact
Good
locality
✓
Space
efficient
✓
Expensive
multi-pass
collection
Actual data, taken from geomean of DaCapo, jvm98, and jbb2000 on 2.4GHz Core 2 Duo
10
Semi-Space
Bump Allocation + Trace + Evacuation
Good
locality
Space
inefficient
✓
Space
inefficient
Actual data, taken from geomean of DaCapo, jvm98, and jbb2000 on 2.4GHz Core 2 Duo
11
Mark-Region
with Sweep-To-Region
Reclamation
Mark-Sweep
Sweep-to-Free
Free-list + trace + sweep-to-free
Mark-Compact
Compact
Bump allocation + trace + compact
Semi-Space
Evacuate
`
Bump allocation + trace + evacuate
Mark-Region
Bump + trace + sweep-to-region
Sweep-to-Region
12
Mark-Region
Bump Allocation + Trace + Sweep-to-Region
Good
locality
✓
Simple,
very fast
collection
✓
Space
efficient
✓
Excellent
performance
✓
Actual data, taken from geomean of DaCapo, jvm98, and jbb2000 on 2.4GHz Core 2 Duo
13
Naïve Mark-Region
0
• Contiguous allocation into regions
 Excellent locality
– For simplicity, objects cannot span regions
• Simple mark phase (like mark-sweep)
– Mark objects and their containing region
• Unmarked regions can be freed
14
Immix
Efficient Mark-Region Garbage Collection
15
Lines and Blocks
Large Regions
✓
More contiguous allocation
✗ Fragmentation (false marking)
Small Regions
✗ Fragmentation (can’t fill blocks)
N pages
Free
✓
✗ Increased metadata o/h
✗ Constrained object sizes
Lines & Blocks
Recyclable lines
TLB locality, cache locality
Objects span lines
Less fragmentation
Free
✓
approx 1 cache line
0 lines
Recyclable
Block > 4 X max object size
Lines marked with objects
Fast common case
16
Allocation Policy
(Recycling)
• Recycle partially marked blocks first
 Minimizes fragmentation
 Maximizes sharing of freed blocks
• Recycle in address order
– We explored other options
• Allocate into free blocks last
17
Opportunistic
Defragmentation
• Opportunistically evacuate fragmented blocks
– Lightweight, uses same allocation mechanism
– No cost in common case (specialized GC)
0
• Identify source and target blocks
– (see paper for heuristics)
• Evacuate objects in source blocks
– Allocate into target blocks
• Opportunistic
– Leave in place if no space, or object pinned
18
Other Optimizations
Implicit Marking
Small objects implicitly mark next line
Large objects mark lines exactly
✓
✓
Mostmark
objects small
Line
line mark
V. Implicit
Fast common
case
Overflow Allocation
Multi-line objects may skip many small holes
Overflow allocation (used on failure)
✓ Large objects uncommon
✓ V. effective solution
✓
19
Results
Complete data available at:
http://cs.anu.edu.au/~Steve.Blackburn/pubs
20
Evaluation
20 Benchmarks
DaCapo
SPECjvm98
SPEC jbb2000
Methodology
MMTk
Jikes RVM 2.9.3
(Perf ≈ HotSpot 1.5)
Replay compiler
Discard outliers
Report 95th %ile
Collectors
Full Heap
Immix
MarkSweep
MarkCompact
SemiSpace
Generational
GenIX
GenMS
GenCopy
Sticky
StickyIX
StickyMS
Hardware
Core 2 Duo
2.4GHz, 32KB L1,
4MB L2, 2GB RAM
AMD Athlon
3500+
2.2GHz, 64KB L1,
512KB L2, 2GB
`
RAM
PowerPC 970
1.6GHz, 32KB L1,
512KB L2, 2GB
RAM
Please see the paper for details.
21
Mutator Time
Geomean of DaCapo, jvm98 and jbb2000 on 2.4GHz Core 2 Duo
22
Minimum Heap
23
GC Time
Geomean of DaCapo, jvm98 and jbb2000 on 2.4GHz Core 2 Duo
24
Total Performance
Geomean of DaCapo, jvm98 and jbb2000 on 2.4GHz Core 2 Duo
25
Generational Performance
Geomean of DaCapo, jvm98 and jbb2000 on 2.4GHz Core 2 Duo
26
Sticky Performance
Total Time (Normalized)
1.25
1.2
1.15
1.1
1.05
1
1
3
5
Heap Size (Normalized)
Geomean of DaCapo, jvm98 and jbb2000 on 2.4GHz Core 2 Duo
27
PseudoJBB 2000
On 2.4GHz Core 2 Duo
28
PseudoJBB 2000
On 2.4GHz Core 2 Duo
29
Prior Work
http://www.ibm.com/developerworks/ibm/library/i-garbage1/
• IBM product collector
– Mark-Region not characterized
– Collector not evaluated
– Product and basis for other research
• [Domani et al 2000][Kermany & Petrank 2006]
30
Mark-Region Collection
Mark-Sweep
Sweep-to-Free
Free-list + trace + sweep-to-free
Compact
Mark-Compact
Bump allocation + trace + compact
Evacuate
`
Semi-Space
Bump allocation + trace + evacuate
Sweep-to-Region
Mark-Region
Bump allocation + trace + sweep-to-region
31
Immix
Efficient Mark-Region Collection
Good
locality
✓
Simple,
very fast
collection
✓
Space
efficient
✓
Excellent
performance
✓
Actual data, taken from geomean of DaCapo, jvm98, and jbb2000 on 2.4GHz Core 2 Duo
32
Open Source
Code available in JikesRVM 2.9.3 onward.
http://www.jikesrvm.org
Complete data available at:
http://cs.anu.edu.au/~Steve.Blackburn/pubs
33
Research History
• PLDI 1998
– Clinger & Hanson postulated the
radioactive decay model for object
lifetimes
• Genesis of Older-First
– [Stefanovic, McKinley, Moss OOPSLA’99]
34
Garbage Collection
Hypotheses
• Generational hypothesis: younger objects
die quickly, so collect them first
• Older-first hypothesis: the collector can
collect less the longer it waits
Age ordered heap
Survival function s(v)
for object lifetime
distribution
s(v)
younger
0

1/2V
older
V
35
Older-first Algorithm
36
Next Steps
• Beltway
– [BJMM PLDI’02]
0
1
3
4
5
6
7
8
9
10
33
34
35
36
37
38
39
40
– Increments
– Belts
– Combines generational and older-first
• Ulterior Reference Counting
– [BM OOPSLA’03]
– Reference count on-per-object basis
– Responsiveness and throughput
• MMTk: [BCM SIGMETRICS’04 ICSE’04]
– Toolkit for building & understanding GC
– Motivated today’s work
37
Garbage Collection is the
Answer to All Your Problems
• Improves data and code locality
– [Huang et al. OOPSLA’02 ISMM’04, VEE’04]
• Cooperative GC optimizations
– Colocation [Guyer OOPSLA’05]
– Free-me [Guyer et al. PLDI’06]
• Finds leaks
– [Bond ASPLOS’06, Jump POPL’07]
• Tolerates leaks
– [Bond OOSLA’08]
• Helps with dynamic software updating!
– [Subramaniam, Hicks ??’08]
• DaCapo Benchmarks
– [Blackburn et al. OOPSLA’06 CACM’08]
38
380C
• Where are we & where we are going
– Why you need to care about workloads
– Managed languages
• Dynamic compilation
• Inlining
• Garbage collection
– Opportunity to improve data locality on-the-fly
– Read: X. Huang, S. M. Blackburn, K. S. McKinley, J. E. B. Moss, Z. Wang,
and P. Cheng, The Garbage Collection Advantage: Improving Program
Locality, ACM Conference on Object Oriented Programming, Systems,
Languages, and Applications (OOPSLA), pp. 69-80, Vancouver, Canada,
October 2004.
–
–
–
–
Alias analysis
Dependence analysis
Loop transformations
EDGE architectures