1. No Bit Left Behind: The Limits of Heap Data Compression
Jennifer B. Sartor*
Martin Hirzel†, Kathryn S. McKinley*
*U Texas at Austin, †IBM Watson

2. Current State CPU L1 L2 CPU L1 L2 Managed languages ubiquitous
Embedded devices
Multicore
CPU L1 L2
CPU L1 L2
Need memory efficiency!

3. Memory Efficiency of Managed Languages
COST
8-94% information content in heap in 37 benchmarks. [Mitchell & Sevitsky, OOPSLA 07]
Boxed objects
Trailing zeros in arrays
Redundant objects
Extra bit-width
Data structure back-bones
bzip2
86%
OPPORTUNITY
Memory layout abstraction
(Location + size) != identity

4. Related Work Ananian & Rinard. LCTES 03 Equal obj sharing
Appel & Goncalves. Tech Report 93
Dom value field hash, const field elide, Bit-width
Chen, Kandemir & Irwin. VEE 05
Dom value field elide
Chen, et al. OOPSLA 03
Zero compr, Trail zero trim
Cooprider & Regehr. PLDI 07
Value set indirection
Marinov & O’Callahan. OOPSLA 03
Eql obj sharing
Stephenson, Babb & Amarasinghe. PLDI 00
Const field elide, Bit-width reduction
Titzer, et al. PLDI 07
Zilles. ISMM 07
Bit-width reduction

5. Limit Study 58% Quantitatively compare heap data compression
Surveyed literature
Savings equations
Methodology for evaluation
Apples-to-apples comparison
Future work: implementation
Hybrid techniques
58%
Findings: array & hybrid compression

6. Compression Example Redundancy x0001 x0058 x0004 x0000 x0001 x0058
Equal array sharing

7. Equal Object Sharing 14% Two objects are equal if both
Marinov & O’Callahan. OOPSLA 03;
Appel & Goncalves. Tech Report 93
Two objects are equal if both
Same class & all fields have same value
Strictly-equal: pointer fields identical
Deep: objects pointer targets are equal
JVM store only 1 copy in hashtable
14%
Class C, N objects, D distinct; save:

8. Compression Example Redundancy x0001 x0058 x0004 x0000 Dictionary:
Equal array sharing
Value set indirection
Dictionary:
x0001
x0058
x0004
x0000 1 2 3

9. Value Set Indirection & Caching
Cooprider,Regehr’07/ Titzer,Palsberg’07
For object field or array elements with large range of values
Dictionary 256 distinct values, instance stores small 1 byte indices
If > 256 values, 255 in dictionary, 256th says to store rest (M) in hashtable w/ objectID
14%

10. Compression Example x00A0 x0073 x0002 x0001 x0101 x0000 x00A0 x0073
Trim trailing zeros
x00A0
x0073
x0002
x0001
x0101 8 5
Bit width reduce
x0A0
x073
x002
x001
x101 8 5
Zero compress
x0A
x73 x2
x001
x101 8 5
xAF 8 5

11. Zero-based Object Compression
Chen, et al. OOPSLA 03
Remove bytes that are entirely zero
Per object bit-map: 1 bit per byte
Store only non-zero bytes
Savings:
45%

12. Compression Example x00A0 x0073 x0002 x0001 x0101 x0000 x00A0 x0073
Trim trailing zeros
x00A0
x0073
x0002
x0001
x0101 8 5
Bit width reduce
x0A0
x073
x002
x001
x101 8 5
Zero compress
x0A
x73 x2
x001
x101 8 5
xAF

13. Analysis representation
Methodology
Garbage
Collection
Program run
Heap dump series
Analysis representation t

Model 1
–
Model n …
 s
Limit savings
snapshot

14. Experimental Details Jikes Research Virtual Machine
Java-in-Java
DaCapo benchmarks + pseudojbb
20-25 heap snapshots per benchmark
MarkSweep with 2x min heap
Analysis
Per class
Objects and arrays separated
JVM+app vs application (separated in paper)
Per heap snapshot, and over all snapshots

15. Technique Class Array GC/Run Lempel-Ziv compression X GC
Strictly-equal object sharing
Obj
Type
Deep-equal object sharing
Zero-based object compression
Inst
Trailing zero array trimming
Bit-width reduction
Fld
Dominant-value field hashing
Lazy invariant computation
Value set indirection
Value set caching
Constant field elision
Run
Dominant-value field elision

16. Savings (average over all benchmarks)

17. Stability of Savings
fop: snapshots over time

18. Conclusions
Limit study compare apples-to-apples heap data compression techniques
Potential to reduce memory inefficiencies in managed languages
Arrays
Hybrids
Future: save space
Challenge: efficient detection & recovery
Thank you!

19. Value Indirection & Cache
Deep Equal Sharing
Zero Compression
Hybrid Compression