No Bit Left Behind: The Limits of Heap Data Compression

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No Bit Left Behind The Limits of Heap Data
Compression

• Jennifer B. Sartor
• Martin Hirzel, Kathryn S. McKinley

U Texas at Austin, IBM Watson
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Current State

• Managed languages ubiquitous

• Embedded devices

• Multicore

• Need memory efficiency!

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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
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• OPPORTUNITY
• Memory layout abstraction
• (Location size) ! identity

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Related Work
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Limit Study

• Quantitatively compare heap data compression
• Surveyed literature
• Savings equations
• Methodology for evaluation
• Apples-to-apples comparison
• Future work implementation
• Hybrid techniques

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• Findings array hybrid compression

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Hybrid Array Compression

• Redundancy

• Equal array sharing

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Equal Object Sharing

• Marinov OCallahan. 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

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• Class C, N objects, D distinct save

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Hybrid Array Compression

• Redundancy

• Equal array sharing

• Value set indirection

Dictionary
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Value Set Indirection Caching

• Cooprider Regehr/ Titzer, et al. PLDI 07
• For object field or array elements with large
  range of values
• Dictionary (or cache) of 256 most frequent
  values, instance stores small 1 byte indices

• If gt 256 values, 255 in dictionary, 256th says
  to store rest (M) in hashtable w/ objectID

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Hybrid Array Compression 2

• Remove zeros

• Trim trailing zeros

• Bit width reduce

• Zero compress

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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

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Hybrid Array Compression 2

• Remove zeros

• Trim trailing zeros

• Bit width reduce

• Zero compress

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Methodology
Garbage Collection
snapshot
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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
• JVMapp vs application (separated in paper)
• Per heap snapshot, and over all snapshots

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Value Indirection Cache Deep Equal Sharing Zero
Compression Hybrid Compression
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Stability of Savings
fop snapshots over time
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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!