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Utilizing Field Usage Patterns for Java Heap Space Optimization

Explore methods to optimize Java heap space by analyzing field usage patterns, reducing memory footprint, and implementing a heap compression mechanism.

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Utilizing Field Usage Patterns for Java Heap Space Optimization

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  1. Utilizing Field Usage Patterns for Java Heap Space Optimization Z. Guo, N. Amaral, D. Szafron and Y. Wang Department of Computing Science University of Alberta

  2. Motivation • Java has become a popular platform for mobile and embedded systems. • Prediction: 1.8 billion by 2008. • Mobile and embedded systems tend to be memory constrained: • Physics: device size, weight, power consumption. • Economics: price • Applications: multimedia streams, video games…

  3. Motivation • Memory constraints make Java programs to execute slowly or even crash. • Many researches have been done to reduce the memory footprint of Java: • Runtime environment • Code • Data

  4. Contribution • A study of the characteristics of the field usage patterns using the SpecJVM 98 benchmark. • New opportunities for space optimization. • A design of a heap compression mechanism.

  5. Field Usage Patterns • A Java class has a set of fields with a fixed layout. • However, not all fields need be stored: • A number of fields have zero or null values. • Used fields often have frequent field values. • Optimization opportunities: • Scheme-1: eliminating zero or null fields. • Scheme-2: externalizing fields with frequent values.

  6. Field-Usage Patterns • Instances of the same class often exhibit different field-usage patterns. • Definition: • The field usage pattern of an object is the set of fields used by the object. • Consider fields with frequent values: The field usage pattern of an object is the set of fields of the object that do not store frequent field values.

  7. The SpecJVM 98 benchmark • Field usage pattern distributions (Scheme-1):

  8. The SpecJVM 98 benchmark • Field usage pattern distributions (Scheme-2):

  9. The SpecJVM 98 benchmark • Field usage pattern distributions:

  10. The SpecJVM 98 benchmark • Summary: • Except raytrace, 80% instances are compressible using field usage patterns. • On average, each class is expected to have 2 to 3 fields with frequent values and its instances have 3 to 4 different field-usage patterns. • The 1st and 2nd most frequent field usage patterns account for a significant portion of field usage pattern occurrences. • Compressing non-zero frequent field values can dramatically benefit some programs, including jess and javac.

  11. Existing Compression Mechanisms • LCTES 2003. C. S. Ananian and M. Rinard. • Use hash table to record non-frequent field values. • VEE 2005. G. Chen, M. Kandemir and M. J. Irwin • Only assume a single field-usage pattern for each class. • Extra space (one word) required for objects that do not have the single field-usage pattern.

  12. An Example • In jess, a benchmark in the SpecJVM 98 suite, the class Value has four fields: _type, intval, floatval, and Objectval.

  13. Our Mechanism • Comprehensive: exploits opportunities associated with multiple field-usage patterns per class. • Aggressive: eliminates all zero/null fields and fields with frequent values. • Low space overhead: requires extra space per field usage pattern, rather than per object.

  14. Our Mechanism • Fields are classified into three levels: • Level-0: fields with no frequent values. • Level-1: fields with non-zero frequent values. • Level-2: fields with zero/null frequent values.

  15. Our Mechanism • Bidirectional object layout

  16. Our Mechanism • Meta-class

  17. Performance Evaluation • Compression effectiveness (Scheme-1 max):

  18. Performance Evaluation • Compression effectiveness (Scheme-2 max):

  19. Performance Evaluation • Runtime overhead

  20. Performance Evaluation • Summary: • Scheme-1 reduces the max heap space requirement by 12% on average. • Scheme-2 reduces the max heap space requirement by 14% on average. • A performance penalty 4.4% on average for scheme-2.

  21. Thanks

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