Lightweight Abstraction for Mathematical Computation in Java

1. Lightweight Abstraction for Mathematical Computation in Java PavelBourdykine and Stephen M. Watt Department of Computer ScienceWestern University London Ontario, Canada CASC 2012 Maribor, Slovenia 3-6 September 2012

2. The Context • Modern programming languages allow the creation of new abstractions • Hide implementation details • Eliminate programming errors • Allow future changes Z/5Zis different from int Z/5Z [x]is different from int[ ]

3. The Problem • In the popular scientific programming languages, these mechanisms aren’t good enough. • In C++, typedef doesn’t create a distinct type. • In Java, adding a class abstraction is very costly.

4. The Problem • In symbolic mathematical computing,we often have a great many small values. • E.g. • Poly coeffs in Z mod small prime • Packed exponent vectors • Extra storage allocation.Several 100% overhead.

5. The Problem • So libraries writers cheat…. • Compromise abstractions…. • Circumvent the system …. • The standardJava libraries use intand long values to represent • Colours, Layout strategies, Border types, etc instead of abstract types. • I.e. language features aren’t good enough.

6. Swing, undermining abstraction static Integers representing different properties ints representing different parameters

7. Swing, undermining abstraction Would like to DISTINGUISH between layer and position! legal, but does not necessarily make sense same arguments, different meanings

8. Why? • Machine-supported data types, such as single- and double-word integers and floating point numbers are primitive types in Java, and specially handled. • No storage allocation • No “object” overhead (synchronization, etc) • User-defined types must be classes inheriting from “Object”, with all the associated overhead.

9. Why? Primitive type: Problem multiplies with vectors of these things.

10. Idea! • Use the Java type system for correctness,then • Compile using primitive types.

11. Approach: Objectives • Combine type-safety and low cost • Improve performance without crippling safety and expressive power • It is about opacity • Framework for a straight forward construction • easy to use • noticeable benefits

12. Approach: Practice • Want: objects that perform like primitive types • combine the two! • Allow class derivation, inheritance, virtualization • i.e. object properties • WITHOUT the heavy overhead • Want to avoid allocation but keep the type safety • Works with ANY underlying type! • This layer of abstraction does not require its own inheritance structure!

13. What we would like But achieve this without losing performance and rewriting library functions! new objects method arguments no longer ambiguous

14. Approach: Rules and Restrictions • To keep object properties need to • keep representation field protected • follow Object-Oriented guidelines Result: Type-check the objects by name • To boost performance and eliminate overhead • keep constructor(s) private • make methods public static Result: Implement using underlying type

15. Approach: Rules and Restrictions Summary: • Rule 1Object must have a single protected field of the underlying type, unless it is a subclass of an opaque type (and then must have no fields). • Rule 2Any object constructors must be declared private. • Rule 3All methods accessing or modifying the underlying type field representation must be declared static.

16. Approach: Implementation • Annotate opaque classes • @Opaque(“type”) annotation • Type-check regular objects • Convert annotated classes to use underlying type representation • Compile the fast versions • Converter implemented in Java • Building process automated by Ant

17. Code Transformation Annotated opaque class

18. Code Transformation Converted opaque class

19. Compilation Process • Annotated code is analyzed and types recorded. • All occurrences of opaque types are substituted with the underlying representation. • New code is compiled. • Process is automated using Ant. • Compiles original code for type checking. • Backs up the original code, converts it. • Calls compiler again on converted code.

20. Performance • Test performance in terms of execution speed & memory use • Test a variety of uses and declarations • cover a wide range of possible applications • Measure: regular code vs. opaque annotated code vs. converted code

21. Performance: Example I Usual Definition: Usage:

22. Performance: Example I Opaque Definition: Usage:

23. Performance: Example I (time) • Opaque types execute about twice as fast

24. Performance: Example I (space) • Opaque types are able to reside entirely on the stack, i.e. no allocation is needed

25. Performance: Example II Regular class:

26. Performance: Example II Opaque class:

27. Performance: Example II Regular class usage: Opaque class usage:

28. Performance: Example II (time) • Opaque objects execute 12-15 times faster

29. Performance: Example II (space) • Even before conversion to underlying type opaque types use 10-12 times less memory

30. Performance: Example II Opaque class:

31. Performance: Example II Converted opaque class:

32. Performance: Example II (time) • Converted (to long[]) opaque types execute 20-25 times faster

33. Performance: Example II (space) • Converted (to long[]) opaque types use over 15 times less memory

34. Conclusions • Successfully implemented structures that are type safe and perform as machine types. • Code conversion and build process are automated. • Performance is well worth the restrictions. • Sufficient for computer algebra. Performance – native levels achieved. Safety – maintained.

35. Future work • Implement Java generics • Cover all Java language features • Algebra library using opaque types • Native implementation?