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Compactly Representing First-Order Structures for Static Analysis

Compactly Representing First-Order Structures for Static Analysis

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Compactly Representing First-Order Structures for Static Analysis

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  1. Compactly Representing First-Order Structures for Static Analysis Tel-Aviv University Roman Manevich Mooly Sagiv I.B.M T.J. Watson Ganesan RamalingamJohn FieldDeepak Goyal

  2. Motivation • TVLA is a powerful and general abstract interpretation system • Abstract interpretation in TVLA • Operational semantics is expressed with first-order logic formulae • Program states are represented assets of Evolving First-Order Structures • Space is a major bottleneck

  3. Desired Properties • Sparse data structures • Share common sub-structures • Inherited sharing • Incidental sharing due to program invariants • But feasible time performance • Phase sensitive data structures

  4. Outline • Background • First-order structure representations • Base representation (TVLA 0.91) • BDD representation • Empirical evaluation • Conclusion

  5. First-Order Logical Structures • Generalize shape graphs • Arbitrary set of individuals • Arbitrary set of predicates on individuals • Dynamically evolving • Usually small changes • Properties are extracted by evaluating first order formula: ∃v1 , v: x(v1) ∧ n(v1, v) • Join operator requires isomorphism testing

  6. First-Order Structure ADT • Structure : new() /* empty structure */ • SetOfNodes : nodeSet(Structure) • Node : newNode(Structure) • removeNode(Structure, node) • Kleeneeval(Structure, p(r), <u1, . . . ,ur>) • update(Structure, p(r), <u1, . . . ,ur>, Kleene) • Structurecopy(Structure)

  7. print_all Example /* list.h */typedef struct node { struct node * n; int data;} * L; /* print.c */#include “list.h”void print_all(L y) { L x;x = y; while (x != NULL) { /* assert(x != NULL) */ printf(“elem=%d”, xdata);x = xn; }}

  8. print_all Example n=½ usm=½ u1y=1 n=½ S1 n=½ usm=½ u1y=1 n=½ S0 x = y x’(v) := y(v) copy(S0) : S1 nodeset(S0) : {u1, u} eval(S0, y, u1) : 1 update(S1, x, u1, 1) x=1 eval(S0, y, u) : 0 update(S1, x, u, 0)

  9. print_all Example n=½ while (x != NULL)precondition : ∃v x(v) u1x=1y=1 usm=½ n=½ S1 n=½ x = x  nfocus : ∃v1 x(v1) ∧ n(v1, v)x’(v) := ∃v1 x(v1) ∧ n(v1, v) usm=½ u1y=1 S2.0 n=½ u1y=1 ux=1 S2.1 n=1 n=½ n=½ n=½ u.0sm=½ u1y=1 n=1 S2.2 u.1x=1

  10. Overview and Main Results • Two novel representations of first-order structures • New BDD representation • New representation using functional maps • Implementation techniques • Empirical evaluation • Comparison of different representations • Space is reduced by a factor of 4–10 • New representations scale better

  11. Base Representation (Tal Lev-Ami SAS 2000) • Two-Level Map : Predicate  (Node Tuple  Kleene) • Sparse Representation • Limited inherited sharing by “Copy-On-Write”

  12. BDDs in a Nutshell (Bryant 86) • Ordered Binary Decision Diagrams • Data structure for Boolean functions • Functions are represented as (unique) DAGs x1 x2 x2 x3 x3 x3 x3 0 0 0 1 0 1 0 1

  13. BDDs in a Nutshell (Bryant 86) • Ordered Binary Decision Diagrams • Data structure for Boolean functions • Functions are represented as (unique) DAGs • Also achieve sharing across functions x1 x1 x1 x2 x2 x2 x2 x2 x3 x3 x3 x3 x3 x3 x3 0 1 0 1 0 1 Duplicate Terminals Duplicate Nonterminals Redundant Tests

  14. Encoding Structures Using Integers • Static encoding of • Predicates • Kleene values • Dynamic encoding of nodes • 0, 1, …, n-1 • Encode predicate p’s values as • ep(p).en(u1). en(u2) . … . en(un) . ek(Kleene)

  15. x1 x2 x2 x3 0 1 BDD Representation of Integer Sets • Characteristic function • S={1,5} 1=<001>5=<101> S = (¬x1¬x2x3) (x1¬x2x3)

  16. x1 x2 x2 x3 1 BDD Representation of Integer Sets • Characteristic function • S={1,5} 1=<001>5=<101> S = (¬x1¬x2x3) (x1¬x2x3)

  17. BDD Representation Example n=½ usm=½ S0 n=½ S0 u1y=1 1

  18. BDD Representation Example n=½ usm=½ S0 S1 n=½ S0 u1y=1 x=y n=½ u1x=1y=1 usm=½ n=½ S1 1

  19. S2.2 BDD Representation Example n=½ usm=½ S0 S1 n=½ S0 u1y=1 x=y n=½ u1x=1y=1 usm=½ n=½ S1 x=xn n=½ n=½ n=½ u.0sm=½ u1y=1 n=1 S2.2 u.1x=1 1

  20. S2.2 BDD Representation Example n=½ usm=½ S0 S1 n=½ S0 u1y=1 x=y n=½ u1x=1y=1 usm=½ n=½ S1 x=xn n=½ n=½ n=½ u.0sm=½ u1y=1 n=1 S2.2 u.1x=1 1

  21. Improved BDD Representation • Using this representation directlydoesn’t save space • Observation • Node names can be arbitrarily remapped without affecting the ADT semantics • Our heuristics • Use canonic node names to encode nodes • Increases incidental sharing • Reduces isomorphism test to pointer comparison • 4-10 space reduction

  22. Reducing Time Overhead • Current implementation not optimized • Expensive formula evaluation • Hybrid representation • Distinguish between phases:mutable phase  Join  immutable phase • Dynamically switch representations

  23. Functional Representation • Alternative representation for first-order structures • Structures represented by maps from integers to Kleene values • Tailored for representing first-order structures • Achieves better results than BDDs • Techniques similar to the BDD representation • More details in the paper

  24. Empirical Evaluation • Benchmarks: • Cleanness Analysis (SAS 2000) • Garbage Collector • CMP (PLDI 2002) of Java Front-End and Kernel Benchmarks • Mobile Ambients (ESOP 2000) • Stress testing the representations • We use “relational analysis” • Save structures in every CFG location

  25. Space Results

  26. Abstract Counters • Ignore language/implementation details • A more reliable measurement technique • Count only crucial space information • Independent of C/Java

  27. Abstract Counters Results

  28. Trends in theCleanness Analysis Benchmark

  29. What’s Missing from this Work? • Investigate other node mapping heuristics • Compactly represent sets of structures • Time optimizations

  30. Conclusions • Two novel representations of first-order structures • New BDD representation • New representation using functional maps • Implementation techniques • Normalization techniques are crucial • Empirical evaluation • Comparison of different representations • Space is reduced by a factor of 4–10 • New representations scale better

  31. Conclusions • The use of BDDs for static analysis is not a panacea for space saving • Domain-specific encoding crucial for saving space • Failed attempts • Original implementation of Veith’s encoding • PAG

  32. The End