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Correcting the Dynamic Call Graph Using Control Flow Constraints

This research focuses on correcting inaccuracies in the dynamic call graph to improve the performance of large object-oriented programs. It proposes the use of control flow constraints and presents static and dynamic approaches for correction. Experimental results show improved accuracy and minimal overhead.

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Correcting the Dynamic Call Graph Using Control Flow Constraints

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  1. Correcting the Dynamic Call Graph Using Control Flow Constraints Byeongcheol (BK) Lee Kevin Resnick Michael Bond Kathryn McKinley UT Austin

  2. b w1 c a w2 Dynamic call graph Motivation • Complexity of large object oriented programs • Decompose the program into small methods • Method boundary becomes performance-bottleneck • Dynamic interprocedural optimization • Solve the method boundary problem • Inlining and specialization vary the performance by factor of 2 • Dynamic call graph (DCG) is critical input!

  3. b 1,000 call b c a 500 call c DCGSample Error method a Inaccurate call graph

  4. Timer-based sampling and timing bias Call stack … … c b c b c b c c c b c c c a a t

  5. Timer-based sampling and timing bias Call stack … … c b c b c b c c c b c c c a a t

  6. Timer-based sampling and timing bias Call stack … … c b c b c b c c c b c c c a a t

  7. Timer-based sampling and timing bias Call stack … … c b c b c b c c c b c c c a a t

  8. timer tick timer tick timer tick timer tick DCGSample b … … b b b 9991000 11 910 1011 c a c c c a a a 500 5 5 56 Timer-based sampling and timing bias Call stack … … c b c b c b c c c b c c c a a t

  9. Full instrumentation Arnold-Grove sampling[2005] Timer-based sampling [2000] Correction [2007] Overhead and accuracyin call graph profiling 25 20 Overhead (%) 15 10 5 0 100 40 60 80 Accuracy (%)

  10. Outline • Motivation • Call graph correction • Evaluation

  11. Sampling Timing bias in SPEC JVM98 raytrace Normalized frequency(%) Method calls grouped by source method

  12. Timing bias in SPEC JVM98 raytrace Normalized frequency(%) Method calls grouped by source method

  13. Correction algorithms • Detect and correct DCG error • DCG constraint • Static and dynamic approaches • Static FDOM (Frequency dominator) correction • Static approach • Uses static FDOM constraint on DCG • Dynamic basic block profile correction • Dynamic approach • Uses dynamic basic block profile constraint on DCG New

  14. a b a c Static FDOM constraint • FDOM constraint on CFG • call cis executed at least as many times ascall b • call cFDOMcall b • FDOM constraint on DCG • f( ) ≥f( ) call b call c method a

  15. a a b c Static FDOM correction FDOM constraint: f( ) ≥ f( ) b b Correction 750 1,000 c c a a 500 750 DCGFDOMCorrection DCGSample • Detect error and assign the same average frequency • One possible solution to the FDOM constraint • Preserve total frequency sum

  16. a a c b Dynamic basic block profile constraint • Some dynamic optimization systems do edge profiling • Baseline compiler in Jikes RVM • Dynamic basic block profile constraint on CFG • f(call c) = 2 * f(call b) • Dynamic basic block profile constraint on DCG • f( ) = 2 * f( ) 50% 50% call b call c method a

  17. a a a a c b c b Dynamic basic block profile correction Constraint:f( ) = 2* f( ) b b Correction 1,000 500 c c a a 1,000 500 DCGEdgeProfileCorrection DCGSample fNew( ) = 1/(1+2) * (1,000+500) = 500 fNew( ) = 2/(1+2) * (1,000+500) = 1,000

  18. Static FDOM correction Dynamic basic block profile correction Best result: raytrace Sampling

  19. Outline • Motivation • Call graph correction • Evaluation

  20. Experimental methodology • Jikes RVM 2.4.5 on 3.2G Pentium 4 • Replay methodology [Blackburn et al. ‘06] • Deterministic run • 1st iteration – compilation + application run • 2nd iteration – application run • Measurement • Accuracy • Use overlap accuracy [Arnold & Grove ’05] • Overhead • 1st iteration includes call graph correction • Performance • 2nd iteration is application-only • SPECJVM98 and DaCapo benchmarks

  21. Accuracy

  22. Overhead

  23. Inlining performance Baseline: profile-guided inlining with default call graph sampling

  24. Summary • CFG constraint improves the DCG • Inlining has been tuned for bad call graph • Advantages • Can be easily combined with other DCG profiling • Minimal overhead only during the compilation • Future work • More inter-procedural optimizations with high accuracy DCG

  25. Question and comment • Thank you!

  26. Timing bias misleads optimizer • DCGSample • Edge frequencies were reversed! • Inlining decision • Inliner may inline b instead of c b b 5,000times 1,000samples Sampling with timing bias c a c a 10,000 times 500samples DCGPerfect DCGSample

  27. Compile & instrument Machine code Dynamic call graph Call graph profiling in online optimization system • Profiling and program run at the same time • Minimize profiling overhead • Corollary: sacrifice profiling accuracy Sourceprograme.g. Java byte code Online optimization system

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