1. Statistical Analysis of Inlining Heuristics in Jikes RVM Jing Yang jy8y@cs.virginia.edu Department of Computer Science, University of Virginia

2. Outline Introduction Data Set Description Data Analysis, Summarization and Interpretation Future Work Conclusion

3. Introduction Java highly portable programming language portability is often at the cost of execution speed seeking optimizations to improve the performance inlining – one of the heavily used optimizations reduce overhead associated with the function call code expansion aggressive inlining can hurt performance

4. Introduction

5. Inlining Heuristic used in Java Virtual Machines to decide whether to inline or not need to be tuned to achieve the optimal solution current tuning technique manually – not accurate genetic algorithm – time-consuming due to the large number of parameters

6. Introduction Goal locate parameters used in the inlining heuristic that actually impact its performance focus on these “effective” parameters reduce time for tuning process simplify the inlining heuristic to reduce its running overhead without sacrificing its performance

7. Data Set Description

9. Data Collection a complete combination of all the values – 500 for each configuration, run the whole SPECjvm98 benchmark (including nine Java programs) – 9 within each run, collect three performance metrics (compiled code size, compilation time, execution time) – 3 raw data – 500*9*3=13,500 observations form the raw data into different formats for different statistical analysis

10. Data Analysis, Summarization and Interpretation Inside Performances 3 variables – compiled code size, compilation time, execution time geometric mean from the first seven benchmarks in SPECjvm98 500 observations standardization before analysis

11. Data Analysis, Summarization and Interpretation Principal Component Analysis

12. Data Analysis, Summarization and Interpretation First Principal Component explain 71.58% of the total sample variance. correlations between the first principal component and the three variables compiled code size/compilation time has a contrast to execution time accords with our intuition of inlining

13. Data Analysis, Summarization and Interpretation Performances versus Parameters 7 variables – four parameters and three performance metrics 500 observations standardization before analysis

14. Data Analysis, Summarization and Interpretation Canonical Correlation Analysis

15. Data Analysis, Summarization and Interpretation Only Consider One Performance Metric

16. Data Analysis, Summarization and Interpretation Conclusion CALLEE_MAX_SIZE and MAX_INLINE_DEPTH are important ALWAYS_INLINE_SIZE and CALLER_MAX_SIZE can be neglected unless only optimizing execution time

17. Data Analysis, Summarization and Interpretation Verification classify data from the remaining two benchmarks the more important a parameter is, the better classification can be performed based on it Fisher's discriminant function pairwise classifications based on CALLEE_MAX_SIZE multi-population classification based on CALLEE_MAX_SIZE most separate pairwise classification based on ALWAYS_INLINE_SIZE, MAX_INLINE_DEPTH and CALLER_MAX_SIZE

18. Data Analysis, Summarization and Interpretation

22. Future Work Verification by Jikes RVM Better Discriminant Function Statistical Method to Find Optimal Solution Program Properties

23. Conclusion Locate and Verify the Important Parameters Reduce Time for Tuning Process Simplify the Inlining Heuristic