1. © Richard JonesISMM 2008, Tucson, AZ 000 A Study of Java Object Demographics Richard Jones Chris Ryder Computing Laboratory University of Kent

2. © Richard JonesISMM 2008, Tucson, AZ 001 Overview Motivation & Contribution Object demographics examples Data capture Clustering Program inputs Calling context Related work

3. © Richard JonesISMM 2008, Tucson, AZ 002 Tomorrow Never Dies? Object segregation Generations Older-First Immortal region Large object areas Idea: Segregate objects by age, size, type, mortality, etc. Collect regions under different policies and mechanisms. Choice of GC Select the best GC for the application a priori. Hot-swap running GC. Idea: Different applications have different demographics. Respond to phase changes. Exploiting program behaviour

4. © Richard JonesISMM 2008, Tucson, AZ 003 Dr. No ‘one size fits all’ Most systems manage objects uniformly E.g. allocate all objects in a nursery and collect all nursery objects at the same time, promoting to the same older generation. Pre-tenuring GC uses a very simple classification E.g. short-lived, long-lived, immortal. Contributions A detailed study of Java object demographics reveals A richer landscape than short/long/immortal. Distinct behaviour of application, library and JVM objects. Clusters of allocation sites, stable across program inputs. A small number of clusters dominate. Context is an important predictor for library allocation.

5. © Richard JonesISMM 2008, Tucson, AZ 004 The Living Daylights Compiles JavaLex scanner 4 times. Allocates [char] for a GNU classpath internal String constructor. 6% of total allocation. Compiles JavaLex scanner 4 times. 85% of these objects very short-lived. A few are immortal. Some survive to the end of the phase. A few are long lived. Age ToD No go area ToD < Age Lifespan _213_javac, speed 100

6. © Richard JonesISMM 2008, Tucson, AZ 005 DaCapo hsqldb, default input. 4 sites 17% volume 95% space rental [volume x lifetime] Scarcely any objects are very short-lived. Die Another Day

7. © Richard JonesISMM 2008, Tucson, AZ 006 Live And Let Die DaCapo fop, default input 18 sites 19% volume 8.29% short-lived 9.27% immortal 16% space rental

8. © Richard JonesISMM 2008, Tucson, AZ 007 For Your Eyes Only MemTrace compiles method to… Record allocation sites. Modify allocation routines. –Tag object header with site & position in calling context tree. –Emit allocation record. Benefit: same framework as for method specialisation [ISMM07]. MemTrace profiles using… Baseline compiler — focus on application objects. Forced full collections (64K granularity). –GCspy framework to log death events. –Exaggerates lifetimes of short-lived objects.

9. © Richard JonesISMM 2008, Tucson, AZ 008 Casino Royale Aim Characterise lifetimes of objects allocated by a site. Identify sites with similar lifetimes. We call the cumulative frequency curve the lifetime distribution function (ldf) of the site. Expect collaborating sites to have similar ldf’s.

10. © Richard JonesISMM 2008, Tucson, AZ 009 From Russia With Love Compare ldf’s statistically for some confidence n% Kolmogorov-Smirnov Two Sample test D = the maximum difference between 2 frequency distributions Ei(t) p(D is significant) < n? Benefit: non-parametric, distribution-free, cheap.

11. © Richard JonesISMM 2008, Tucson, AZ 0010 Thunderball Greedy, gravitational clustering

12. © Richard JonesISMM 2008, Tucson, AZ 0011 Thunderball Greedy, gravitational clustering

13. © Richard JonesISMM 2008, Tucson, AZ 0012 Thunderball Greedy, gravitational clustering

14. © Richard JonesISMM 2008, Tucson, AZ 0013 Thunderball Greedy, gravitational clustering

15. © Richard JonesISMM 2008, Tucson, AZ 0014 DaCapo: all allocation Immortal cluster always cluster 0

16. © Richard JonesISMM 2008, Tucson, AZ 0015 DaCapo: application packages Immortal cluster always cluster 0

17. © Richard JonesISMM 2008, Tucson, AZ 0016 DaCapo: Jikes RVM excluding compiler scope Immortal cluster always cluster 0

18. © Richard JonesISMM 2008, Tucson, AZ 0017 DaCapo: library packages excluding compiler scope Immortal cluster always cluster 0

19. © Richard JonesISMM 2008, Tucson, AZ 0018 DaCapo: Jikes RVM packages compiler scope Immortal cluster always cluster 0

20. © Richard JonesISMM 2008, Tucson, AZ 0019 DaCapo: library packages compiler scope

21. © Richard JonesISMM 2008, Tucson, AZ 0020 You Only Live Twice Does an allocation site generate the same lifetime behaviour regardless of input? Do allocation sites share the same cluster from one input to another? i.e. continue to behave in the same way as each other? Compare cluster membership with Adjusted Rand Index antlrjythonpmdps CoApLiVMCoApLiVMCoApLiVMCoApLiVM SD 0.90.51.0 0.71.0 0.91.0 0.9 SL 0.80.40.91.00.7 1.0 0.71.0 0.8 1.00.9 DL 1.0 0.81.0 0.60.71.00.9

22. © Richard JonesISMM 2008, Tucson, AZ 0021 The World Is Not Enough? Earlier studies Java: site is sufficient [Blackburn et al, OOPSLA01] C: more context required [Zorn & Seidl, ASPLOS98] Calling context Increasing depth of context splits an ldf into 1 or more. Compare the variance of site ldf’s Variance of program = weighted sum of the variances of its ldf’s

23. © Richard JonesISMM 2008, Tucson, AZ 0022 Context Context (2) All Jikes RVM Library Application Variance as a multiple of depth = 

24. © Richard JonesISMM 2008, Tucson, AZ 0023 A View To A Kill Related work: choice of GC Fitzgerald and Tarditi [ISMM00]. Hot-swapping: Printezis [JVM01]; Soman, Krintz, Bacon [ISMM04]; Singer, Brown & Watson [ISMM07]. Thomas [Inf Proc Letters '95] tailors GC to the program. Demographics Dieckman and Holzle [ECOOP98] focus on reference densities, proportion of arrays, etc. DaCapo [OOPSLA06] characterise benchmarks by heap-related metrics. Pretenuring - Cheng, Harper, Lee [PLDI98], Harris [ISMM00]; Blackburn et al [TOPLAS07]; Marion, Jones, Ryder [ISMM07]. Merlin [SIGMETRICS02].

25. © Richard JonesISMM 2008, Tucson, AZ 0024 Conclusions No one size of collector fits all. Programs exhibit only a few distinct object lifetime distributions. These are richer than short/long/immortal. A very small number of clusters dominate. Clusterings are stable across inputs. Calling context is important for libraries. http://www.cs.kent.ac. uk/projects/gc/demographics

26. © Richard JonesISMM 2008, Tucson, AZ 0025 Questions?