1. Copyright (c) 2004 Borys Bradel Myths and Realities: The Performance Impact of Garbage Collection Paper: Stephen M. Blackburn, Perry Cheng, and Kathryn S. McKinley Presentation: Borys Bradel

2. 2 Introduction Garbage collection makes life easier Additional level of abstraction No worries about memory management How to make it efficient? What are the tradeoffs?

3. 3 Outline Terminology Garbage Collectors Benchmarks Results Conclusions

4. 4 Runtime Components Mutator: the executing program includes time for object allocation and, if necessary, a write barrier Garbage collectors: allocate memory in a simple manner periodically identify unused memory and free it

5. 5 Allocators Contiguous: append new objects Free-List: k size-segregated free-lists Example: a: allocate 8 bytes b: allocate 12 bytes c: allocate 16 bytes free b d: allocate 16 bytes

6. 6 Contiguous a: 8 b: 12 c: 16 free b d: 16 a

7. 7 Contiguous a: 8 b: 12 c: 16 free b d: 16 ab

8. 8 Contiguous a: 8 b: 12 c: 16 free b d: 16 abc

9. 9 Contiguous a: 8 b: 12 c: 16 free b d: 16 abc

10. 10 Contiguous a: 8 b: 12 c: 16 free b d: 16 abcd Good spatial locality sequential allocation creates sequential memory location No reclamation quickly run out of space

11. 11 Free-List a: 8 b: 12 c: 16 free b d: 16 a 8 byte 16 byte

12. 12 Free-List a: 8 b: 12 c: 16 free b d: 16 a b 8 byte 16 byte

13. 13 Free-List a: 8 b: 12 c: 16 free b d: 16 a b c 8 byte 16 byte

14. 14 Free-List a: 8 b: 12 c: 16 free b d: 16 a c 8 byte 16 byte

15. 15 Free-List a: 8 b: 12 c: 16 free b d: 16 a cd 8 byte 16 byte Bad spatial locality: different queues Reclamation Some fragmentation

16. 16 Collectors Whole heap work on entire heap treats everything the same: slow Generational divide heap into old and new optimize for the common case: faster Tracing – compute transitive closure Reference Counting

17. 17 Example a b c d e f g h

18. 18 Tracing – Collection 1 a b c d e f g h Roots

19. 19 Tracing – Collection 1 a b c d e f g h Roots Live

20. 20 Tracing – Collection 1 a b c d e f g h Roots Live

21. 21 Tracing - Change a b d e f g h Roots

22. 22 Tracing – Collection 2 a b d e f g h Roots Live Dead

23. 23 SemiSpace Contiguous allocator Divide space into two At collection time move live data over d e f g h defgh

24. 24 d e f g h SemiSpace defgh

25. 25 SemiSpace Waste of space Copies long lived objects many times Time proportional to survivors d f df

26. 26 Mark and Sweep Free-list allocator Collect when heap is full Use bitmaps Copies long lived objects many times d ef g h d ef g h d f

27. 27 Reference Counting a b c d: 2 e: 1 f: 1 g: 1 h: 2 Roots

28. 28 Reference Counting a b d: 2 e: 0 f: 1 g: 1 h: 2 Roots

29. 29 Reference Counting a b d: 2 f: 1 g: 0 h: 1 Roots

30. 30 Reference Counting a b d: 2 f: 1 h: 0 Roots

31. 31 Reference Counting a b d: 2 f: 1 Roots Free-List allocator Time proportional to dead objects Detects cycles Uses object logging at writing

32. 32 Generational Collector “Weak generational hypothesis” the young die quickly, the old die slowly Put young objects in a nursery When nursery full, collect it and move survivors to old generation, a la SemiSpace When heap full, perform all out collection

33. 33 Generational Example d e f g h NurseryMature

34. 34 Generational Example d f NurseryMature i k m j l

35. 35 Generational Example d f NurseryMature j l i m

36. 36 Generational Example d f NurseryMature n o r p q j l i m

37. 37 Generational Example d f NurseryMature o r q j l i m

38. 38 Generational Collectors On writes, record pointers from mature to nursery objects Nursery is smaller than half of memory generally just contiguously allocated survivors copied over into mature space Mature space uses one of the three collectors – has their benefits/pitfalls

39. 39 Benchmarks Low memory usage: 201, 222 Low nursery survival: 202, 228, 205, 227 High nursery survival: 213, 209, jbb focus: accesses/size Table 1 in [1]

40. 40 Results Larger heap size decreases frequency of collection, up to a point Small difference in sizes cause different behaviours Generational are big win Less examined at each collection Fewer collections too, especially Mark and Sweep Figure 1 in [1]

41. 41 More Results Write Barrier Up to 13.6% overhead, average of 3.2% Benefit from generations more than makes up for the overhead Free-List versus contiguous allocation Free-List 11% slower.

42. 42 Mutator Costs Whole heap SemiSpace 7-15% over Mark and Sweep Mainly due to cache effects Generational Locality of mature objects not a factor (except when it is, like in jbb) So fewer collections for Mark and Sweep are a win Figure 2 in [1]

43. 43 More Results For small heaps, collection overhead dominates – want generational Mark and Sweep For large heaps generational SemiSpace Reference counting too expensive

44. 44 Infinite Heaps No garbage collection has small effect on mutator compared to when garbage collection is performed Most reference patterns exhibit temporal locality, not spatial locality When spatial locality is important, garbage collection results in better performance

45. 45 Nursery Size Sweetspot well beyond size of L2 cache Larger size leads to fewer collections Eventually too big Figure 4 in [1]

46. 46 Conclusions Contiguous allocation yields better locality Free-lists are more space efficient, so less collection Counters myth that collection frequency is first order effect on performance? Explicit allocation is bad because free list can’t capture locality???

47. 47 References Figures and Tables are all from: [1] Stephen M. Blackburn, Perry Cheng, and Kathryn S. McKinley. Myths and Realities: The Performance of Garbage Collection. Sigmetrics – Performance, 2004.