Rethinking garbage collection
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This talk aims to give overview of memory management and Garbage Collection and a new look to the typical process of Garbage Collection. Speaker: Dr. Rifat Shahriyar Assistant Professor Department of Computer Science and Engineering Bangladesh University of Engineering and Technology
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Rethinking garbage collection
- 2. Who Am I? • Asst. Prof., Dept. of CSE, BUET • PhD from Australian National University (ANU) • Dissertation title – High Performance Reference Counting and Conservative Garbage Collection • Supervised by – Steve Blackburn (ANU) – Kathryn McKinley (MSR) 2
- 4. The Birth of GC (1960) 4
- 5. Today Garbage collection is ubiquitous • GC algorithms – Tracing and Reference counting • GC implementations – Exact and Conservative ✔ Tracing and Exact in all highly engineered, high performance systems ✘ Reference counting and conservative only in non-performance critical settings 5
- 6. GC Fundamentals Algorithmic Components Allocation Reclamation 6 Identification Bump Allocation Free List ` Tracing (implicit) Reference Counting (explicit) Sweep-to-Free Compact Evacuate 3 1
- 7. Mark-Compact [Styger 1967] Bump allocation + trace + compact GC Fundamentals Canonical Garbage Collectors 7 ` Sweep-to-Free Compact Evacuate Mark-Sweep [McCarthy 1960] Free-list + trace + sweep-to-free Semi-Space [Cheney 1970] Bump allocation + trace + evacuate
- 8. Generational[Ungar 1984] • Most objects die young 8 Nursery space Mature space
- 9. Immix [Blackburn and McKinley 2008] • Contiguous allocation into regions – 256B lines and 32KB blocks – Objects span lines but not blocks • Simple mark phase – Mark objects and containing regions • Free unmarked regions • Recycled allocation and defragmentation 9 block line recyclable linesobject mark line mark
- 10. Down for the Count? Getting Reference Counting Back in the Ring ISMM’12 10
- 12. Reference Counting [Collins 1960] 12 1 1 1 1 2 1 2 0 1 ✗ Roots
- 13. Why Reference Counting? Advantages ✔ Immediacy ✔ Object local ✔ Basic RC is easy Disadvantages ✘ Cycles ✘ Performance 13
- 14. Problem ✔ One of the two fundamental GC algorithms ✔ Many advantages ✘ Neglected by performance-conscious VMs So how much slower is it? Can we get RC back in the ring? 14 30%
- 15. Optimizing RC • Limited bit count – Use just few bits, fix o/f with backup tracing • Elision of new object counts – Only do RC work if object survives the first GC • Born as dead – Avoid free-list work for short lived objects 15
- 16. 16 New RC ≈ MS Optimized RC vs. MS -20% 0% 20% 40% 60% 80% compress jess db javac mtrt jack avrora bloat eclipse fop hsqldb luindex pmd sunflow xalan pjbb2005 geomean faster←Time→slower Old RC New RC
- 17. Summary • Old RC – 30% slower than MS – 40% slower than production • New RC – Limited bit count – Optimization for new objects • Performance – Matches MS – Still 10% slower than production 17 40% 10% TotalTimevProduction < 2012 2012
- 18. Taking Off the Gloves with Reference Counting Immix OOPSLA’13 18
- 19. Why So Slow? 10% 10% -3% TimevProduction 19 Total Mutator GC
- 20. Looking a Little Deeper… 10% 9% 32% 7% 4% 28% -2% -3% -2%-3% -3% 1% MutatorvProduction RC MS SS Immix 20 Time Instructions Retired L1 D Cache Misses
- 21. Looking a Little Deeper… 10% 9% 32% 7% 4% 28% -2% -3% -2%-3% -3% 1% MutatorvProduction RC MS SS Immix 21 Time Instructions Retired L1 D Cache Misses Free List Bump Pointer
- 22. Goal & Challenge • Goal – Object-local collection – Excellent mutator locality – Copying to eliminate fragmentation • Immix provides opportunistic copying ✔ Same mutator locality as contiguous allocator • However, RC is inherently local – References to an object generally unknown – but copying must redirect all references 22
- 23. RC Immix ✔ Combines RC and Immix ✔ Line/block reclamation ✔ Line live object count with object reference count ✔ Exploit Immix’s opportunistic copy ✔ Observe new objects can be copied by first GC ✔ Observe old objects can be copied by backup GC 23
- 24. Total time 3% faster then Gen Immix, +6% worst case, -21% best case 24 -30% -20% -10% 0% 10% 20% 30% 40% compress jess db javac mtrt jack avrora bloat eclipse fop hsqldb jython luindex lusearchfix pmd sunflow xalan pjbb2005 geomean faster←Time→slower RC RC Immix
- 25. Summary • RC Immix – Object-local collection – Excellent mutator locality – Copying with RC • Great performance – Outperforms fastest production • Transforms RC TotalTimevProduction 10% RC 2013 RC Immix -3% 25 Questions?