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Exploiting Managed Language Semantics to Mitigate Wear-out in Persistent Memory Shoaib Akram Ghent University, Belgium Flash Memory Summit 2019 Santa Clara, CA 1 Main memory capacity expansion Charge storage in DRAM a scaling limitation 1


  1. Exploiting Managed Language Semantics to Mitigate Wear-out in Persistent Memory Shoaib Akram Ghent University, Belgium Flash Memory Summit 2019 Santa Clara, CA 1

  2. Main memory capacity expansion Charge storage in DRAM a scaling limitation 1 Price/Gb ($) 0.9 Manufacturing complexity makes 0.8 DRAM pricing 0.7 volatile WSTS, IC Insights 0.6 Jan’17 Jan’18 Flash Memory Summit 2019 Santa Clara, CA 2

  3. Phase change memory (PCM) πŸ™ƒ Scalable β†’ More Gb for the same price Byte addressable like DRAM Latency closer to DRAM πŸ™‚ Low write endurance Flash Memory Summit 2019 Santa Clara, CA 3

  4. Why PCM has low write endurance? Store information as change in resistance Crystalline is set & Amorphous is reset Amorphous temperature Electric pulses to program PCM cells Crystalline wear them out time Flash Memory Summit 2019 Santa Clara, CA 4

  5. Mitigating PCM wear-out Wear-leveling to spread writes across PCM Flash Memory Summit 2019 Santa Clara, CA 5

  6. Mitigating PCM wear-out Wear-leveling to spread writes across PCM Flash Memory Summit 2019 Santa Clara, CA 5

  7. Mitigating PCM wear-out Wear-leveling to spread writes across PCM Problem: PCM-Only with wear-leveling wears out in a few months Flash Memory Summit 2019 Santa Clara, CA 5

  8. Hybrid DRAM-PCM memory Capacity Endurance Persistence DRAM PCM This talk β†’ Use DRAM to limit PCM writes Flash Memory Summit 2019 Santa Clara, CA 6

  9. OS to limit PCM writes DRAM PCM Page migrations hurt performance and PCM lifetime Flash Memory Summit 2019 Santa Clara, CA 7

  10. Managed runtimes Platform independence Application Abstract hardware/OS Managed β†’ Aka Virtual Machine Runtime Ease programmer’s burden Operating Garbage collection (GC) System Hardware Flash Memory Summit 2019 Santa Clara, CA 8

  11. GC to limit PCM writes Application GC aware of heap semantics β†’ Pro-active allocation GC operates with objects Operating β†’ Fine-grained mgmt. System Hardware Flash Memory Summit 2019 Santa Clara, CA 9

  12. Write Distribution in GC heap mature nursery GC 70% of writes Flash Memory Summit 2019 Santa Clara, CA 10

  13. Write Distribution in GC heap mature nursery GC 22% 70% of writes to 2% of objects Flash Memory Summit 2019 Santa Clara, CA 10

  14. Write-Rationing Garbage Collection Limit PCM writes by discovering highly written objects Kingsguard β†’ dynamic monitoring Crystal Gazer β†’ prediction Flash Memory Summit 2019 Santa Clara, CA 11

  15. Kingsguard-Nursery (KG-N) nursery mature large DRAM PCM Flash Memory Summit 2019 Santa Clara, CA 12

  16. Kingsguard-Writers (KG-W) nursery mature large observer mature large DRAM PCM Flash Memory Summit 2019 Santa Clara, CA 13

  17. Metadata optimization meta payload Full-heap GC: Mark a bit in meta of all live objects Meta Opt: Place object meta-data in DRAM Flash Memory Summit 2019 Santa Clara, CA 14

  18. KG-W drawbacks Monitoring overhead Limited opportunity to predict writes Fixed DRAM consumption Flash Memory Summit 2019 Santa Clara, CA 15

  19. Write-Rationing Garbage Collection Limit PCM writes by discovering highly written objects Kingsguard β†’ monitoring Crystal Gazer β†’ prediction Flash Memory Summit 2019 Santa Clara, CA 16

  20. Allocation site as a write predictor a = new Object() b = new Object() c = new Object() Produces highly written d = new Object() objects Uniform distribution πŸ™‚ Skewed distribution πŸ™ƒ Flash Memory Summit 2019 Santa Clara, CA 17

  21. Write distribution by allocation site Few sites capture majority of writes 100 % mature objects Writes 75 Volume 50 25 0 0 50 100 150 Sites sorted by writes Flash Memory Summit 2019 Santa Clara, CA 18

  22. Crystal Gazer operation Application Advice Bytecode Profiling Generation Compilation a = new Object() a = new Object() … … b = new Object() b = new_dram Object() Flash Memory Summit 2019 Santa Clara, CA 19

  23. Advice generation Generate <alloc-site, advice> pairs advice β†’ DRAM or PCM input is a write-intensity trace Two heuristics to classify allocation sites as DRAM Flash Memory Summit 2019 Santa Clara, CA 20

  24. DRAM allocation sites Frequency : More than a threshold writes βœ” Aggressively limits writes βœ— 1 Byte and 1024 Byte object treated similarly Density : More than a threshold write-density βœ” Optimizes for writes and DRAM capacity Flash Memory Summit 2019 Santa Clara, CA 21

  25. Classification examples Frequency threshold = 1 PCM writes = ?, DRAM bytes = ? Object Allocation Identifier # Writes # Bytes site O1 0 4 A() + 10 O2 0 4 A() + 10 O3 128 4 A() + 10 O4 128 4096 B() + 4 Flash Memory Summit 2019 Santa Clara, CA 22

  26. Classification examples Frequency threshold = 1 PCM writes = ?, DRAM bytes = ? Object Allocation Identifier # Writes # Bytes site O1 0 4 A() + 10 O2 0 4 A() + 10 β†’ O3 128 4 A() + 10 β†’ O4 128 4096 B() + 4 Flash Memory Summit 2019 Santa Clara, CA 22

  27. Classification examples Frequency threshold = 1 PCM writes = 0/256, DRAM bytes = 5008 Object Allocation Identifier # Writes # Bytes site O1 0 4 A() + 10 O2 0 4 A() + 10 β†’ O3 128 4 A() + 10 β†’ O4 128 4096 B() + 4 Flash Memory Summit 2019 Santa Clara, CA 22

  28. Classification examples Density threshold = 1 PCM writes = ?, DRAM bytes = ? Object Allocation Identifier # Writes # Bytes site O1 0 4 A() + 10 O2 0 4 A() + 10 O3 128 4 A() + 10 O4 128 4096 B() + 4 Flash Memory Summit 2019 Santa Clara, CA 22

  29. Classification examples Density threshold = 1 PCM writes = ?, DRAM bytes = ? Object Allocation Identifier # Writes # Bytes site O1 0 4 A() + 10 O2 0 4 A() + 10 β†’ O3 128 4 A() + 10 32 O4 128 4096 B() + 4 Flash Memory Summit 2019 Santa Clara, CA 22

  30. Classification examples Density threshold = 1 PCM writes = ?, DRAM bytes = ? Object Allocation Identifier # Writes # Bytes site O1 0 4 A() + 10 O2 0 4 A() + 10 O3 128 4 A() + 10 β†’ <1 O4 128 4096 B() + 4 Flash Memory Summit 2019 Santa Clara, CA 22

  31. Classification examples Density threshold = 1 PCM writes = 128/256, DRAM bytes = 12 Object Allocation Identifier # Writes # Bytes site O1 0 4 A() + 10 O2 0 4 A() + 10 O3 128 4 A() + 10 O4 128 4096 B() + 4 Flash Memory Summit 2019 Santa Clara, CA 22

  32. Object placement in Crystal Gazer new_dram() β†’ Set a bit in the object header GC β†’ Inspect the bit on nursery collection to copy object in DRAM or PCM Flash Memory Summit 2019 Santa Clara, CA 23

  33. Object placement in Crystal Gazer nursery mature large πŸ§‘ mature large DRAM Is marked highly written? βœ“ PCM Flash Memory Summit 2019 Santa Clara, CA 24

  34. Persistence Persistent parent β†’ copy child objects to PCM VM startup β†’ Move highly-written to DRAM Write barrier tracks writes & persistent candidates Flash Memory Summit 2019 Santa Clara, CA 25

  35. Evaluation methodology 15 Applications β†’ DaCapo, GraphChi, SpecJBB Medium-end server platform Different inputs for production and advice Jikes RVM Flash Memory Summit 2019 Santa Clara, CA 26

  36. Emulation platform App Jikes RVM OS βœ— CPU CPU Flash Memory Summit 2019 Santa Clara, CA 27

  37. PCM write rates β†’ lifetime PCM-Only write rate is up to 1.8 GB/s Safe operation is 200 MB/s for 5-10 year lifetime Flash Memory Summit 2019 Santa Clara, CA 28

  38. PCM write rates KG-N KG-W Dens Freq Write rate in MB/s 800 600 400 200 0 Flash Memory Summit 2019 Santa Clara, CA 29

  39. Performance KG-W Dens Freq 1.5 execution time KG-N norm 30% 8% 1.0 0.5 0.0 Flash Memory Summit 2019 Santa Clara, CA 30

  40. DRAM capacity KG-W Dens Freq 75 % of heap in DRAM 50 25% 25 0 Flash Memory Summit 2019 Santa Clara, CA 31

  41. KG-W versus Crystal Gazer 0.8 KG-N norm. PCM writes 0.7 KG-W 0.6 0.5 0.4 0.3 100 150 200 250 DRAM MB Flash Memory Summit 2019 Santa Clara, CA 32

  42. KG-W versus Crystal Gazer 0.8 KG-N norm. Crystal Gazer PCM writes 0.7 Crystal Gazer KG-W 0.6 opens up 0.5 Pareto-optimal 0.4 trade-offs 0.3 100 150 200 250 DRAM MB Flash Memory Summit 2019 Santa Clara, CA 32

  43. Write-rationing garbage collection Hybrid memory is inevitable DRAM PCM Each layer can play a role in wider adoption Write-rationing GC is pro-active and fine-grained Flash Memory Summit 2019 Santa Clara, CA 33

  44. More information PLDI 2018 β†’ Write-rationing garbage collection for hybrid memories SIGMETRICS 2019 β†’ Crystal Gazer: Profile-driven write- rationing garbage collection for hybrid memories ISPASS 2019 β†’ Emulating and evaluating hybrid memory for managed languages on NUMA platform Flash Memory Summit 2019 Santa Clara, CA 34

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