1. Type Systems For Distributed Data Sharing Ben Liblit Alex AikenKathy Yelick

2. Distributed Sharing: Many Uses Data location management Cache coherence Race condition detection Program/algorithm documentation Consistency model relaxation Synchronization elimination Autonomous garbage collection Security

3. Distributed Memory Model Multiple machines, each with local memory Global memory is union of local memories Distinguish two types of pointers: –Local –Local points to local memory only –Global –Global points anywhere:  machine, address  –Different representations & operations

4. Type Grammar Boxed and unboxed values Integers, pointers, and pairs –Pairs are not assumed boxed References to boxes are either local or global

5. Review of Global Dereferencing: Standard Approach Unsound 5 x =  x =

6. Review of Global Dereferencing: Sound With Type Expansion 5 x =  x =

7. Type Expansion in Detail

8. Representation Versus Sharing Locally pointed-to data might not be private 5

9. Representation Versus Sharing Locally pointed-to data might not be private –Because of local / global aliasing 5 x =

10. Representation Versus Sharing Locally pointed-to data might not be private –Because of transitivity + pointer widening 5 y =  y =

11. Representation Versus Sharing Globally pointed-to data might not be shared –What if “  y” never actually happens? 5 y =

12.  y = Representation Versus Sharing But globally used data must be shared –If “  y” can happen, local pointer cell is shared. –What about cell containing “5”? 5 y =

13. Data Sharing as Types Shared data allows certain operations –Access by way of global pointer Private data allows other operations –Optimizations, GC, fast monitors, etc. Some form of polymorphism is essential –Neither subsumes the other –But we can have a common supertype

14. Augmented Type Grammar Allow subtyping of pointers, pairs –But not across pointers, since we allow assignment Allocation is explicitly shared or private Question: what can you do with mixed data?

15. Late Enforcement: Limited Use of Global Pointers

16. Late Enforcement: Applicability Data location management Cache coherence Race condition detection Program/algorithm documentation Consistency model relaxation Synchronization elimination  Autonomous garbage collection (in practice)  Security

17. Why Garbage Collection Breaks 1.Send out global pointer to my private data 2.Destroy all my local pointers to it 3.GC locally unreachable private data 4.… 5.Get that global pointer back again later 6.It points to my data, so coerce to local 7.Use this local pointer to my private data

18. Slightly Earlier Enforcement: No Escape of Private Addresses Note that τ′ might reference private data Autonomous garbage collection: OK  Security: not OK

19. Early Enforcement: Shared is Transitively Closed

20. Recap of Enforcement Strategies Late enforcement –Anything can point to anything –Restricted global dereference & assignment 5 y = 3

21. Recap of Enforcement Strategies Slightly earlier enforcement –Can only reveal shared addresses –Still restrict global pointer operations 5 y = 3

22. Recap of Enforcement Strategies Early enforcement –Shared universe is transitively closed –Global pointer restrictions trivially satisfied 5 y = 3

23. Type Inference: Constraint Generation Type structure already known –Including local / global Induce constraints on sharing qualifiers δ = sharedfrom global deref / assign δ ≤ δ′from assignments δ = δ′from various other operations Stricter enforcement adds more constraints δ = shared  δ′ = shared

24. Type Inference: Constraint Resolution Given constraints: private ≤ δ 1 δ ≤ δ 1 shared ≤ δ 2 δ ≤ δ 2 privatesharedδ δ1δ1 δ2δ2

25. Type Inference: Constraint Resolution Two “minimal” solutions δ = shared  δ 1 = mixed  δ 2 = shared privatesharedδ = shared δ 1 = mixedδ 2 = shared

26. Type Inference: Constraint Resolution Two “minimal” solutions δ = shared  δ 1 = mixed  δ 2 = shared δ = private  δ 1 = private  δ 2 = mixed privatesharedδ = private δ 1 = privateδ 2 = mixed

27. Type Inference: Biased Constraint Resolution 1.Push “shared” and “mixed” forward privatesharedδ δ1δ1 shared ≤ δ 2

28. Type Inference: Biased Constraint Resolution 1.Push “shared” and “mixed” forward 2.Identify qualifiers which cannot be private privatesharedδ δ1δ1 shared ≤ δ 2

29. Type Inference: Biased Constraint Resolution 1.Push “shared” and “mixed” forward 2.Identify qualifiers which cannot be private 3.Set all other qualifiers to private privatesharedδ = private δ 1 = privateshared ≤ δ 2

30. Type Inference: Biased Constraint Resolution 2.Identify qualifiers which cannot be private 3.Set all other qualifiers to private 4.Push “private” forward privatesharedδ = private δ 1 = private shared ≤ δ 2 private ≤ δ 2

31. Type Inference: Biased Constraint Resolution 3.Set all other qualifiers to private 4.Push “private” forward 5.Set remaining qualifiers to “shared” or “mixed” privatesharedδ = private δ 1 = privateδ 2 = mixed

32. Implementation For Titanium Java + extensions –Objects, classes, interfaces, methods –Multidimensional arrays, templates –Local / global, communications primitives Sharing validation as type checking Sharing inference as compiler analysis –Late or early enforcement –Whole-program or partial

33. Experimental Findings: Static Metrics How much data is “private”? –16% - 75% of all static declaration sites –46% overall; 50% on largest benchmark Is “mixed” really needed? –Up to 6% of static sites, but large impact –Some utility code: could use parametric poly

34. Experimental Findings: Static Metrics Why have “local shared”? –24% - 53% of shared data is locally addressed –Bad idea to force these to global Does enforcement policy affect results? –No change for small benchmarks (<1000 lines) –1% - 4% shift for larger codes

35. Experimental Findings: Consistency Model Relaxation Impose sequentially consistent semantics –Restrict both Titanium & C optimizers –Relax restrictions for private data Performance impact varies widely –Negligible sequential slowdown: nothing to do –Sequential slowdown, offset by inference –Sequential slowdown, better inference needed

36. Experimental Findings: Other Dynamic Metrics Data location management –1% - 100% of allocated bytes are private 45% in gas benchmark –amr : highly sensitive to enforcement policy 74% late / 19% early Synchronization elimination –Statically, one third eliminated –Dynamically, not significant for these codes

37. Summary “Shared” might not mean what you think –Related to local/global, but not the same –Different degrees of privacy to choose from Escape analysis, or several weaker alternatives –Generalizes on earlier language designs Experimental implementation –Ideas & algorithms scale to real system –More aggressive clients needed –Potential for stronger (phase-aware) inference