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Scaling Model Checking of Dataraces Using Dynamic Information

Explore the challenges and solutions in detecting dataraces in multi-threaded programs. Learn about dynamic and static detection tools, Lockset technique, and a hybrid solution combining Lockset and model checking to provide accurate witnesses for rare dataraces. Witness how the hybrid approach eliminates spurious dataraces and helps in analyzing program concurrency efficiently.

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Scaling Model Checking of Dataraces Using Dynamic Information

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  1. Scaling Model Checking of Dataraces Using Dynamic Information Ohad Shacham Tel Aviv University IBM Haifa Lab Mooly Sagiv Tel Aviv University Assaf Schuster Technion

  2. Datarace • Happens when two threads access a memory location concurrently • At least one access is a write • Unpredictable results • Can indicate bugs • Hard to detect • Hard to reproduce

  3. Datarace example TicketPurchase(NumOfTickets) { if (NumOfTickets · FreeTickets) FreeTickets -= NumOfTickets else Print “Full”; }

  4. TicketPurchase(NumOfTickets) { if (NumOfTickets · FreeTickets) FreeTickets -= NumOfTickets else Print “Full”; } Datarace example {FreeTickets = 4} Thread I Thread II TicketPurchase(2) if (NumOfTickets · FreeTickets) TicketPurchase(4) if (NumOfTickets · FreeTickets) FreeTickets -= NumOfTickets FreeTickets -= NumOfTickets {FreeTickets = -2}

  5. Datarace example TicketPurchase(NumOfTickets) { Lock(lockFreeTickets) if (NumOfTickets · FreeTickets) FreeTickets -= NumOfTickets else Print “Full”; Unlock(lockFreeTickets) }

  6. Datarace detection • Static datarace detection tools • Racex [Engler and Ashcraft] • TVLA [Sagiv et. al.] • Dynamic datarace detection tools: • Lamport’s happens-before partial order (Djit) • Lock based techniques (Lockset)

  7. Difficulties in model checking dataraces • Infinite state space • Huge number of interleavings • Huge transition systems • Size problem

  8. Observation Programs maintaining a locking discipline are dataraces free

  9. Hybrid solution • Dynamically check a locking discipline • Produce witnesses for dataraces using a model checker • Explore suffixes of the trace

  10. Basic idea

  11. Algorithm flow Multithreaded program List of Warnings Lockset Find a1 Model Checker 1 2

  12. Lockset • Savage et. al. SOSP 1997 • Lockset invariant • multiple accesses to a specific memory location are guarded by a unique lock

  13. Lockset example Locks I Thread I Thread II Locks II C(X) {lockx, locky} Lock(lockx)  {lockx} X = 7 {lockx} Unlock(lockx) Lock(locky)  Z = Y {locky} Unlock(locky) Lock(locky)  Y = 2 {locky} Unlock(locky)  Lock(locky) {locky} Y = X 

  14. Lockset • Advantage • Predict dataraces which may occur in a different thread interleaving • Disadvantages • Spurious dataraces • Hard to use • Lack of trace

  15. Lockset strength Locks I Thread I Thread II Locks II C(X) {lockx, locky} Lock(lockx);  {lockx} X = 7; {lockx} Unlock(lockx); Lock(locky);  Z = Y; {locky} Unlock(locky); Lock(locky);  Y = 2; {locky} Unlock(locky);  Lock(locky); {locky} Y = X; 

  16. Our hybrid solution • Combine Lockset & Model Checking • Provide witnesses for dataraces • Rare dataraces • Dataraces in large programs Model CheckingProvide witnesses for rare DR + Locksetscale for large programs

  17. A witness for a datarace 1 2 a1 ma1 = ma2a1=Write Ç a2=Write a2

  18. Required data from Lockset Thread I Thread II X=7 Z=Y Y=2 Y=X

  19. Using Lockset data • Lockset provides for each warning only a single access event a2 • Find a prior access event a1 which can take part in a race with a2 a1 a2

  20. Using Lockset data X=7 X = 7 {lockx} Z=Y Z = Y {locky} Y=2 {locky} Y = 2 Y=X Y = X {locky} A Warning on X

  21. Prefix a1 a2

  22. Building a model a1 MODEL without t1

  23. Using a model checker Is a2 reachable by t2? 1 a1 a2

  24. Using a model checker a1 a2 a1

  25. Reduce the model checker cost • Reduction in the model size • Elimination of thread t1 • Providing a single new initial configuration • Heuristically reducing the number of steps that the model checker should carry out

  26. Example Locks I Thread I Thread II Locks II C(X) 1 {lockx, locky} Lock(lockx);  {lockx} X = 7; {lockx} Unlock(lockx); Lock(locky); Lock(locky);  Y = 2; Z = Y; {locky} 2 Unlock(locky); Unlock(locky); Lock(locky); Y = X; Lock(locky); X = 7; Y = 2; {locky} Unlock(locky);  Lock(locky); {locky} Y = X; 

  27. Prototype implementation • A prototype tool based on IBM tools • Lockset – The IBM Watson tool • Wolf – IBM Haifa’s software model checker

  28. Prototype implementation Multi-threaded program List of Warnings Lockset Find a1 Extend 1 Wolf 1 2

  29. Benchmark programs

  30. Experimental results

  31. Conclusion • Hybrid technique which combines dynamic datarace detector and a model checker • Provide witnesses for dataraces which occur only in rare interleavings • Helps the user in analyzing the datarace • No spurious dataraces

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