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Parallel and Distributed Computing in Model Checking

Parallel and Distributed Computing in Model Checking. Diana DUBU (UVT) Dana PETCU (IeAT, UVT) {ddubu,petcu}@info.uvt.ro. Subject:. +. = ?. Model (?!) checking. Parallel (?!) computing. Model Checking. 1) Modeling : convert a design into a formalism accepted by a model checking tool.

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Parallel and Distributed Computing in Model Checking

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  1. Parallel and Distributed Computing in Model Checking Diana DUBU (UVT) Dana PETCU (IeAT, UVT) {ddubu,petcu}@info.uvt.ro

  2. Subject: + = ? Model (?!) checking Parallel (?!) computing

  3. Model Checking 1) Modeling: convert a design into a formalism accepted by a model checking tool. 2) Specification: state the properties that the design must satisfy. 3) Verification: ideally completely automatic.

  4. Model Checking Results  Design/Implementation/Testing  2. Modeling/Property Specification • Finite-state model extraction • Simplifications • Restrictions  3. Verification • Abstractions • Divide-and-conquer techniques

  5. Model Checking Problems • The state explosionproblem Model checkers today can routinely handle systems with between 100 and 300 state variables. Systems with 10^120 reachable states have been checked • The model construction problem. • The requirement specification problem. • The output interpretationproblem.

  6. Two aspects • Parallel and distributed algorithms for model checking • Model checking on parallel and distributed codes

  7. Why to parallize? • gain memory: in state exploration • gain time: discovering a bug after 1 day is better than 4! State of art: Parallel and Distributed Model Checking, PDMC, August 19, 2002 -- Brno, Czech Republic, Satellite Workshop to CONCUR 2002. In Electronic Notes in Theoretical Computer Science vol. 68 (4), October 2002

  8. Parallel and distributed algorithms were developed for • Explicit state methods- reachability and model construction- LTL model checking- model checking for alternation-free -calculus • BDD-based methods- reachability and generation of counter example- model checking for full -calculus

  9. Problems in designing parallel/distributed algs. • Partitioning the work among the processes • Dynamic or static load balance to maintain balanced use of memory • Maintaining a good proportion between computation at each process and communication • Distributed or centralized termination detection

  10. Reachability in distributed state space • The state space is partitioned into slices • Each slice isownedby one process • Each process runs a check on its slice • When non-owned states are discovered they are sent to the process that owns them

  11. Load Balance • The initial slicing distributes the memory requirements equally among the processes. • As more states are discovered, the memory requirements might become unbalanced. • Therefore, at the end of each step in the computation of the reachable states a load balance procedure is applied.

  12. Solutions for load balancing • Stern, Dill [1997]: Random load balancing • Lerda, Sisto[1999]: Partition to minimise cross transitions • Garavel et al. [2001]: Partition independent of state structure • Lafuente [2002]: Partition to localise cycles

  13. Static slicing • Martin, Huddart [2000]: Graph pruning algorithm • Barnat, et al. [2001]: Extra data structures to keep track of 2ndsearches • Brim , et al. [2001]: Negative cycle detection • Bollig , et al. [2002]: Algorithm without cycle detection

  14. Parallel and distributed codes • Hard to test • Poor coverage • Programmers have less intuition • Parallel and distributed programs force us to encounter all possibleinterleavings – generates large models • One of the common heuristics to reduce the model is partial-orderreductions

  15. Formal Methods for Parallel Programming: Theory and Applications • FMPPTA'2001: April 27, 2001, San Francisco • FMPPTA'2002 : April 19, 2002, Fort Lauderdale • FMPPTA'2003: April 22-26, 2003, Nice

  16. Parallel oriented model checker • VeriSoft from Lucent: mainly useful for explicit model checking • Spin from Bell Labs: is the most widely distributed software package that supports the formalverification of distributed systems SPIN2003, May 3-10, 2003, in Portland

  17. What we intend to do • Create own software for distributed state space construction algorithm • Use PC Myrinet cluster. • Implement a known parallel algorithm (Garavel). • Use message passing interface MPI. • Port the code on Grids, using Globus technologies. • Verify various properties of the tested software. • At start, small Java programs for which wecan manually generate a model.

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