A dynamic firing speculation to speedup distributed symbolic state-space generation

Chung, Ming-Ying; Ciardo, Gianfranco

doi:10.1109/ipdps.2006.1639371

Cited by 2 publications

References 24 publications

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Distributed Synthesis of Fault-Tolerant Programs in the High Atomicity Model

Bonakdarpour

Kulkarni

Abujarad

Lecture Notes in Computer Science

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Abstract. In this paper, we concentrate on distributed algorithms for automated synthesis of fault-tolerant programs in the high atomicity model, where all processes can read and write all program variables in one atomic step. Although there has recently been an increasing interest in using parallel and distributed techniques in the model checking community, these technique have not been investigated in program synthesis. Developing such techniques is crucial as a means to cope with the state explosion problem in the context of program synthesis and transformation as well. We propose two distributed multithreaded algorithms for adding two levels of fault-tolerance, namely failsafe and masking, to existing fault-intolerant programs whose state space is distributed over a network or cluster of workstations.

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Distributed Synthesis of Fault-Tolerant Programs in the High Atomicity Model

Bonakdarpour

Kulkarni

Abujarad

Lecture Notes in Computer Science

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Parallel symbolic state-space exploration is difficult, but what is the alternative?

Ciardo

Zhao

Jin

2009

Electron. Proc. Theor. Comput. Sci.

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State-space exploration is an essential step in many modeling and analysis problems. Its goal is to find the states reachable from the initial state of a discrete-state model described. The state space can used to answer important questions, e.g., "Is there a dead state?" and "Can N become negative?", or as a starting point for sophisticated investigations expressed in temporal logic. Unfortunately, the state space is often so large that ordinary explicit data structures and sequential algorithms cannot cope, prompting the exploration of (1) parallel approaches using multiple processors, from simple workstation networks to shared-memory supercomputers, to satisfy large memory and runtime requirements and (2) symbolic approaches using decision diagrams to encode the large structured sets and relations manipulated during state-space generation. Both approaches have merits and limitations. Parallel explicit state-space generation is challenging, but almost linear speedup can be achieved; however, the analysis is ultimately limited by the memory and processors available. Symbolic methods are a heuristic that can efficiently encode many, but not all, functions over a structured and exponentially large domain; here the pitfalls are subtler: their performance varies widely depending on the class of decision diagram chosen, the state variable order, and obscure algorithmic parameters. As symbolic approaches are often much more efficient than explicit ones for many practical models, we argue for the need to parallelize symbolic state-space generation algorithms, so that we can realize the advantage of both approaches. This is a challenging endeavor, as the most efficient symbolic algorithm, Saturation, is inherently sequential. We conclude by discussing challenges, efforts, and promising directions toward this goal

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A dynamic firing speculation to speedup distributed symbolic state-space generation

Abstract: Abstract

Cited by 2 publications

References 24 publications

Distributed Synthesis of Fault-Tolerant Programs in the High Atomicity Model

Distributed Synthesis of Fault-Tolerant Programs in the High Atomicity Model

Parallel symbolic state-space exploration is difficult, but what is the alternative?

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