The drinking philosophers problem
ACM Transactions on Programming Languages and Systems (TOPLAS) - Lecture notes in computer science Vol. 174
Parallel program design: a foundation
Parallel program design: a foundation
Communication and concurrency
A discipline of multiprogramming: programming theory for distributed applications
A discipline of multiprogramming: programming theory for distributed applications
A Discipline of Programming
A General Resource Allocation Synchronization Problem
ICDCS '01 Proceedings of the The 21st International Conference on Distributed Computing Systems
Inferring Synchronization under Limited Observability
TACAS '09 Proceedings of the 15th International Conference on Tools and Algorithms for the Construction and Analysis of Systems: Held as Part of the Joint European Conferences on Theory and Practice of Software, ETAPS 2009,
A distributed maximal scheduler for strong fairness
DISC'07 Proceedings of the 21st international conference on Distributed Computing
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Typically, program design involves constructing a program P that implements a given specification S; that is, the set P of executions of P is a subset of the set S of executions satisfying S. In many cases, we seek a program P that not only implements S, but for which P = S. Then, every execution satisfying the specification is a possible execution of the program; we then call P maximal for the specification S. We argue that maximality is an important criterion in the context of designing concurrent programs because it disallows implementations that do not exhibit enough concurrency. In addition, a maximal solution can serve as a basis for deriving a variety of implementations, each appropriate for execution on a specific computing platform.This paper also describes a method for proving the maximality of a program with respect to a given specification. Even though we prove facts about possible executions of programs, there is no need to appeal to branching time logics; we employ a fragment of linear temporal logic for our proofs. The method results in concise proofs of maximality for several non-trivial examples. The method may also serve as a guide in constructing maximal programs.