The Experts below are selected from a list of 15 Experts worldwide ranked by ideXlab platform
Vijay K Garg - One of the best experts on this subject based on the ideXlab platform.
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optimistic distributed simulation based on Transitive Dependency tracking
Workshop on Parallel and Distributed Simulation, 1997Co-Authors: Om P Damani, Yimin Wang, Vijay K GargAbstract:In traditional optimistic distributed simulation protocols, a logical process (LP) receiving a straggler rolls back and sends out anti-messages. The receiver of an anti-message may also roll back and send out more anti-messages. So a single straggler may result in a large number of anti-messages and multiple rollbacks of some LPs. In the authors' protocol, an LP receiving a straggler broadcasts its rollback. On receiving this announcement, other LPs may roll back but they do not announce their rollbacks. So each LP rolls back at most once in response to each straggler. Anti-messages are not used. This eliminates the need for output queues and results in simple memory management. It also eliminates the problem of cascading rollbacks and echoing, and results in faster simulation. All this is achieved by a scheme for maintaining Transitive Dependency information. The cost incurred includes the tagging of each message with extra Dependency information and the increased processing time upon receiving a message. They also present the similarities between the two areas of distributed simulation and distributed recovery. They show how the solutions for one area can be applied to the other area.
Om P Damani - One of the best experts on this subject based on the ideXlab platform.
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optimistic distributed simulation based on Transitive Dependency tracking
Workshop on Parallel and Distributed Simulation, 1997Co-Authors: Om P Damani, Yimin Wang, Vijay K GargAbstract:In traditional optimistic distributed simulation protocols, a logical process (LP) receiving a straggler rolls back and sends out anti-messages. The receiver of an anti-message may also roll back and send out more anti-messages. So a single straggler may result in a large number of anti-messages and multiple rollbacks of some LPs. In the authors' protocol, an LP receiving a straggler broadcasts its rollback. On receiving this announcement, other LPs may roll back but they do not announce their rollbacks. So each LP rolls back at most once in response to each straggler. Anti-messages are not used. This eliminates the need for output queues and results in simple memory management. It also eliminates the problem of cascading rollbacks and echoing, and results in faster simulation. All this is achieved by a scheme for maintaining Transitive Dependency information. The cost incurred includes the tagging of each message with extra Dependency information and the increased processing time upon receiving a message. They also present the similarities between the two areas of distributed simulation and distributed recovery. They show how the solutions for one area can be applied to the other area.
Guyvincent Jourdan - One of the best experts on this subject based on the ideXlab platform.
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incremental Transitive Dependency tracking in distributed computations
Parallel Processing Letters, 1996Co-Authors: Claude Jard, Guyvincent JourdanAbstract:The notion of causal Dependency between events in distributed systems plays a central role in reasoning about distributed program behaviours [14]. Different techniques have been designed to track these dependencies during execution. We suggest a new incremental Transitive Dependency tracking technique. Once the Transitive dependencies are recorded for an observable event, the Dependency tracking cost can be reduced by propagating only future dependencies beyond that event. Furthermore, in contrast with the direct Dependency tracking technique already proposed in the literature, our technique allows to compute the dependencies among an arbitrary subset of observable events. This gives an interesting filtering capability.
Yimin Wang - One of the best experts on this subject based on the ideXlab platform.
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optimistic distributed simulation based on Transitive Dependency tracking
Workshop on Parallel and Distributed Simulation, 1997Co-Authors: Om P Damani, Yimin Wang, Vijay K GargAbstract:In traditional optimistic distributed simulation protocols, a logical process (LP) receiving a straggler rolls back and sends out anti-messages. The receiver of an anti-message may also roll back and send out more anti-messages. So a single straggler may result in a large number of anti-messages and multiple rollbacks of some LPs. In the authors' protocol, an LP receiving a straggler broadcasts its rollback. On receiving this announcement, other LPs may roll back but they do not announce their rollbacks. So each LP rolls back at most once in response to each straggler. Anti-messages are not used. This eliminates the need for output queues and results in simple memory management. It also eliminates the problem of cascading rollbacks and echoing, and results in faster simulation. All this is achieved by a scheme for maintaining Transitive Dependency information. The cost incurred includes the tagging of each message with extra Dependency information and the increased processing time upon receiving a message. They also present the similarities between the two areas of distributed simulation and distributed recovery. They show how the solutions for one area can be applied to the other area.
Claude Jard - One of the best experts on this subject based on the ideXlab platform.
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incremental Transitive Dependency tracking in distributed computations
Parallel Processing Letters, 1996Co-Authors: Claude Jard, Guyvincent JourdanAbstract:The notion of causal Dependency between events in distributed systems plays a central role in reasoning about distributed program behaviours [14]. Different techniques have been designed to track these dependencies during execution. We suggest a new incremental Transitive Dependency tracking technique. Once the Transitive dependencies are recorded for an observable event, the Dependency tracking cost can be reduced by propagating only future dependencies beyond that event. Furthermore, in contrast with the direct Dependency tracking technique already proposed in the literature, our technique allows to compute the dependencies among an arbitrary subset of observable events. This gives an interesting filtering capability.
