The Aurora or-parallel Prolog system
New Generation Computing - Selected papers on parallel logic programming from the International Conference on Fifth Generation Computer Systems, 1988
Vector models for data-parallel computing
Vector models for data-parallel computing
The Muse Or-parallel Prolog model and its performance
Proceedings of the 1990 North American conference on Logic programming
Reform Prolog: the language and its implementation
ICLP'93 Proceedings of the tenth international conference on logic programming on Logic programming
Efficient techniques for fast nested barrier synchronization
Proceedings of the seventh annual ACM symposium on Parallel algorithms and architectures
&ACE: a high-performance parallel Prolog system
IPPS '95 Proceedings of the 9th International Symposium on Parallel Processing
Hints for computer system design
SOSP '83 Proceedings of the ninth ACM symposium on Operating systems principles
Data parallel logic programming in &ACE
SPDP '95 Proceedings of the 7th IEEE Symposium on Parallel and Distributeed Processing
Parallel implementation of Prolog: the ACE project
ACM SIGSOFT Software Engineering Notes
Parallel execution of prolog programs: a survey
ACM Transactions on Programming Languages and Systems (TOPLAS)
Backtracking in Independent And-Parallel Implementations of Logic Programming Languages
IEEE Transactions on Parallel and Distributed Systems
PALS: An Or-Parallel Implementation of Prolog on Beowulf Architectures
Proceedings of the 17th International Conference on Logic Programming
Multiset Constraints and P Systems
WMP '00 Proceedings of the Workshop on Multiset Processing: Multiset Processing, Mathematical, Computer Science, and Molecular Computing Points of View
Issues in parallel execution of non-monotonic reasoning systems
Parallel Computing
Journal of Parallel and Distributed Computing
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Naive parallel implementation of nondeterministic systems (such as a theorem proving system) and languages (such as a logic, constraint, or a concurrent constraint language) can result in poor performance. We present three optimization schemas based on flattening of the computation tree, procrastination of overheads, and sequentialization of computations that can be systematically applied to parallel implementations of non-deterministic systems/languages to reduce the parallel overhead and to obtain improved efficiency of parallel execution. The effectiveness of these schemas is illustrated by applying them to the ACE parallel logic programming system. Performance data presented shows that considerable improvement in performance can result.