CTDNet-A Mechanism for the Concurrent Execution of Lambda Graphs
IEEE Transactions on Software Engineering
Advances in parallel algorithms
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Parallel computing (2nd ed.): theory and practice
CTDNet III—an eager reduction model with laziness features
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ACM Computing Surveys (CSUR)
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A Multithreaded Substrate and Compilation Model for the Implicity Parallel Language pH
LCPC '96 Proceedings of the 9th International Workshop on Languages and Compilers for Parallel Computing
List Processing with a Data Flow Machine
Proceedings of RIMS Symposium on Software Science and Engineering
Non-strict execution in parallel and distributed computing
International Journal of Parallel Programming
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Much work has been done to implement declarative languages in parallel form. Most of them tend to resort to imperative features for some purposes, particularly for description of the parallelism. We propose parallel computation on associative networks, a machine independent parallel programming model, for automatic extraction of available inherent parallelism and optimization of declarative programs. Associative networks are used for representing program-like and data-like information. The computation follows the transformation style of information processing. All computational mechanisms are oriented toward the processing incomplete information and perform parallel partial evaluation. This partial evaluation is a base of the proposed technique for automatic transforming, optimizing, and parallelizing declarative programs.