Speculative data-parallelalgorithms for language recognition have been widely experimented for various types of finite-state automata (FA), deterministic (DFA) and nondeterministic (NFA), often derived from regular e...
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Speculative data-parallelalgorithms for language recognition have been widely experimented for various types of finitestate automata (FA), deterministic (DFA) and nondeterministic (NFA), often derived fromregular exp...
详细信息
ISBN:
(纸本)9798400714436
Speculative data-parallelalgorithms for language recognition have been widely experimented for various types of finitestate automata (FA), deterministic (DFA) and nondeterministic (NFA), often derived fromregular expressions (RE). Such an algorithm cuts the input string into chunks, independently recognizes each chunk in parallel by means of identical FAs, and at last joins the chunk results and checks the overall consistency. In chunk recognition, it is necessary to speculatively start the FAs in any state, thus causing an overhead that reduces the speedup over a serial algorithm. The existing data-parallel DFA-based recognizers suffer from an excessive number of starting states, and the NFA-based ones suffer from the number of nondeterministic transitions.
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