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• W4247714695 abstract "Finite-State Machine (FSM) applications are important for many domains. But FSM computation is inherently sequential, making such applications notoriously difficult to parallelize. Most prior methods address the problem through speculations on simple heuristics, offering limited applicability and inconsistent speedups. This paper provides some principled understanding of FSM parallelization, and offers the first disciplined way to exploit application-specific information to inform speculations for parallelization. Through a series of rigorous analysis, it presents a probabilistic model that captures the relations between speculative executions and the properties of the target FSM and its inputs. With the formulation, it proposes two model-based speculation schemes that automatically customize themselves with the suitable configurations to maximize the parallelization benefits. This rigorous treatment yields near-linear speedup on applications that state-of-the-art techniques can barely accelerate." @default.
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• W4247714695 date "2014-02-24" @default.
• W4247714695 modified "2023-09-26" @default.
• W4247714695 title "Challenging the embarrassingly sequential" @default.
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• W4247714695 doi "https://doi.org/10.1145/2644865.2541989" @default.
• W4247714695 hasPublicationYear "2014" @default.
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