Parallel Algorithms for Operations on Multi-valued Decision Diagrams

Abstract : Multi-valued Decision Diagrams (MDDs) have been extensively studied in the last ten years. Recently, efficient algorithms implementing operators such as reduction, union, intersection , difference, etc., have been designed. They directly deal with the graph structure of the MDD and a time reduction of several orders of magnitude in comparison to other existing algorithms have been observed. These operators have permitted a new look at MDDs, because extremely large MDDs can finally be manipulated as shown by the models used to solve complex application in music generation. However, MDDs become so large (50GB) that minutes are sometimes required to perform some operations. In order to accelerate the manipulation of MDDs, parallel algorithms are required. In this paper , we introduce such algorithms. We carefully design them in order to overcome inherent difficulties of the paralleliza-tion of sequential algorithms such as data dependencies, software lock-out, false sharing, or load balancing. As a result, we observe a speed-up , i.e. ratio between parallel and sequential runtimes, growing linearly with the number of cores.
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Contributor : Jean-Charles Regin <>
Submitted on : Thursday, September 20, 2018 - 2:44:28 PM
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Jean-Charles Régin, Guillaume Perez. Parallel Algorithms for Operations on Multi-valued Decision Diagrams. AAAI 2018 Conference | The Thirty-Second AAAI Conference on Artificial Intelligence, Feb 2018, New Orleans, France. ⟨hal-01877882⟩

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