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Parallel sifting algorithm

机译:并行筛选算法

摘要

A binary decision diagram (BDD) representing a function having n variables is accessed and the n variables of the BDD are reordered by iteratively moving k variables of the n variables to their locally optimum layers, until a size of the BDD has reached a desired threshold, wherein each iteration comprises: selecting, from the n layers, k layers that currently have the k largest sizes among the n layers, wherein the k variables are currently positioned at the k layers; iteratively and concurrently moving the k variables to different layers of the BDD until each of the k variables has been at all the n layers to determine a locally optimum layer for each of the k variables, wherein the locally optimum layer of a variable during each iteration is one of the n layers that currently yields a smallest size among the n layers with the variable at each of the n layers; and concurrently moving the k variables to their respective locally optimum layers.
机译:访问表示具有n个变量的函数的二进制决策图(BDD),并通过将n个变量中的k个变量迭代移动到其局部最优层来重新排序BDD的n个变量,直到BDD的大小达到所需的阈值为止,其中每个迭代包括:从n个层中选择n个层中当前具有k个最大尺寸的k个层,其中k个变量当前位于k个层;迭代并同时将k变量移动到BDD的不同层,直到k个变量中的每一个都位于所有n层,以确定k个变量中的每一个的局部最优层,其中,变量在每次迭代过程中的局部最优层是n层中当前产生的最小尺寸的n层之一,并且在n层的每一层都有变量;同时将k个变量移动到它们各自的局部最优层。

著录项

  • 公开/公告号EP2439666B1

    专利类型

  • 公开/公告日2018-08-08

    原文格式PDF

  • 申请/专利权人 FUJITSU LIMITED;

    申请/专利号EP20110184514

  • 发明设计人 JAIN JAWAHAR;STERGIOU STERGIOS;

    申请日2011-10-10

  • 分类号G06F17/50;

  • 国家 EP

  • 入库时间 2022-08-21 13:20:20

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