Performance Evaluation of Quine-McCluskey Method on Multi-core CPU

H. Vu, Ngoc-Dai Bui, Anh-Tu Nguyen, ThanhBangLe
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引用次数: 3

Abstract

The Quine-McCluskey method is an algorithm to minimize Boolean functions. Although the method can be programmed on computers, it takes a long time to return the set of prime implicants, thus slowing the analysis and design of digital logic circuits. As a result, it slows down the dynamic reconfiguration process of programmable logic devices. In this paper, we first propose a data representation for storing implicants in memory to reduce the cache misses of the program. We then propose an algorithm to find all prime implicants of a Boolean function. The algorithm aims to reuse the data available on cache, thus decreasing cache misses. After that, we propose an algorithm for step 2 of the Quine-McCluskey method to select the minimal number of essential prime implicants. The evaluation shows that our proposals achieve much higher performance than the original Quine-McCluskey method. The number of essential prime implicants is a low percentage, less than 50%, of the total prime implicants generated in step 1 of the method.
Quine-McCluskey方法在多核CPU上的性能评价
Quine-McCluskey方法是一种最小化布尔函数的算法。虽然该方法可以在计算机上编程,但它需要很长时间才能返回一组素数蕴涵,从而减慢了数字逻辑电路的分析和设计。因此,它减慢了可编程逻辑器件的动态重构过程。在本文中,我们首先提出了一种将隐含式存储在内存中的数据表示,以减少程序的缓存丢失。然后,我们提出了一种算法来找到布尔函数的所有素数蕴涵。该算法旨在重用缓存中可用的数据,从而减少缓存丢失。在此基础上,我们提出了Quine-McCluskey方法的第二步算法,以选择最小数量的基本素数隐含子。评估结果表明,我们的方法比原来的Quine-McCluskey方法取得了更高的性能。在该方法的第1步中生成的总主要蕴涵数中,基本主要蕴涵数的数量是一个较低的百分比,小于50%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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