在实际的大规模并行架构中,相对于静态字典的接近最优压缩

D. Belinskaya, S. Agostino, J. Storer
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引用次数: 29

摘要

我们考虑了相对于静态字典压缩文本的次线性大规模并行算法。PRAM模型的算法可以在O(m+log(n))时间内用n个处理器完成此任务,其中m是字典中最长条目的长度,n是输入字符串的长度。我们考虑的可能是大规模并行计算最实用的模型:一个处理器的线性阵列,其中每个处理器只连接到它的左右邻居。我们提出了一种算法,保证在n/(km)个处理器的O(km+mlog(m))时间内,对于任意整数k/spl ges/1,其最优性在(k+1)/k因子内。我们还提出了实验,表明在实践中表现可能更好。
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Near optimal compression with respect to a static dictionary on a practical massively parallel architecture
We consider sublinear massively parallel algorithms for compressing text with respect to a static dictionary. Algorithms for the PRAM model can do this optimally in O(m+log(n)) time with n processors, where m is the length of the longest entry in the dictionary and n is the length of the input string. We consider what is perhaps the most practical model of massively parallel computation imaginable: a linear array of processors where each processor is connected only to its left and right neighbors. We present an algorithm which in time O(km+mlog(m)) with n/(km) processors is guaranteed to be within a factor of (k+1)/k of optimal, for any integer k/spl ges/1. We also present experiments indicating that performance may be even better in practice.
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