Parallel random access machines with bounded memory wordsize

IF 1 4区 计算机科学 Q3 COMPUTER SCIENCE, THEORY & METHODS Information and Computation Pub Date : 1991-04-01 DOI:10.1016/0890-5401(91)90069-E
Stephen J. Bellantoni
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Abstract

The PRAM model of parallel computation is examined with respect to wordsize, the number of bits which can be held in each global memory cell. First, adversary arguments are used to show the incomparability of certain machines which store the same amount of global information but which differ in wordsize. Next, for machines with infinitely many memory cells, a counting argument is used to show a large lower bound and to separate a hierarchy of machine classes based on wordsize. Finally, an efficient simulation by boolean circuits is used to give a simple new proof of the tight Ω((log n)(log log n)) time bound for parity on small-wordsize machines. Overall the results suggest that, in some circumstances, the memory wordsize is a more significant resource than the write resolution rule, number of memory cells, or number of processors.
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具有有限内存字长的并行随机存取机
并行计算的PRAM模型在字长方面进行了检查,每个全局存储单元中可以保存的比特数。首先,对手的论点被用来表明某些机器的不可比较性,这些机器存储了相同数量的全局信息,但单词大小不同。接下来,对于具有无限多内存单元的机器,使用计数参数来显示一个较大的下界,并根据单词大小分离机器类的层次结构。最后,通过布尔电路的有效模拟给出了一个简单的新证明,证明了在小单词大小的机器上奇偶性的严格Ω((log n)(log log n))时间限制。总的来说,结果表明,在某些情况下,内存字长是比写解析规则、内存单元数或处理器数更重要的资源。
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来源期刊
Information and Computation
Information and Computation 工程技术-计算机:理论方法
CiteScore
2.30
自引率
0.00%
发文量
119
审稿时长
140 days
期刊介绍: Information and Computation welcomes original papers in all areas of theoretical computer science and computational applications of information theory. Survey articles of exceptional quality will also be considered. Particularly welcome are papers contributing new results in active theoretical areas such as -Biological computation and computational biology- Computational complexity- Computer theorem-proving- Concurrency and distributed process theory- Cryptographic theory- Data base theory- Decision problems in logic- Design and analysis of algorithms- Discrete optimization and mathematical programming- Inductive inference and learning theory- Logic & constraint programming- Program verification & model checking- Probabilistic & Quantum computation- Semantics of programming languages- Symbolic computation, lambda calculus, and rewriting systems- Types and typechecking
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