数据流语言

W. Ackerman
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引用次数: 76

摘要

有几种计算机系统体系结构的目标是利用并行——多处理器、向量机和阵列处理器。对于这些体系结构中的每一种,都有尝试设计编译器来优化用传统语言编写的程序。FORTRAN语言的“向量化”编译器)。为了方便使用这些系统,还出现了新的语言设计,例如用于多处理器的Concurrent PASCAL 6,以及直接利用这些系统特性的语言,例如用于Illiac IV阵列处理器的GLYPNIR 19和FORTRAN的各种“矢量化”方言。这些语言几乎总是使程序员可以看到计算机的多处理器、向量或数组属性——也就是说,它们实际上是程序员帮助编译器揭示并行性的工具。这些语言或方言中的许多都是“不自然的”,因为它们紧密地反映了它们所设计的系统的行为,而不是反映程序员思考问题解决方案的方式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data flow languages
There are several computer system architectures which have the goal of exploiting parallelism—multiprocessors, vector machines and array processors. For each of these architectures there have been attempts to design compilers to optimize programs written in conventional languages (e.g. "vectorizing" compilers for the FORTRAN language). There have also been new language designs to facilitate using these systems, such as Concurrent PASCAL for multiprocessors,6 and languages that utilize the features of such systems directly, such as GLYPNIR for the Illiac IV array processor19 and various "vectorizing" dialects of FORTRAN. These languages almost always make the multiprocessor, vector, or array properties of the computer visible to the programmer—that is, they are actually vehicles whereby the programmer helps the compiler uncover parallelism. Many of these languages or dialects are "unnatural" in that they closely reflect the behavior of the system for which they were designed, rather than reflecting the way programmers think about problem solutions.
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