A dataflow-based APL for the hypercube

A. Mazer
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引用次数: 2

Abstract

Traditional hypercube programming has three main characteristics. Most is done in a compiled language, FORTRAN or C, directly for the hypercube architecture and usually, one particular hypercube operating system. Secondly, algorithms have had very symmetrical decompositions; each node does essentially the same thing as other nodes. Similarly, data decomposition has normally been very regular, To an extent, these characteristics are very understandable. The people coding for the hypercube have been programmers writing code to solve problems suited to their particular hypercube as quickly as possible. The hypercube architecture is obviously well-suited to regular problems. The hypercube is reaching a stage of maturity, however, at which it’s appropriate to consider alternatives to these methods. In particular, compiled languages such as FORTRAN and C offer little to the casual user in the way of a convenient development environment or real-time feedback. Moreover, individual users must either build up and maintain a software library or recode commonly-used routines as they develop applications. The user must be familiar with the operating system interface to the hypercube, be willing to change code if the program needs to be ported, and be willing and able to convert the code into a parallel implementation.
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用于超立方体的基于数据流的APL
传统的超立方体编程有三个主要特点。大多数都是用编译语言(FORTRAN或C)完成的,直接用于超多维数据集体系结构,通常是一个特定的超多维数据集操作系统。其次,算法有非常对称的分解;每个节点基本上与其他节点做相同的事情。同样,数据分解通常也是非常规则的,在某种程度上,这些特征是非常可以理解的。为超立方体编写代码的人是程序员,他们编写代码以尽可能快地解决适合他们特定超立方体的问题。超立方体架构显然非常适合解决常规问题。然而,超立方体正在达到成熟阶段,此时考虑这些方法的替代方案是合适的。特别是,像FORTRAN和C这样的编译语言在方便的开发环境或实时反馈方面几乎没有为普通用户提供什么。此外,个人用户在开发应用程序时必须建立和维护一个软件库,或者重新编写常用的例程。用户必须熟悉hypercube的操作系统接口,在需要移植程序时愿意更改代码,并且愿意并能够将代码转换为并行实现。
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