ASC: an associative-computing paradigm
ASC: an associative-computing paradigm
复制标题
ASC：关联计算范式
作者：
J. Potter;J. Baker;S. Scott;A. Bansal;C. Leangsuksun;Chandra R. Asthagiri
如今计算速度的提高使传统的时序机器能够有效地模拟关联计算技术。我们提出了一种被称为ASC(关联计算)的并行编程范例，它被设计用于广泛的计算引擎。我们的范例有一个高效的基于关联的动态内存分配机制，它不使用指针。它在基本级引入了数据并行性，因此程序员不必指定排序、循环和并行化等低级顺序任务。我们的范例支持所有标准的数据并行和大规模并行计算算法。它将数值计算(如卷积、矩阵乘法和图形)与非数值计算(如编译、图形算法、基于规则的系统和语言解释器)结合在一起。本文关注的是ASC.<；<；ETX；>；
Today's increased computing speeds allow conventional sequential machines to effectively emulate associative computing techniques. We present a parallel programming paradigm called ASC (ASsociative Computing), designed for a wide range of computing engines. Our paradigm has an efficient associative-based, dynamic memory-allocation mechanism that does not use pointers. It incorporates data parallelism at the base level, so that programmers do not have to specify low-level sequential tasks such as sorting, looping and parallelization. Our paradigm supports all of the standard data-parallel and massively parallel computing algorithms. It combines numerical computation (such as convolution, matrix multiplication, and graphics) with nonnumerical computing (such as compilation, graph algorithms, rule-based systems, and language interpreters). This article focuses on the nonnumerical aspects of ASC.<<ETX>>