The Memory-Bounded Speedup Model and Its Impacts in Computing
The Memory-Bounded Speedup Model and Its Impacts in Computing
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DOI：
10.1007/s11390-022-2911-1
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发表时间：
2023-01
影响因子：
0.7
通讯作者：
Xian-He Sun;Xiaoyang Lu
中科院分区：
文献类型：
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作者：
Xian-He Sun;Xiaoyang Lu
随着大数据应用的激增和内存墙问题的日益严重，内存系统取代计算单元成为计算领域公认的主要关注点。然而，这种“以记忆为中心”的共识有一个卑微的开始。三十多年前，内存限制加速比模型是第一个将内存视为计算限制的模型，并提供了加速比的一般限制和计算-内存权衡公式。即使在那时，内存限制模型也受到了广泛的欢迎。在20世纪90年代，它立即被引入到几个先进的计算机体系结构和并行计算教科书中，作为可扩展计算的必备知识。其中包括Kai Hwang教授的著作《Scalable Parallel Computing》，他在书中介绍了内存限制加速模型作为Sun-Ni定律，与Amdahl定律和Gustafson定律平行。多年来，这种模型的影响已经远远超出了并行处理，并成为计算的基础。在本文中，我们将重新审视内存限制加速模型，并深入讨论其进展和影响，为这一特殊问题做出独特的贡献，激发大数据应用的新解决方案，并促进以数据为中心的思考和反思。
With the surge of big data applications and the worsening of the memory-wall problem, the memory system, instead of the computing unit, becomes the commonly recognized major concern of computing. However, this “memory-centric” common understanding has a humble beginning. More than three decades ago, the memory-bounded speedup model is the first model recognizing memory as the bound of computing and provided a general bound of speedup and a computing-memory trade-off formulation. The memory-bounded model was well received even by then. It was immediately introduced in several advanced computer architecture and parallel computing textbooks in the 1990’s as a must-know for scalable computing. These include Prof. Kai Hwang’s book “Scalable Parallel Computing” in which he introduced the memory-bounded speedup model as the Sun-Ni’s Law, parallel with the Amdahl’s Law and the Gustafson’s Law. Through the years, the impacts of this model have grown far beyond parallel processing and into the fundamental of computing. In this article, we revisit the memory-bounded speedup model and discuss its progress and impacts in depth to make a unique contribution to this special issue, to stimulate new solutions for big data applications, and to promote data-centric thinking and rethinking.