Eliminating Dark Bandwidth: A Data-Centric View of Scalable, Efficient Performance, Post-Moore
Eliminating Dark Bandwidth: A Data-Centric View of Scalable, Efficient Performance, Post-Moore
复制标题
消除暗带宽：以数据为中心的可扩展、高效性能、后摩尔定律的观点
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
J. Randall
中科院分区：
文献类型：
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作者：
J. Beard;J. Randall
大多数计算研究都集中在计算技术本身以及完整的系统如何使用它们（例如，内存结构、互连、软件和计算元素的组合）。技术人员在很大程度上未能将计算系统视为一个整体，而是大多孤立地优化子系统。例如，结果是，在构建的系统中，应用程序只能请求固定倍数的数据（例如，来自 DRAM 的 64 字节），即使所需的数据要少得多。从硬件接口的角度来看，这是有效的，但是，它会导致消耗核心从未使用过的宝贵带宽；这个隐藏的带宽对系统来说实际上是黑暗的。暗带宽的原因是系统性的，内置于我们的虚拟内存抽象和内存接口的核心。继续关注更新的、革命性的内存技术来提高表面性能特征，而不专注于减少数据移动的系统只会将这个问题推到未来的系统上。本文研究了暗带宽问题，并提供了一种整体方法来减少未来计算系统中的整体数据移动。
Most of computing research has focused on the computing technologies themselves versus how full systems make use of them (e.g., memory fabric, interconnect, software, and compute elements combined). Technologists have largely failed to look at the compute system as a whole, instead optimizing subsystems mostly in isolation. The result, for example, is that systems are built where applications can only ask for a fixed multiple of data (e.g., 64-bytes from DRAM), even if what is required is far less. This is efficient from a hardware interface perspective, however, it results in consuming valuable bandwidth that is never utilized by the core; this hidden bandwidth is effectively dark to the system. The causes of dark bandwidth are systemic, built into the very core of our virtual memory abstractions and memory interfaces. Continued focus on newer, revolutionary memory technologies to improve surface performance characteristics without a systems focus on reducing data movement will simply push this problem off onto future systems. This paper examines the problem of dark bandwidth and offers a holistic approach to reduce overall data movement within future compute systems.