Project/Proposal Title: EAGER: Creating a New Paradigm for Computer Architecture and Implementation: The 10 X 10 Idea

微处理器在扩展性能方面面临着能量墙，这需要与过去25年的扩展技术（微架构创新和缓存）有很大的不同。在Exascale计算时间窗口（2018+）中，即使有并行性，先进的电路技术（如近阈值电压）和简化的微架构，能源将是关键的约束，这意味着需要最有效地利用每个晶体管开关来完成应用工作的架构（和软件）。许多研究人员已经发表了利用异质性或定制的技术，这些技术有可能降低能耗并提高性能，通常是10倍或更多。然而，由于90/10优化模型，这些技术中很少有大规模部署，该模型歧视仅使平均工作负载的一部分受益的创新。到目前为止，还没有系统的框架来考虑如何引入这种异构性，也没有纪律严明的方法来分析和优化共享类似属性的工作负载。我们建议探索性地开发10 x10，这是一种变革性的模型，通过查看10个应用程序计算结构集群，实现对每个集群的专业化，可以广泛利用定制来提高能效和性能。为了满足这些需求，我们将开发10 x10模型，这是一种用于分析工作负载的规范框架，将它们划分为单独的集群以实现能源和性能优化？以及异质性/定制的规范化引入？这产生了可理解和可预测的好处。 我们介绍了10 x10架构的概念，它利用10 x10框架来驱动节能和更高性能的微处理器的设计，在一个技术缩放制度，产生大量的晶体管，但适度的能量缩放。因为这些10 x10架构可以更好地利用他们的晶体管比常规复制的传统核心的并行性，可以预期它们在低并行度阶段（？sequential？）。此外，由于其更高的能源效率，它们也应该并行优于基于高并行性阶段的传统核心复制的并行系统。简而言之，如果10 x10研究成功，它将改变我们对应用程序工作负载分析、计算机体系结构和实现以及软件编译器工具的看法。如果成功，这些努力将改变计算研究社区和行业的思维。可能会爆发出一场？局部极小值被权力之墙和摩尔的终结所禁止的法律，使科学突破的物理和数学过程的exascale模拟促进。这项工作将与新的计算机科学家的教育和经验联系起来，因为它涉及到一个新颖的和潜在的变革性的架构思想。异构系统能源效率的改进将传播到新的NSF系统，如橡树岭的轨道2D实验系统。

Microprocessors are facing an energy wall in scaling performance which requires a major deviation from the scaling technologies of the past 25 years (microarchitecture innovation and caches). In the Exascale computing time window (2018+), even with parallelism, advanced circuit techniques such as near-threshold voltage, and simplified microarchitectures, energy will be the key constraint, implying that architectures (and software) which make most efficient use of each transistor switching to complete application work are needed. Many researchers have published techniques which exploit heterogeneity or customization which have the potential to reduce energy and increase performance, often by 10x or more. However, few of these techniques have made it into large-scale deployment because of the 90/10 optimization model, which discriminates against innovations which benefit only a portion of the average workload. To date there has been no systematic framework for thinking about how to introduce such heterogeneity and no disciplined method for analyzing and optimizing for workloads which shared like properties. We propose exploratory development of 10x10, a transformative model which enables both broad exploitation of customization for higher energy efficiency and performance, by looking at 10 clusters of application computational structure, enabling specialization for each.To meet these needs, we will develop a 10x10 model, a disciplined framework for analyzing workloads, dividing them in separate clusters for energy and performance optimization ? and disciplined introduction of heterogeneity/customization ? which yields understandable and predictable benefits. We introduce the notion of 10x10 architecture, which exploits the 10x10 framework to drive the design of energy efficient and higher performance microprocessors in a technology scaling regime which yields plentiful transistors, but modest energy scaling.Because these 10x10 architectures may make better use of their transistors than regular replication of traditional cores for parallelism, they can be expected to outperform them in low parallelism phases (?sequential?). In addition, because of their greater energy efficiency, they should also parallel outperform parallel systems based on the replication of traditional cores in phases with high parallelism. In short, if 10x10 research is successful, it will transform how we think about application workload analysis, computer architecture and implementation, and software compiler tools. If successful, these efforts will transform the thinking of the computing research community and the industry. The potential is to break out of a ?local minima? proscribed by the power wall and the end of Moore?s Law to enable scientific breakthroughs facilitated by exascale simulation of physical and mathematical processes. This work will tie into the education and experience of new computer scientists, dealing as it does with a novel and potentially transformative architecture idea. Improvements in energy efficiency on heterogeneous systems will be disseminated to new NSF systems such as the Track 2D experimental system at Oak Ridge.