SHF:Medium:Overcoming the Intuition Wall: Automatic Graphical Analysis of Programs to Discover and Program New Computer Architectures

可编程特性是开发新计算机体系结构的核心。移动云模式的兴起增加了应用程序创建的多样性和速度，从而挑战了计算机架构师为其构建优化系统的能力。在过去，架构师能够检查感兴趣的软件代码（如果必要的话，通常通过缓慢而费力的手动检查），当发布之间的距离很远并且很少时，以获得进行架构和微架构发现所需的直觉。但是这种方法并不能扩展到每天都有数百个应用程序的新兴应用程序。此外，新的语言和平台具有与传统代码完全不同的行为，迫切需要对这些应用程序的直觉。如果没有新的方法来描述新兴的工作负载，计算机架构师就有可能陷入直觉的困境。如果不加以缓解，这种风险可能是灾难性的，因为（微）架构师需要开发更节能的设计，以弥补因Dennard缩放速度减慢而造成的损失。机器学习的进步为克服直觉墙提供了机会。在过去的十年中，基于社交网络中挖掘行为的需求/益处以及廉价的商品计算，图上的机器学习取得了许多重大进展。在这个项目中，PI计划利用这些进步来发现和编程新的计算机架构。通过将程序执行视为一个图，对这些图进行聚类，并挖掘它们的相似性，PI计划发现架构师和微架构师可以用来开发新的片上加速结构的新行为。PI还计划研究如何将遗留代码半自动转换为在具有新加速器的架构上执行。

Workload characterization is central to development of new computer architectures. The rise of the mobile-cloud paradigm has increased the diversity and rate at which applications are created thus challenging computer architects' ability to build optimized systems for them. In the past, architects have been able to examine software codes of interest (often through slow laborious manual inspection if necessary) when releases were far and few in between to derive intuition necessary to make architectural and microarchitectural discoveries. But this method does not scale to emerging applications that are literally hammered out in the hundreds by the day. Further, new languages and platforms have behaviors that are quite different from legacy codes and there is an urgent need for intuition on these applications. Without new methods to characterize emerging workloads, computer architects risk running into an intuition wall. This risk might prove calamitous if unmitigated, given the added reliance on (micro)architects to develop more energy efficient designs to compensate for the losses due to slowdowns in Dennard's scaling.Advances in machine learning provide an opportunity to overcome the intuition wall. In the last decade there have been many major advances in machine learning on graphs motivated by need/benefits of mining behaviors in social networks and enabled by cheap commodity computing. In this project, the PIs plan to leverage these advances to discover and program new computer architectures. By viewing program execution as a graph, clustering these graphs, and mining them for similarities, the PIs plan to discover new behaviors that architects and microarchitects can use to develop new on-chip acceleration structures. The PIs also plan to study how legacy code can semi-automatically be converted to execute on the architectures with the new accelerators.