SPX: Collaborative research: Scalable Heterogeneous Migrating Threads for Post-Moore Computing

该项目将推动极端和异类并行的计算机体系结构和编程系统的最先进水平。很明显，后摩尔定律时代将需要对计算机系统进行重大颠覆。这个项目将解决这个新时代的计算机体系结构和编程系统挑战，重点放在通过保留冯·诺伊曼计算模型的一些原则(不同于更具探索性的方法，如生物或量子计算)，有望在规模、成本效益和可用性方面可扩展的方法。通过强调数据分析，这项工作还将使迅速增长的现代生活样本(商业、网络、国家安全、社交网络)受益。更深入地了解如何使这些应用程序更具可扩展性，并具有足够的响应性，以应对日益增长的实时要求，应该会在日常生活中产生更广泛的影响，具有巨大的技术过渡潜力。它还与教学和劳动力发展有直接联系，因为这项提案的硬件和软件方面都可以使广泛的学生更好地了解未来技术路线图中预测的更广泛的计算平台多样性。在本奖项中开发的SHMT(可伸缩异类迁移线程)模型将包括对迁移线程和异步任务模型的扩展以支持异构性，以及对事务和参与者模型的扩展以支持数据一致性。此外，研究人员建议使用数据分析图问题来评估他们的研究，因为这些应用在实践中很重要，而且在当前的系统上解决起来很有挑战性。考虑到此类计算的大小、复杂性和动态性质预计将继续增长，了解如何在包括高速更新和查询流的环境中以一种可以扩展到非常高的并发级别的方式实现它们具有越来越大的价值。这些技术还可以应用于其他应用程序类别，例如数据稀疏或不规则的科学应用程序。这个为期3年的研究项目的总体目标是推进计算机体系结构和编程系统的基础，以应对可伸缩并行和极端异构性的新挑战，重点是数据分析和解决数据一致性、系统管理、资源分配和任务调度问题。调查人员将利用他们在体系结构和编程系统领域的独特但协同的专业知识，建立并整合他们过去在迁移线程和近内存处理、针对异类计算的异步任务并行的软件支持以及数据分析方面的工作。佐治亚理工学院的新型计算层次结构研究中心(CRNCH)将提供第一个用于评估新概念的替代系统。行业合作者包括Lexis-Nexis Risk Solutions和Kyndi，对他们来说，图形数据分析是其业务模型的核心。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The project will advance the state of the art in computer architecture and programming systems for extreme and heterogeneous parallelism. It is clear that the post-Moore' law era will require major disruptions in computing systems. This project will address computer architecture and programming system challenges for this new era, with a focus on approaches that are expected to be scalable in size, cost effectiveness, and usability by retaining some tenets of the von Neumann computing model (unlike more exploratory approaches like biological or quantum computing). By emphasizing data analytics, the work will also benefit a rapidly growing swatch of modern life (commercial, cyber, national security, social networks). A deeper understanding of how such applications can be made more scalable, and responsive enough to handle increasing real-time requirements, should lead to wider impacts across every-day life with significant potential for technology transition. There is also a direct connection to pedagogy and workforce development, since both hardware and software aspects of this proposal can enable a broad range of students to better understand the wider diversity of computing platforms projected in future technology roadmaps. The SHMT (Scalable Heterogeneous Migrating Thread) model developed in this award will include extensions to the migrating threads and asynchronous task models to support heterogeneity, and extensions to the transaction and actor models to support data coherence. Further, the investigators propose to use data analytic graph problems to evaluate their research, since these applications are both important in practice and are challenging to solve on current systems. Given the expected continued increase in the size, complexity, and dynamic nature of such computations, it is of growing value to understand how to implement them in a manner that can scale to very high levels of concurrency in environments that include high rate streams of both updates and queries. These techniques can also apply to other application classes, such as scientific applications where data is sparse or irregular. The overall objective of this 3-year research project is to advance the foundations of computer architecture and programming systems to address the emerging challenges of scalable parallelism and extreme heterogeneity, with an emphasis on data analytics and solving data coherence, system management, resource allocation, and task scheduling issues. The investigators will leverage their distinct but synergistic expertise in the architecture and programming systems areas by building on, and integrating, their past work on migrating threads and near-memory processing, software support for asynchronous task parallelism for heterogeneous computing, and data analytics. The Center for Research into Novel Computing Hierarchies (CRNCH) at Georgia Tech will provide access to first-of-a-kind alternative systems for use in evaluating the new concepts. Industrial collaborators include Lexis-Nexis Risk Solutions and Kyndi, for whom graph data analytics are central to their business model.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.