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

SPX：协作研究：后摩尔计算的可扩展异构迁移线程

基本信息

批准号：
1822939
负责人：
Peter Kogge
金额：
$ 52.45万
依托单位：
University of Notre Dame
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-10-01 至 2023-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1822939&HistoricalAwards=false
关键词：
SPX Collaborative research Scalable Heterogeneous

项目摘要

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.

该项目将在计算机架构和编程系统中推进最高和异质并行性的技术。很明显，后期后的法律时代将需要计算系统中的重大干扰。该项目将解决这个新时代的计算机架构和编程系统挑战，重点是通过保留von Neumann计算模型的某些原则（与生物学或量子计算等更多探索性方法不同），预计有望在规模，成本效益和可用性方面具有可扩展性的方法。通过强调数据分析，这项工作还将受益于现代生活的快速增长（商业，网络，国家安全，社交网络）。更深入地了解如何使这些应用程序更具扩展性，并且足够迅速地应对实时需求的响应能力，这应该会导致每日生活的更大影响，并具有巨大的技术过渡潜力。与教育学和劳动力发展有直接的联系，因为该提案的硬件和软件方面都可以使广泛的学生能够更好地了解未来技术路线图中投影的计算平台的更广泛多样性。该奖项中开发的SHMT（可扩展的异质迁移线程）将包括向迁移线程和异步任务模型的扩展，以支持异质性，以及对交易和Actor模型的扩展，以支持数据相干性。此外，研究人员建议使用数据分析图问题来评估其研究，因为这些应用在实践中都很重要，并且在当前系统上挑战。考虑到此类计算的规模，复杂性和动态性质的预期持续增加，了解如何以可以扩展到包括更新和查询的高速速率流中的环境中的高度并发率的方式来了解它们的价值越来越大。这些技术也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。这个为期三年的研究项目的总体目标是推动计算机架构和编程系统的基础，以应对可扩展并行性和极端异质性的新兴挑战，重点是数据分析并解决数据连贯性，系统管理，资源分配和任务计划问题。研究人员将通过构建和整合到迁移线程和近乎内存处理的工作，在架构和编程系统领域中的独特但协同的专业知识，对异质计算的异步任务并行的软件支持以及数据分析。佐治亚理工学院的新型计算层次结构研究中心（CRNCH）将为您提供首个替代系统，以评估新概念。工业合作者包括Lexis-Nexis风险解决方案和Kyndi，对于其图形数据分析对其业务模型至关重要。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子的智力优点和更广泛影响的评估标准来通过评估来获得支持的。

项目成果

期刊论文数量（12）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Multi-threading Semantics for Highly Heterogeneous Systems Using Mobile Threads

使用移动线程的高度异构系统的多线程语义

DOI：
发表时间：
2019
期刊：
Int. Conf. on High Performance Computing & Simulation
影响因子：
0
作者：
Kogge, Peter M.
通讯作者：
Kogge, Peter M.

Greatly Accelerated Scaling of Streaming Problems with A Migrating Thread Architecture

通过迁移线程架构大大加速流处理问题的扩展

DOI：
10.1109/ia354616.2021.00009
发表时间：
2021
期刊：
2021 IEEE/ACM 11th Workshop on Irregular Applications: Architectures and Algorithms (IA3
影响因子：
0
作者：
Page, Brian A.;Kogge, Peter M.
通讯作者：
Kogge, Peter M.

Scalability of streaming on migrating threads

迁移线程上流的可扩展性

DOI：
发表时间：
2020
期刊：
IEEE High Performance Extreme Computing Conf. (HPEC
影响因子：
0
作者：
Page, Brian A.;Kogge, Peter M.
通讯作者：
Kogge, Peter M.

Scalability of Sparse Matrix Dense Vector Multiply (SpMV) on a Migrating Thread Architecture

迁移线程架构上稀疏矩阵密集向量乘法 (SpMV) 的可扩展性

DOI：
10.1109/ipdpsw50202.2020.00088
发表时间：
2020
期刊：
2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW
影响因子：
0
作者：
Page, Brian A.;Kogge, Peter M.
通讯作者：
Kogge, Peter M.

Locality: The 3rd Wall and The Need for Innovation in Parallel Architectures

局部性：第三堵墙和并行架构创新的需求