SHF: Large: Collaborative Research: PXGL: Cyberinfrastructure for Scalable Graph Execution

历史上，世界上最强大的计算系统一直致力于解决科学问题。直到最近，这些系统进行的计算通常是对各种物理现象的模拟。然而，一种新的科学发现范式的重要性正在稳步上升，即数据密集型科学，它将复杂的分析技术集中在科学、商业和社会活动中产生的大量（并且不断增加的）数据上。基于数据密集型科学的重要研究包括知识发现、生物信息学、蛋白质组学和基因组学、数据挖掘和搜索、电子设计自动化、计算机视觉和互联网路由等领域。不幸的是，数据密集型科学所需的计算方法与那些基于模拟的超级计算非常有效的方法明显不同。为了有效执行数据密集型科学问题，该项目将为数据密集型科学开发一个全面的硬件和软件超级计算系统。图算法和数据结构是数据密集型计算的基础，因此，这个项目的重点是提供对大规模图处理基础知识的基本的、新的理解，以及如何构建可扩展的系统来有效地解决大规模图问题。特别是，这项工作将描述处理开销和图形处理可扩展性的限制，开发适当捕获图形算法的性能模型，定义开发特定于图形的硬件的（协同设计）过程，并通过原型执行环境实验验证我们的方法。我们系统的关键功能包括：一个新颖的细粒度并行编程模型，一个可扩展的图算法和数据结构库，一个图优化的核心架构，以及一个可扩展的图执行平台。该项目还将解决在为数据密集型问题构建可扩展和可靠的软件时所涉及的编程挑战。

The most powerful computing systems in the world have historically been dedicated to solving scientific problems. Until recently, the computations performed by these systems have typically been simulations of various physical phenomena. However, a new paradigm for scientific discovery has been steadily rising in importance, namely, data-intensive science, which focuses sophisticated analysis techniques on the enormous (and ever increasing) amounts of data being produced in scientific, commercial, and social endeavors. Important research based on data-intensive science include areas as diverse as knowledge discovery, bioinformatics, proteomics and genomics, data mining and search, electronic design automation, computer vision, and Internet routing. Unfortunately, the computational approaches needed for data-intensive science differ markedly from those that have been so effective for simulation-based supercomputing. To enable and facilitate efficient execution of data-intensive scientific problems, this project will develop a comprehensive hardware and software supercomputing system for data-intensive science.Graph algorithms and data structures are fundamental to data-intensive computations and, consequently, this project is focused on providing fundamental, new understandings of the basics of large-scale graph processing and how to build scalable systems to efficiently solve large-scale graph problems. In particular, this work will characterize processing overheads and the limits of graph processing scalability, develop performance models that properly capture graph algorithms, define the (co-design) process for developing graph-specific hardware, and experimentally verify our approach with a prototype execution environment. Key capabilities of our system include: a novel fine-grained parallel programming model, a scalable library of graph algorithms and data structures, a graph-optimized core architecture, and a scalable graph execution platform. The project will also address the programming challenges involved in constructing scalable and reliable software for data-intensive problems.