SI2-SSE: Gunrock: High-Performance GPU Graph Analytics

许多数据集可以用“图表”表示。图形表示实体之间的关系，这些实体和关系可用于解决许多领域中感兴趣的问题。例如，社交图（如Facebook的）通过友谊（关系）将人（实体）联系起来，通过该图，Facebook可以向您推荐可能是您的朋友的人。亚马逊可能会使用一个由人和销售物品（实体）组成的图表，通过购买这些物品的人（关系）来建议您可能想要购买的物品。信用卡公司可能会查看您的购买模式，甚至在您知道信用卡被盗之前就发现可能的欺诈行为。图表在许多科学领域也很有用，如基因组学、流行病学和经济学。这个项目使用一个新兴的可编程处理器，图形处理器（GPU），来解决图形问题。GPU正在迅速进入我们国家最大的数据中心和超级计算机。该项目团队正在构建一个图形计算系统，该系统将显着提高这些问题的性能。在这个项目中，团队将与计算社区和科学社区合作，这两个社区都有许多有趣的，具有挑战性的图形计算问题，这个系统将针对这些问题。该系统是开源软件，可供世界各地的研究人员和工业界免费使用。该项目由高级网络基础设施办公室（Office of Advanced Cyberinfrastructure）支持，旨在为图形处理器（GPU）开发“Gunrock”可编程、高性能、开源图形分析库，从工作原型到GPU计算生态系统的强大、可持续、开源组件。Gunrock的优势在于其编程模型和高度优化的实现。通过这项工作，项目团队希望通过提高Gunrock的可扩展性，功能，核心运算符和支持的图形计算来解决Gunrock在计算和科学社区中的可用性。在这项工作中，该团队将与GPU Open Analytics Initiative和NSF赞助的CINET网络科学项目合作，以确保我们的工作产生尽可能广泛的影响。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响评审标准进行评估，被认为值得支持。

Many sets of data can be represented as "graphs". Graphs express relationships between entities, and those entities and relationships can be used to solve problems of interest in many fields. For instance, a social graph (like Facebook's) links people (entities) by friendships (relationships), and with that graph, Facebook can suggest people to you who might be your friends. Amazon might use a graph made of people and items for sale (entities) connected by who bought those items (relationships) to suggest items you might want to buy. A credit card company might look at your pattern of purchases and detect possible fraud even before you know your credit card was stolen. Graphs are also useful in many fields of science, such as genomics, epidemiology, and economics. This project uses an emerging programmable processor, the graphics processor (GPU), to solve graph problems. GPUs are rapidly moving into our nation's largest data centers and supercomputers. The project team is building a system for computation on graphs that will significantly improve performance on these problems. In this project, the team will work with the computing community and the scientific community, both of whom have numerous interesting, challenging graph computation problems that this system will target. The system is open-source software and can be used freely by researchers and industry all over the world.This project, supported by the Office of Advanced Cyberinfrastructure seeks to develop the "Gunrock" programmable, high-performance, open-source graph analytics library for graphics processors (GPUs) from a working prototype to a robust, sustainable, open-source component of the GPU computing ecosystem. Gunrock's strengths are its programming model and highly optimized implementation. With this work the project team hopes to address Gunrock's usability in the computing and scientific communities by improving Gunrock's scalability, capabilities, core operators, and supported graph computations. In this work the team will collaborate with the GPU Open Analytics Initiative and the NSF-sponsored CINET project for network science to ensure that our work has the broadest possible impact.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.