CAREER: An Algorithm and System Co-Designed Framework for Graph Sampling and Random Walk on GPUs

职业生涯：用于 GPU 上的图形采样和随机游走的算法和系统协同设计框架

• 批准号: 2326141
• 负责人: Hang Liu
• 金额: $ 58.4万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2026-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2326141&HistoricalAwards=false
• 关键词: CAREER; Algorithm; System; Co; Designed

Graph analytics is one of the key technologies to address the grand challenges of our time, such as understanding the spread of pandemics, designing extremely large-scale integrated circuits and uncovering software vulnerabilities among many others. However, as the size of the graph continues to grow, learning, mining and computing such gigantic graphs become ineffective, impractical, and potentially dire. Fortunately, Graph Sampling and Random Walk can dramatically reduce the size of the original graphs, while still capturing the desired properties for downstream graph analytics tasks. But a comprehensive system that can perform graph sampling and random walk on real-world trillion-edge graphs at an acceptable speed is absent. This research pioneers the effort of uniting various graph sampling and random walk algorithms behind a user-friendly framework that can take advantage of world-class Graphics Processing Unit (GPU) computing facilities, including the future exascale ones, to rapidly handle trillion-edge graphs. This project contributes to the U.S. national goal of increasing participation in science and engineering, which is crucial to America’s success in addressing global challenges, building a stronger and more diversified workforce, and meeting the needs of the global innovation economy. This project produces a high-performance software library that serves as a foundational tool for fellow science and engineering practitioners from academia, national laboratories and industry. With a commitment to helping K-12, undergraduate, female, and Underrepresented Minority (URM) populations in the Science, Technology, Engineering, and Mathematics (STEM) field through the interesting investment and rewarding education plan, this project lays out a comprehensive road map to prepare the next-generation high-performance graph analytics professional workers and researchers. This project revamps and creates core courses in both graduate and undergraduate levels for the PI's home department. To benefit the society at large, this project disseminates the project data, software, and publications to the broader research community at http://personal.stevens.edu/~hliu77/gsrw.html.The overarching goal of this research is to make graph sampling and random walk fast, scalable and user-friendly. Towards that end, this career proposal advocates algorithm and system co-designed researches. First, this research introduces novel update and construction designs for transition probability of various major Monte Carlo methods that are essential for fast sampling. Second, to fully unleash the potential of GPUs, this project formulates the key primitive into problems that can take advantage of general, and reserved tensor and ray tracing cores on GPUs. Third, based upon the asynchronous processing nature of graph sampling and random walk, this research exploits Remote Direct Memory Access (RDMA)-assisted task and partition adaptive scheduling mechanism to reduce the data transfers for scalable trillion-edge graph sampling and random walk. Last but not the least, this career research delivers a bias-centric framework, which offers end users expressiveness to program not only a variety of exiting GSRW algorithms but also future ones, and simplicity by hiding the aforementioned advanced optimization techniques.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.

图形分析是应对我们这个时代重大挑战的关键技术之一，例如了解流行病的传播、设计超大规模集成电路和发现软件漏洞等。然而，随着图的大小不断增长，学习、挖掘和计算如此巨大的图变得无效、不切实际，甚至可能变得可怕。幸运的是，图形采样和随机游走可以极大地减少原始图形的大小，同时仍然可以捕获下游图形分析任务所需的属性。但目前还没有一个全面的系统能够以可接受的速度在真实世界的万亿边图上执行图形采样和随机游动。这项研究率先将各种图形采样和随机游走算法统一在一个用户友好的框架后面，该框架可以利用世界级的图形处理单元(GPU)计算设施(包括未来的亿级计算设施)来快速处理万亿边图。该项目有助于实现美国增加科学和工程参与度的国家目标，这对美国成功应对全球挑战、建设更强大和更多元化的劳动力队伍以及满足全球创新经济的需求至关重要。这个项目产生了一个高性能的软件库，作为来自学术界、国家实验室和工业界的科学和工程从业者的基础工具。该项目致力于通过有趣的投资和回报教育计划，帮助科学、技术、工程和数学(STEM)领域的K-12、本科生、女性和代表不足的少数民族(URM)人口，该项目规划了一个全面的路线图，以培养下一代高性能图形分析专业工作者和研究人员。这个项目为PI的国内部门修改和创建了研究生和本科生的核心课程。为了使整个社会受益，该项目在http://personal.stevens.edu/~hliu77/gsrw.html.The向更广泛的研究社区传播项目数据、软件和出版物。本研究的首要目标是使图形采样和随机游走快速、可扩展和用户友好。为此，这份职业提案提倡算法和系统共同设计研究。首先，本研究介绍了快速抽样所必需的各种主要蒙特卡罗方法的转移概率的新的更新和构造设计。其次，为了充分释放GPU的潜力，本项目将关键原语描述为可以利用通用的、保留在GPU上的张量和光线跟踪核心的问题。第三，基于图采样和随机游走的异步处理特性，利用远程直接内存访问(RDMA)辅助的任务和分区自适应调度机制来减少可伸缩万亿边图采样和随机游走的数据传输量。最后但并非最不重要的一点是，这项职业研究提供了一个以偏见为中心的框架，它不仅为最终用户提供了编程各种现有GSRW算法以及未来算法的表现力，还通过隐藏前面提到的高级优化技术来实现简单性。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。