RII Track-4: NSF: Massively Parallel Graph Processing on Next-Generation Multi-GPU Supercomputers

RII Track-4：NSF：下一代多 GPU 超级计算机上的大规模并行图形处理

• 批准号: 2229394
• 负责人: Da Yan
• 金额: $ 27.56万
• 依托单位: University of Alabama at Birmingham
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229394&HistoricalAwards=false
• 关键词: RII; Track; NSF; Massively; Parallel; RII; Track; NSF; Massively; Parallel

Graph processing is essential in real-world applications such as bioinformatics and social network analysis. Many fundamental graph operations are compute-intensive, for which the PI has successfully developed a series of CPU-scalable graph processing systems following a novel task-based parallel paradigm called T-thinker. However, it is non-trivial to extend this success to a GPU-rich environment due to a much larger gap between IO bandwidth and computing power of GPUs, and due to the unique programming requirements for GPU programs to be scalable. This project will develop a new task-based distributed GPU framework, T-thinkerGPU, and implement three applications on top, including subgraph matching, dense subgraph mining, and frequent subgraph pattern mining. T-thinkerGPU will be tested on the Aurora supercomputer at Argonne National Laboratory (ANL) as well as UAB’s Cheaha supercomputer, and the implementation will exploit modern GPU features including atomic operations, unified shared memory, and dynamic parallelism. This work will establish a solid foundation for long-term collaboration with ANL towards the development of GPU-scalable HPC solutions for various scientific applications. The project will also train a GPU-programming workforce (including a PhD student who will also visit ANL) that is in urgent need in Alabama, and all the proposed tools will be open source.This Research Infrastructure Improvement Track-4 EPSCoR Research Fellows (RII Track-4) proposal would provide a fellowship to an Assistant professor and training for a graduate student at the University of Alabama at Birmingham (UAB). GPU supercomputers are increasingly being deployed in place of CPU supercomputers in the hope to benefit from not only significant performance improvement but also energy efficiency. Built on the success of task-based parallel paradigm, T-thinker, for scaling graph processing in a multi-CPU environment, this project aims to investigate novel task-based techniques to scale fundamental compute-intensive graph operations in a multi-GPU environment, especially the exascale Aurora supercomputer at ANL that is based on Intel GPUs. Specifically, the project will first investigate efficient representation schemes that encode and compress the input graph and intermediate subgraph results compactly to reduce memory footprint and enable coalesced memory access and data reuse in shared memory, such as hashed neighborhood signature and lossless pattern-based contraction. Secondly, the project will design GPU-friendly task-based algorithms for fundamental graph operations including subgraph matching, dense subgraph mining, and frequent subgraph pattern mining, to unleash the massive parallelism enabled by a multi-GPU environment like the Aurora supercomputer. Novel techniques will be investigated such as kernel-as-a-task execution model, a truly hybrid BFS-DFS task scheduling strategy, and several other GPU optimization approaches, which will be combined into a unified programming framework, T-thinkerGPU, with extendibility in mind to facilitate the development of GPU-scalable task-based algorithms for other graph operations in the future. Finally, the developed GPU programs will be extensively evaluated on Aurora (with Intel GPUs) and UAB’s Cheaha supercomputer (with Nvidia GPUs), using public benchmarks and scientific applications at ANL and UAB, and the code will be released on GitHub.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.

图在现实世界中必不可少的应用程序，例如生物信息学和社交网络分析。许多基本的图形操作都是计算密集型，为此，PI成功地开发了一系列CPU尺度的图形处理系统，遵循一种新型的基于任务的平行范式，称为T-Theminker。但是，由于IO带宽和GPU的计算能力之间的差距更大，并且由于GPU程序具有可扩展的独特编程要求，因此将这一成功扩展到GPU充足的环境是不足的。该项目将开发一个新的基于任务的分布式GPU框架T-ThinkergPU，并在顶部实现三个应用程序，包括子图匹配，密集的子图挖掘和经常的子图模式挖掘。 T-ThinkergPU将在Argonne National Laboratory（ANL）的Aurora超级计算机以及UAB的Cheaha SuperComputer上进行测试，并且该实施将利用现代GPU功能，包括原子操作，统一共享内存和动态平行性。这项工作将为与ANL的长期合作建立坚实的基础，以开发为各种科学应用的GPU-Scalable HPC解决方案。 The project will also train a GPU-programming workforce (including a PhD student who will also visit ANL) that is in urgent need in Alabama, and all the proposed tools will be open source.This Research Infrastructure Improvement Track-4 EPSCoR Research Fellows (RII Track-4) proposal would provide a fellowship to an Assistant Professor and training for a graduate student at the University of Alabama at Birmingham (UAB). GPU超级计算机越来越多地由CPU超级计算机部署，希望不仅可以从大幅提高性能，而且可以从能源效率中受益。该项目旨在研究基于任务的并行范式T-TheNINGER，用于在多CPU环境中扩展图形处理，该项目旨在调查基于任务的新技术，以在多GPU环境中缩放基本的计算密集型图形操作，尤其是Exascale Aurora Aurora SuperComputer，尤其是基于Intel GPU的ANL。具体而言，该项目将首先调查有效的表示方案，该方案编码和压缩输入图和中间子图结果，以减少内存足迹，并在共享内存中启用合并的内存访问和数据再利用，例如Hashed邻里签名和无损模式的基于模式的合同。其次，该项目将设计基于GPU的基于任务的算法，用于基本图形操作，包括子图匹配，密集的子图挖掘和经常的子图模式挖掘，以释放由Aurora SuperCutiter等多GPU环境启用的大规模平行性。新颖的技术将进行研究，例如内核-AS-A任务执行模型，真正的混合BFS-DFS任务调度策略以及其他几种GPU优化方法，这些方法将组合为统一的编程框架，T-ThinkergPU，并在基于GPU-Scalable Algsable Algorith的开发方面要有更大的困难，以实现更大的困难，以实现GPU-Scalable Algers的开发。最后，将使用ANL和UAB上的公共基准和科学应用，将对开发的GPU计划进行广泛评估，并使用UAB的Cheaha超级计算机（与NVIDIA GPU一起）进行广泛评估，该代码将在GitHub上发布，以评估NSF的Intortair Industriun Induction of Fielding of Fielder，该奖项将在ANL和UAB上进行公共基准和科学应用。 标准。