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

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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.