Collaborative Research: SHF: Medium: Co-optimizing Spectral Algorithms and Systems for High-Performance Graph Learning

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Emerging graph learning techniques have shown promising results for various important applications such as community detection, drug discovery, and electronic design automation (EDA). However, even the state-of-the-art graph learning methods cannot scale to large data sets due to their high algorithm complexity. For example, the latest graph neural network (GNN) algorithms collectively aggregate feature information from the neighborhood of each node, which not only drastically increases the number of computations among nodes but also leads to high memory usage for storing the intermediate results. Hence most graph learning algorithms cannot efficiently handle large-scale problems due to their high computation and storage costs, not to mention the real-world graphs that may involve billions of edges.This project aims at addressing the most pressing challenges in modern graph learning tasks by investigating high-performance spectral graph algorithms and systems based on the latest theoretical breakthroughs. Unlike prior theoretical studies on spectral graph theory that put less focus on practical algorithm implementations and applications, the investigators of this project will develop practically-efficient spectral graph compression algorithms to boost the efficiency and solution quality of existing graph learning methods through algorithm and system co-optimizations by taking advantage of the latest heterogeneous computing devices, such as GPUs, FPGAs, and computational storage devices. The outcome of this project will potentially advance the state of the art in spectral graph theory, machine learning, data analytics, EDA, as well as high-performance computing. This project is also likely to spark new research in other related computer science and engineering fields such as complex system/network modeling, computational biology, precision medicine, and transportation networks. The investigators will partner with the STEM ambassador program at Stevens, and the Diversity Programs in Engineering office at Cornell to recruit highly diversified undergraduate and graduate students to participate in this project, while the latest research results will be integrated into several graduate and upper-division undergraduate courses to prepare a new generation of researchers and practitioners in high-performance machine learning.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.