Keywords
Abstract
Sparse matrix computation is a foundational technique for scalable scientific simulation, graph analytics, and large-scale risk modeling. IA-SpGEMM provides an input-aware auto-tuning framework for parallel sparse matrix-matrix multiplication, which is relevant to applications involving graph propagation, agent-based interaction networks, spatial risk models, and simulation acceleration. This topic connects sparse computation with flood disaster relocation modeling, where household agents, spatial exposure, and policy interactions can form large computational structures. Efficient sparse operations support scalable analysis of relocation scenarios, network effects, and policy experiments. The literature direction emphasizes the computational infrastructure needed to connect scientific simulation with decision-support systems in disaster risk and climate adaptation contexts.
References
Xie, Z., Tan, G., Liu, W., & Sun, N. (2019, June). IA-SpGEMM: An input-aware auto-tuning framework for parallel sparse matrix-matrix multiplication. In Proceedings of the ACM International Conference on Supercomputing (pp. 94–105).
Zhou, Y. (2022). Pre-disaster relocation and agent-based model for flood disaster [Doctoral dissertation, University of Wisconsin–Madison].
Davis, T. A. (2006). Direct Methods for Sparse Linear Systems. SIAM.
Saad, Y. (2003). Iterative Methods for Sparse Linear Systems. SIAM.
Tesfatsion, L. (2006). Agent-based computational economics. Handbook of Computational Economics, 2, 831–880.
Grimm, V., Revilla, E., Berger, U., et al. (2005). Pattern-oriented modeling of agent-based complex systems. Science, 310(5750), 987–991.
Hino, M., Field, C. B., & Mach, K. J. (2017). Managed retreat as a response to natural hazard risk. Nature Climate Change, 7, 364–370.
Meyer, V., Becker, N., Markantonis, V., et al. (2013). Assessing the costs of natural hazards. Natural Hazards and Earth System Sciences, 13, 1351–1373.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Kairouz, P., McMahan, H. B., Avent, B., et al. (2021). Advances and open problems in federated learning. Foundations and Trends in Machine Learning, 14(1–2), 1–210.