Julian Bellavita

Computer Science PhD Student at Cornell

I am a Computer Science PhD student at Cornell advised by Dr. Giulia Guidi. I have a BA in CS from UC Berkeley, and I previously worked at Lawrence Berkeley National Laboratory.

I work on implementing fast distributed sparse matrix multiplication algorithms on heterogeneous HPC systems, and on using sparse matrix multiplication to parallelize applications in computational biology and elsewhere.

I am supported by a Department of Energy Computational Science Graduate Fellowship.

You can email me at jbellavita@cs.cornell.edu

Bellavita_Julian_CSGF_[2024] (5) (1).JPG

Publications

Bellavita, J., Jacquelin, M., Ng, E. G., Bonachea, D., Corbino, J., & Hargrove, P. H. (2023, November). symPACK: A GPU-Capable Fan-Out Sparse Cholesky Solver. In Proceedings of the SC'23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis (pp. 1171-1184).

Castelló, A., Bellavita, J., Dinh, G., Ikarashi, Y., & Martínez, H. (2023). Tackling the Matrix Multiplication Micro-kernel Generation with Exo. IEEE/ACM International Symposium on Code Generation and Optimization (CGO) 2024.

J. Bellavita, C. Sim, K. Wu, A. Sim, S. Yoo, H. Ito, V. Garonne, E. Lancon, "Understanding Data Access Patterns for dCache System", 26th International Conference on Computing in High Energy & Nuclear Physics (CHEP2023), 2023.

Bellavita, J., Sim, A., Wu, K., Monga, I., Guok, C., Würthwein, F., & Davila, D. (2022, June). Studying Scientific Data Lifecycle in On-demand Distributed Storage Caches. In Fifth International Workshop on Systems and Network Telemetry and Analytics (pp. 43-50).

Education

Cornell University

PhD, Computer Science

2023-Present

University of California, Berkeley

B.A. Computer Science

2019-2023