Cody Balos is a computational scientist and research software engineer (RSE) in the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory. He joined the lab in 2018 after graduating with a B.S. in Computer Engineering from the University of the Pacific. He also holds a M.S. in Computational and Applied Mathematics from the University of Washington.

Cody's work focuses on the research and development of scalable numerical algorithms, software, and DevOps solutions for high-performance scientific computing. He is a core developer of the SUNDIALS time integration and nonlinear solver library. He is a member of several projects funded by the Department of Energy’s Scientific Discovery through Advanced Computing program, including the FASTMath Institute, CETOP fusion energy partnership project, and NUCLEI nuclear physics partnership project. Cody has experience working with a wide variety of scientific applications including ones from combustion, nuclear physics, and fusion energy sciences.  

Research Interests

• Scientific software engineering
• Numerical simulation at extreme scales / on next-generation hardware
• Numerical methods for solving differential equations
• Scientific machine learning

M.S. Computational and Applied Mathematics, University of Washington, Seattle, Washington

B.S. Computer Engineering, University of the Pacific, Stockton, California

Selected Journal and Conference Papers

See my Google Scholar page for a full list of publications.

2023

• Balos, C. J., Roberts, S., & Gardner, D. J. (2023). Leveraging Mixed Precision in Exponential Time Integration Methods. 2023 IEEE High Performance Extreme Computing Conference (HPEC). Outstanding Paper Award.

2022

• Balos, C. J., Luszczek, P., Osborn, S., Willenbring, J., & Yang, U. M. (2022). Challenges of and Opportunities for a Large Diverse Software Team. Computing in Science & Engineering. 10.1109/MCSE.2022.3172873. 

2021

• Aggarwal, I., Kashi, A., Nayak, P., Balos, C. J., Woodward, C. S., & Anzt, H. (2021, November). Batched Sparse Iterative Solvers for Computational Chemistry Simulations on GPUs. In 2021 12th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA) (pp. 35-43). IEEE. 10.1109/ScalA54577.2021.00010.

• Balos, C. J., Gardner, D. J., Woodward, C. S., & Reynolds, D. R. (2021). Enabling GPU accelerated computing in the SUNDIALS time integration library. Parallel Computing, 102836. https://doi.org/10.1016/j.parco.2021.102836. 

2020

• D.S. Khan, C.J. Balos, et al. A2Cloud‐RF: A random forest based statistical framework to guide resource selection for high‐performance scientific computing on the cloud. Concurrency Computat Pract Exper. 2020; 32:e5942. https://doi.org/10.1002/cpe.5942

2018

• C. Balos, D. De La Vega, Z. Abuelhaj, C. Kari, D. Mueller and V. K. Pallipuram, "A2Cloud: An Analytical Model for Application-to-Cloud Matching to Empower Scientific Computing," 2018 IEEE 11th International Conference on Cloud Computing (CLOUD), San Francisco, CA, USA, 2018, pp. 548-555.

 

• 2023 IEEE HPEC Outstanding Paper Award for Leveraging Mixed Precision in Exponential Time Integration Methods.
• 2023 SIAM/ACM Prize in Computational Science and Engineering. Awarded to the core developers of SUNDIALS.