Cody Balos is a computational scientist and research software engineer in the Center for Applied Scientific Computing (CASC). He joined the lab in 2018. Cody's work focuses on the research and development of scalable algorithms and software for high-performance scientific computing. He is core developer and team member of the SUNDIALS project as well as the Extreme-scale Scientific Software Development Kit (xSDK). He is also member of the FASTMath SciDAC-5 Institute and NUCLEI SciDAC-5 partnership project. Cody has experience with a wide variety of scientific applications including ones from combustion, nuclear physics and fusion energy sciences.

Before joining LLNL, Cody worked on enterprise web applications for a healthcare company. He holds a B.S. in Computer Engineering from the University of the Pacific as well as a M.S. in Computational and Applied Mathematics from the University of Washington.

Research Interests

• Scientific software engineering
• Numerical simulation at extreme scales / on next-generation hardware
• Numerical methods for solving ordinary 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.