Phil Carns

Phil Carns is a computer scientist at Argonne National Laboratory's Mathematics and Computer Science Division and an Adjunct Associate Professor in the Department of Electrical and Computer Engineering at Clemson University. He is also a fellow of the Northwestern-Argonne Institute for Science and Engineering and serves as deputy director of the Software Tools Ecosystem Project.

Education:

• Ph.D. in Computer Engineering, Clemson University, 2005

His research focuses on high-performance computing (HPC), particularly in the areas of HPC storage architectures, system software, and I/O workload analysis. He has pioneered advancements in scalable data storage and management through projects such as Mochi (composable data services), Darshan (I/O characterization), PVFS (parallel file system), and CODES (storage simulation). His work supports exascale computing initiatives and scientific data infrastructure.

The 15 most recent publications reflect a consistent focus on HPC storage, I/O analysis, and system software design. Broad keywords include Computer Science, High-Performance Computing, Distributed Systems, and Simulation. These works explore subfields such as composable data services, I/O profiling, parallel file systems, performance monitoring, and exascale data libraries, demonstrating a trajectory toward modular, scalable, and efficient data management solutions for scientific computing.

Scientific Awards:

• R&D 100 Award
• R&D 100 Award
• R&D 100 Award

Phil Carns has served as principal investigator, technical lead, and developer on numerous federally funded HPC projects, including the Exascale Computing Project. While no formal students are listed, his leadership roles imply mentorship and collaboration within research teams. He is actively involved in advancing software tools and data ecosystems for large-scale scientific computing.

He leads and contributes to several key research initiatives, including the Mochi Project, Darshan Project, and the Software Tools Ecosystem Project (STEP), all aimed at improving data services, monitoring, and software sustainability in HPC environments.