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Developing Inclusive Practices in Software & Scientific Data Models

data model

For almost four decades at Lawrence Livermore National Laboratory (LLNL), a national security research and development laboratory sponsored by the Department of Energy, Mark C. Miller has built considerable experience in developing, implementing, and augmenting a number of scientific database and data modeling technologies such as SiloVisIt, HDF5, and many others. In fact, he has served as the Silo library’s lead developer since the late 1990s, which has been essential for supporting scalable input/output requirements of LLNL’s HPC simulation codes.

Creating Understanding

In his I AM HPC profile, Miller, who also serves on the SC23 Inclusivity Committee as Vice Chair and Demographics Chair, focuses on the sometimes overlooked value and capabilities of scientific data modeling and its contribution to computing. He also draws a line from cool science fiction to his current role as an engineer at LLNL.

mark miller

Lawrence Livermore National Laboratory

Q: What single event most made you realize you wanted a career in HPC/computing?

Miller: Two movies with early awesome computer graphics, Tron (1982) and The Last Starfighter (1984).

Q: What do you consider your biggest contribution to the HPC/computing community?

Miller: Helping the community understand the impact of scientific data modeling technologies on our ability to share, interoperate, and exchange not only data but, more importantly, software.

Q: In the past 35 years, what is the most significant overlooked breakthrough that has impacted the field in your eyes?

Miller: Data abstraction through formal, math-based, scientific data models. Most abstractions are ad hoc, unnecessarily constrained, and fail to support wide appeal across application domains and/or computing technologies.

Q: What would you like to see change about, within, or among the HPC/computing community?

Miller: To understand that what we are ultimately building is not just hardware or software but the virtual equivalent of scientific instrumentation such as you might see in any real experimental facility (laser, accelerator, reactor, observatory, cleanroom, etc.) and to expect that what we build meets the same standards of rigor, certainty, and quality as our real-world counterparts.

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