arXiv subjects

Kristin Munch

Publications and source records attributed to Kristin Munch.

3 recordsLinked to original sources

arXiv2024

Integrating Energy-Efficient Computing Research to Accelerate Energy Technology

NREL's computational sciences center hosts the largest high-performance computing (HPC) capabilities dedicated to energy research while functioning as a living laboratory for energy-efficient computing. NREL's HPC capabilities support the research needs of the Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE). In ten years of operation, HPC use in EERE-sponsored research has grown by a factor of 30, including work in electricity generation, energy efficiency, transportation, and energy system modeling. This paper analyzes this research portfolio, providing examples of individual use cases. The paper documents NREL's history of operating one of the world's most energy-efficient data centers while examining pathways to reduce economic and environmental impact beyond reduction of Power Usage Efficiency (PUE). This paper concludes by examining the unique opportunities created for accelerating improvements in data center efficiency created by combining an HPC system dedicated to energy research and a research program in energy-efficient computing.

cs.CY↗

arXiv2021

Research Data Infrastructure for High-Throughput Experimental Materials Science

The High-Throughput Experimental Materials Database (HTEM-DB) is the endpoint repository for inorganic thin-film materials data collected during combinatorial experiments at the National Renewable Energy Laboratory (NREL). This unique data asset is enabled by the Research Data Infrastructure (RDI) - a set of custom data tools that collect, process, and store experimental data and metadata. Here, we describe the experimental data-tool workflow from the RDI to the HTEM-DB to illustrate the strategies and best practices currently used for materials data at NREL. Integration of these data tools with the experimental processes establishes a data communication pipeline between experimental and data science communities. In doing so, this work motivates the creation of similar data workflows at other institutions to aggregate valuable data and increase its usefulness for future data studies. These types of investments can greatly accelerate the pace of learning and discovery in the materials science field, by making data accessible to new and rapidly evolving data methods.

cond-mat.mtrl-sci↗

arXiv2014

Handling Large and Complex Data in a Photovoltaic Research Institution Using a Custom Laboratory Information Management System

Twenty-five years ago the desktop computer started becoming ubiquitous in the scientific lab. Researchers were delighted with its ability to both control instrumentation and acquire data on a single system, but they were not completely satisfied. There were often gaps in knowledge that they thought might be gained if they just had more data and they could get the data faster. Computer technology has evolved in keeping with Moore's Law meeting those desires; however those improvement have of late become both a boon and bane for researchers. Computers are now capable of producing high speed data streams containing terabytes of information; capabilities that evolved faster than envisioned last century. Software to handle large scientific data sets has not kept up. How much information might be lost through accidental mismanagement or how many discoveries are missed through data overload are now vital questions. An important new task in most scientific disciplines involves developing methods to address those issues and to create software that can handle large data sets with an eye towards scalability. This software must create archived, indexed, and searchable data from heterogeneous instrumentation for the implementation of a strong data-driven materials development strategy. At the National Center for Photovoltaics in the National Renewable Energy Lab, we began development a few years ago on a Laboratory Information Management System (LIMS) designed to handle lab-wide scientific data acquisition, management, processing, and mining needs for physics and materials science data. and with a specific focus on future scalability for new equipment or research focuses. We will present the decisions, process, and problems we went through while building our LIMS for materials research, its current operational state, and our steps for future development.

cs.DL↗