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Macduff, Matt C.

Publications and source records attributed to Macduff, Matt C..

Evaluation of Use Cases and Types of Databases for Hosting Wind Power Data

The goal of the National Wind Power Production Data Dashboard project is to develop a publicly available platform to model, process, and share wind power with uncertainty quantification for the current and future onshore and offshore wind plants across the continental United States. As of 2021, more than 70,000 utility-scale wind turbines have been installed across the United States and this number is expected to grow in the next few years. A large-scale wind power production database is needed for stakeholders (policy makers, operators, and researchers) to access easily for their decision-making. Currently, meeting that need is a challenge. Most wind power operators and system operators focus on regions of their own interest, and the data are usually inaccessible to the public.

17 WIND ENERGY↗

Data Provenance Hybridization Supporting Extreme-Scale Scientific WorkflowApplications

As high performance computing (HPC) infrastructures continue to grow in capability and complexity, so do the applications that they serve. HPC and distributed-area computing (DAC) (e.g. grid and cloud) users are looking increasingly toward workflow solutions to orchestrate their complex application coupling, pre- and post-processing needs To gain insight and a more quantitative understanding of a workflow’s performance our method includes not only the capture of traditional provenance information, but also the capture and integration of system environment metrics helping to give context and explanation for a workflow’s execution. In this paper, we describe IPPD’s provenance management solution (ProvEn) and its hybrid data store combining both of these data provenance perspectives.

provenance↗

Towards an Application-Specific Thermal Energy Model of Current Processors

Recent developments of high-end processors recognize temperature monitoring and tuning as one of the main challenges towards achieving higher performance given the growing power and temperature constraints. To address this challenge, one needs both suitable thermal energy abstraction and corresponding instrumentation. Our model is based on application-specific parameters such as power consumption, execution time, and asymptotic temperature as well as hardware-specific parameters such as half time for thermal rise or fall. As observed with our out-of-band instrumentation and monitoring infrastructure, the temperature changes follow a relatively slow capacitor-style charge-discharge process. Therefore, we use the lumped thermal model that initiates an exponential process whenever there is a change in processor’s power consumption. Initial experiments with two codes – Firestarter and Nekbone – validate our application-specific thermal model and demonstrate its use for analyzing and potentially improving the balance between temperature, power, and performance.

Getov, Vladimir S.↗

Data Archive and Portal Thrust Area Strategy Report

This report describes the Data Archive and Portal (DAP), a key capability of the U.S. Department of Energy's Atmosphere to Electron (A2e) initiative. The DAP Thrust Area Planning Group was organized to develop a plan for deploying this capability. Primarily, the report focuses on a distributed system--a DOE Wind Cloud--that functions as a repository for all A2e data. The Wind Cloud will be accessible via an open, easy-to-navigate user interface that facilitates community data access, interaction, and collaboration. DAP management will work with the community, industry, and international standards bodies to develop standards for wind data and to capture important characteristics of all data in the Wind Cloud.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Versioning Complex Data

Using the history of ARM data files, we designed and demonstrated a data versioning paradigm that is feasible. Assigning versions to sets of files that are modified with some special assumptions and domain specific rules was effective in the case of ARM data, which has more than 5000 datastreams and 500TB of data.

ARM↗