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The deal.II library, Version 9.2

Journal of Numerical Mathematics

Arndt, Daniel; Bangerth, Wolfgang; Blais, Bruno; Clevenger, Thomas C.; Fehling, Marc; Heister, Timo; Heltai, Luca; Maier, Matthias; Munch, Peter; Pelteret, Jean P.; Rastak, Reza; Tomas, Ignacio; Turcksin, Bruno; Wang, Zhuoran; Wells, David

This paper provides an overview of the new features of the finite element library deal.II, version 9.2.

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TYPE Conference Poster YEAR 2020
OSTIScopusOSTIScopus

LDMS-GPU: Lightweight Distributed Metric Service (LDMS) for NVIDIA GPGPUs

Elwazir, Ammar; Badawy, Abdel-Hameed A.; Aaziz, Omar R.; Cook, Jeanine

GPUs are now a fundamental accelerator for many high-performance computing applications. They are viewed by many as a technology facilitator for the surge in fields like machine learning and Convolutional Neural Networks. To deliver the best performance on a GPU, we need to create monitoring tools to ensure that we optimize the code to get the most performance and efficiency out of a GPU. Since NVIDIA GPUs are currently the most commonly implemented in HPC applications and systems, NVIDIA tools are the solution for performance monitoring. The Light-Weight Distributed Metric System (LDMS) at Sandia is an infrastructure widely adopted for large-scale systems and application monitoring. Sandia has developed CPU application monitoring capability within LDMS. Therefore, we chose to develop a GPU monitoring capability within the same framework. In this report, we discuss the current limitations in the NVIDIA monitoring tools, how we overcame such limitations, and present an overview of the tool we built to monitor GPU performance in LDMS and its capabilities. Also, we discuss our current validation results. Most of the performance counter results are the same in both vendor tools and our tool when using LDMS to collect these results. Furthermore, our tool provides these statistics during the entire runtime of the tool as a time series and not just aggregate statistics at the end of the application run. This allows the user to see the progress of the behavior of the applications during their lifetime.

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TYPE SAND Report YEAR 2020
DOIOSTIDOIOSTI

On mixed-integer programming formulations for the unit commitment problem

INFORMS Journal on Computing

Knueven, Bernard; Ostrowski, James; Watson, Jean-Paul

We provide a comprehensive overview of mixed-integer programming formulations for the unit commitment (UC) problem. UC formulations have been an especially active area of research over the past 12 years due to their practical importance in power grid operations, and this paper serves as a capstone for this line of work. We additionally provide publicly available reference implementations of all formulations examined. We computationally test existing and novel UC formulations on a suite of instances drawn from both academic and real-world data sources. Driven by our computational experience from this and previous work, we contribute some additional formulations for both generator production upper bounds and piecewise linear production costs. By composing new UC formulations using existing components found in the literature and new components introduced in this paper, we demonstrate that performance can be significantly improved—and in the process, we identify a new state-of-the-art UC formulation.

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TYPE Journal Article YEAR 2020
DOIOSTIScopus
Results 1226–1250 of 9,998
Results 1226–1250 of 9,998
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