Browsing by Author "Kress, James"

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    Improving Performance of M-to-N Processing and Data Redistribution in In Transit Analysis and Visualization
    (The Eurographics Association, 2020) Loring, Burlen; Wolf, Mathew; Kress, James; Shudler, Sergei; Gu, Junmin; Rizzi, Silvio; Logan, Jeremy; Ferrier, Nicola; Bethel, E. Wes; Frey, Steffen and Huang, Jian and Sadlo, Filip
    In an in transit setting, a parallel data producer, such as a numerical simulation, runs on one set of ranks M, while a data consumer, such as a parallel visualization application, runs on a different set of ranks N: One of the central challenges in this in transit setting is to determine the mapping of data from the set of M producer ranks to the set of N consumer ranks. This is a challenging problem for several reasons, such as the producer and consumer codes potentially having different scaling characteristics and different data models. The resulting mapping from M to N ranks can have a significant impact on aggregate application performance. In this work, we present an approach for performing this M-to-N mapping in a way that has broad applicability across a diversity of data producer and consumer applications. We evaluate its design and performance with a study that runs at high concurrency on a modern HPC platform. By leveraging design characteristics, which facilitate an ''intelligent'' mapping from M-to-N, we observe significant performance gains are possible in terms of several different metrics, including time-to-solution and amount of data moved.
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    Visualization Environment for Analyzing Extreme Rainfall Events: A Case Study
    (The Eurographics Association, 2023) Kress, James; Afzal, Shehzad; Dasari, Hari Prasad; Ghani, Sohaib; Zamreeq, Arjan; Ghulam, Ayman; Hoteit, Ibrahim; Dutta, Soumya; Feige, Kathrin; Rink, Karsten; Zeckzer, Dirk
    Extreme rainfall events can devastate infrastructure and public life and potentially induce substantial financial and life losses. Although weather alert systems generate early rainfall warnings, predicting the impact areas, duration, magnitude, occurrence, and characterization as an extreme event is challenging. Scientists analyze previous extreme rainfall events to examine the factors such as meteorological conditions, large-scale features, relationships and interactions between large-scale features and mesoscale features, and the success of simulation models in capturing these conditions at different resolutions and their parameterizations. In addition, they may also be interested in understanding the sources of anomalous amounts of moisture that may fuel such events. Many factors play a role in the development of these events, which vary depending on the locations. In this work, we implement a visualization environment that supports domain scientists in analyzing simulation model outputs configured to predict and analyze extreme precipitation events. This environment enables visualization of important local features and facilitates understanding the mechanisms contributing to such events. We present a case study of the Jeddah extreme precipitation event on November 24, 2022, which caused great flooding and infrastructure damage. We also present a detailed discussion about the study's results, feedback from the domain experts, and future extensions.