Ultracold quantum matter

Abstract:

Many software and hardware applications generate an increasing volume of data and logs in real-time. Visual analytics is essential to support system monitoring and analysis of such data. For example, the world's largest radio telescope, the Square Kilometer Array (SKA), is expected to generate an estimated 160 TB a second of raw data captured from different sources. Transporting large amounts of data from distributed sources to a web browser for visualization is time-consuming due to data transport latencies. In addition, visualizing real-time data in the browser is challenging and limited by the data rates which a web browser can handle. We propose a novel low latency data streaming architecture, which uses a messaging system for real-time data transport to the web browser. Based on this architecture, we propose techniques and provide a tool for analyzing the performance of serialization protocols and the web-visualization rendering pipeline. We empirically evaluate the performance of our architecture using three visualizations use cases relevant to the SKA. Our system proved extremely useful in streaming high-volume data in real-time with low latency and greatly enhanced the web-visualization performance by enabling streaming an optimal number of data points to different visualizations.

Abstract:

Computational modeling is a commonly used technology in many scientific disciplines and has played a noticeable role in combating the COVID-19 pandemic. Modeling scientists conduct sensitivity analysis frequently to observe and monitor the behavior of a model during its development and deployment. The traditional algorithmic ranking of sensitivity of different parameters usually does not provide modeling scientists with sufficient information to understand the interactions between different parameters and model outputs, while modeling scientists need to observe a large number of model runs in order to gain actionable information for parameter optimization. To address the above challenge, we developed and compared two visual analytics approaches, namely: algorithm-centric and visualization-assisted, and visualization-centric and algorithm-assisted. We evaluated the two approaches based on a structured analysis of different tasks in visual sensitivity analysis as well as the feedback of domain experts. While the work was carried out in the context of epidemiological modeling, the two approaches developed in this work are directly applicable to a variety of modeling processes featuring time series outputs, and can be extended to work with models with other types of outputs.