Data-driven problem solving in many real-world applications involves analysis of time-
dependent multivariate data, for which dimensionality reduction (DR) methods are often …

Understanding the behavior of software in execution is a key step in identifying and fixing
performance issues. This is especially important in high performance computing contexts …

With the rapid accumulation of text data produced by data-driven techniques, the task of
extracting" data annotations"—concise, high-quality data summaries from unstructured raw …

Currently, growing data sources and long-running algorithms impede user attention and
interaction with visual analytics applications. Progressive visualization (PV) and visual …

Distributed query processing systems such as Apache Hive and Spark are widely-used in
many organizations for large-scale data analytics. Analyzing and understanding the query …

Many real-world applications involve analyzing time-dependent phenomena, which are
intrinsically functional, consisting of curves varying over a continuum (eg, time). When …

The ability to monitor and interpret hardware system events and behaviors is crucial to
improving the robustness and reliability of these systems, especially in a supercomputing …

A common network analysis task is comparison of two networks to identify unique
characteristics in one network with respect to the other. For example, when comparing …

Modern parallel/distributed simulations can produce large amounts of data. The historical
approach of performing analyses at the end of the simulation is unlikely to cope with …

随着科技飞速发展, 超级计算机系统不断向着更大规模迈进, 给系统使用和维护带来困难.
可视化技术以直观易懂的方式展示超级计算机系统的运行状态, 异常情况等 …