CAREER: Comprehensive Techniques and Design for Flexible Graph Visualization of Software and Systems

计算机辅助的药物发现、飓风轨迹预测和新能源模拟都可以通过底层软件（程序）实现。为了构建这些程序，软件开发人员必须将每个问题分成更小的操作，并对这些操作进行仔细排序，以计算出正确的答案。这种排序可以看作是一个网络图，其中每个操作按顺序指向下一个可能的操作。此外，为了使这些程序运行得足够快而有用，开发人员、工程师和研究人员必须找到新的方法来平衡这些操作与系统资源（如存储、处理单元和内存）之间的关系。这些资源约束为图添加了更多的排序需求。验证此图可以帮助避免错误。从此图推断出的信息可用于自动减少程序运行所需的时间。因此，从事计算工作的人经常检查这些图，以生成或验证关于如何编写程序或使用计算资源的新想法。例如，程序员可能会发现一个长序列的操作一个接一个地执行，可以并行化，从而使程序更快。绘制网络的一般方法不能满足计算空间中图形的需求，导致图形难以解释，如果不是不可用的话。该项目的目的是开发新的图形可视化方法，以更好地支持开发人员、工程师和研究人员的工作，以及学习者对计算的理解，并将图形可视化等工具集成到他们的技能集中。从计算环境中产生的图通常采用有向无环或近无环图（类dag图）的形式。这个项目的目的是为这类计算图开发布局算法和设计指南。研究任务包括测量跨计算学科生成的图形，以导出其结构的特征，其特定领域的子结构，以及它们的公共绘图惯例。描述将指导节点链接图布局的约束和目标的开发，以及支持特定领域任务的交互性和可视化编码的设计指南。结果将在一个开源库中实现，并对计算结构的表达能力和计算性能进行评估。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Computationally-assisted drug discovery, predictions of hurricane trajectories, and simulations of new energy sources are all made possible by underlying software (programs). To construct these programs, software developers must divide each problem into smaller operations and order those operations carefully to compute the correct answer. This ordering can be viewed as a network graph, where each operation points to its next possible operations in order. Furthermore, to make these programs run fast enough to be useful, developers, engineers, and researchers must find new ways to balance those operations with system resources such as storage, processing units, and memory. These resource constraints add more ordering requirements to the graph. Verifying this graph can help avoid bugs. Information inferred from this graph can be used to automatically decrease the time the program requires to run. Thus, people working in computing frequently examine these graphs to generate or validate new ideas about how to write their programs or use computational resources. For example, a programmer may discover a long sequence of operations performed one after another that could be parallelized, thus making the program faster. General approaches for drawing networks have not met the demand of graphs in the computing space, resulting in difficult to interpret, if not unusable, diagrams. The purpose of this project is to develop new graph visualization approaches to better support developers, engineers, and researchers in their work and learners in their understanding of computing and their integration of tools such as graph visualization into their skill sets.Graphs arising from computing contexts frequently take the form of directed acyclic or near-acyclic graphs (DAG-like graphs). The aim of this project is to develop layout algorithms and design guidelines specifically for this class of computing graphs. Research tasks include surveying graphs generated across computing disciplines to derive a characterization of their structure, their domain-specific sub-structures, and their common drawing conventions. The characterization will guide the development of constraints and objectives for layout of node-link diagrams as well as design guidelines for interactivity and visual encoding in support of domain-specific tasks. The results will be implemented in an open-source library and evaluated for both expressivity of computing structures and computational performance.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.