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

职业：软件和系统灵活图形可视化的综合技术和设计

• 批准号: 1844573
• 负责人: Katherine Isaacs
• 金额: $ 52.79万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1844573&HistoricalAwards=false
• 关键词: CAREER; Comprehensive; Techniques; Design; Flexible

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.

通过基础软件（程序），通过计算辅助的药物发现，飓风轨迹的预测以及新能源的模拟都成为可能。要构建这些程序，软件开发人员必须将每个问题分为较小的操作，并仔细订购这些操作以计算正确的答案。该订购可以视为网络图，每个操作都指向其下一个可能的操作。此外，为了使这些程序运行得足够快，可以有用，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，例如存储，处理单元和内存。这些资源约束为图表增加了更多的订购要求。验证此图可以帮助避免错误。从该图中推断出的信息可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表，以生成或验证有关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系列长时间执行的操作序列可以并行，从而使程序更快。绘图网络的一般方法尚未满足计算空间中图的需求，从而难以解释（如果不是不可用的）图。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以及将图形可视化等工具的整合到其技能集中。经常采用定向的无环或近酰基图（DAG样图）的形式。该项目的目的是针对此类的计算图开发布局算法和设计指南。研究任务包括跨计算学科生成的测量图，以得出其结构的表征，特定于领域的子结构及其共同的绘图惯例。该表征将指导开发节点链接图的布局的约束和目标，以及设计指南，以支持域特异性任务的交互性和视觉编码。结果将在开源库中实施，并评估计算结构和计算绩效的表达性。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估的评估来获得支持的。

项目成果

Guidelines For Pursuing and Revealing Data Abstractions

追求和揭示数据抽象的指南

• DOI: 10.1109/tvcg.2020.3030355
• 发表时间: 2021
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Bigelow, Alex;Williams, Katy;Isaacs, Katherine E.
• 通讯作者: Isaacs, Katherine E.

CcNav: Understanding Compiler Optimizations in Binary Code

CcNav：了解二进制代码中的编译器优化

• DOI: 10.1109/tvcg.2020.3030357
• 发表时间: 2021
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Devkota, Sabin;Aschwanden, Pascal;Kunen, Adam;Legendre, Matthew;Isaacs, Katherine E.
• 通讯作者: Isaacs, Katherine E.