Task-based Visualization Methods for Scalable Analysis of Large Data Sets

用于大数据集可扩展分析的基于任务的可视化方法

• 批准号: 398122172
• 负责人: Professor Dr. Christoph Garth
• 金额: --
• 依托单位: Lehr- und Forschungsgebiet Virtuelle Realität und Immersive Visualisierung
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/398122172?language=en
• 关键词: Task; based; Visualization; Methods; Scalable

In addition to theory and experiment, simulation of technical and natural phenomena has become the third pillar of modern science and engineering. The analysis of resulting simulation data sets using scientific visualization techniques is an essential component of this approach. As a platform for simulation, massively-parallel high-performance computers are employed with a steadily increasing number of execution units (cores). As the resulting amount of data is growing proportionally to available computing power, the development of scalable, parallel visualization techniques is of ever increasing importance. A similar development can be observed in commodity hardware (PCs); thus corresponding schemes will also be needed on these architectures in the medium to long term future. Research into parallelization of visualization algorithms has thus far focused mostly on individual approaches. For several individual techniques, good results regarding scalability and efficiency have been shown. In contrast, real-world applications of visualization often require a combination of methods. Statements about the efficiency of such a combination, especially where different parallelization paradigms are concerned, are currently not possible. This is a significant hindrance towards the choice of suitable algorithms, especially as an unsuitable choice may result in significant or even prohibitive inefficiencies.In recent years, task-based parallelization has been established as useful across a wide range of applications. Here, an algorithm is formulated as a set of tasks, each of which represents an atomic step of the computation. Dependencies between tasks are modeled explicitly. Through this, as long as dependencies are not violated, a mostly arbitrary and concurrent sequence of execution of the tasks can be chosen in order to optimize the computation.The proposed project aims at the investigation of task-based formulations of established visualization techniques. The overarching goal is to increase applicability and utility of visualization for large data sets on contemporary and future architectures. In this regard, the general suitability of different classes of visualization algorithms towards a task-based formulation will be considered, as well as the resulting efficiency and runtime behavior. In particular, the composition of different techniques, as it is often applied in practice, will be examined. Initial work by the applicants indicates that this approach is promising.

除了理论和实验，技术和自然现象的模拟已经成为现代科学和工程的第三大支柱。使用科学可视化技术分析得到的模拟数据集是这种方法的一个重要组成部分。作为仿真平台，采用了具有稳定增加的执行单元（核心）数量的并行高性能计算机。由于所产生的数据量与可用的计算能力成比例地增长，可扩展的并行可视化技术的发展越来越重要。在商品硬件（PC）中也可以观察到类似的发展;因此，在中长期内，这些体系结构也需要相应的方案。到目前为止，可视化算法的并行化研究主要集中在个别的方法。对于几个单独的技术，关于可扩展性和效率的良好结果已经显示。相比之下，现实世界的可视化应用程序往往需要组合的方法。关于这种组合的效率的声明，特别是在涉及不同并行化范例的情况下，目前是不可能的。这对于选择合适的算法是一个很大的障碍，特别是当一个不合适的选择可能会导致显著的甚至是禁止的低效率。近年来，基于任务的并行化已经被建立为在广泛的应用中有用。在这里，算法被公式化为一组任务，每个任务代表计算的一个原子步骤。任务之间的重复性是明确建模的。通过这一点，只要不违反依赖关系，大多是任意和并发的任务执行顺序可以选择，以优化computation.The拟议项目的目的是在调查的任务为基础的制定可视化技术。总体目标是提高当代和未来架构上大型数据集可视化的适用性和实用性。在这方面，将考虑不同类别的可视化算法对基于任务的制定的一般适用性，以及由此产生的效率和运行时行为。特别是，不同的技术，因为它是经常在实践中应用的组成，将进行审查。申请人的初步工作表明，这一方法很有希望。