VISUALIZATION: Efficient Out-of-Core Isosurface Extraction from Large Datasets

等值面提取和绘制是一种非常有用和流行的体数据挖掘方法，尽管人们对等值面提取技术进行了大量的研究，但很少有人考虑如何在并行计算的同时有效地管理内存访问，从超大规模数据集上提取等值面。对于核心外数据集（即，这些数据太大，不能完全在主存中处理），如果要实现高性能，就必须最大限度地减少访问辅助存储器的延迟。这项工作旨在利用多种类型的并行性，有效地组织内存访问，以尽量减少访问惩罚，并有效地管理进程间通信。 这项工作的一个特点是，它侧重于整个系统的性能，而不是试图只最大限度地提高性能的一个方面的中介措施。测试这些技术的一个主要目标平台是由商用CPU组成的集群计算环境。 拟议的活动可使多个学科受益。许多科学和工程企业生成和/或希望使用大型数据集。一些金融和消费者应用还期望有效地利用大量的业务数据集合。在这些数据集中发现趋势，现象和结构可以通过更有效的可视化来帮助。特别地，对于太大而不能在核内处理的大数据集，由于辅助存储的相对慢的访问时间，计算可视化的时间可能非常高。通过资源（内存、CPU和通信）有效处理减少这些时间将提高科学可视化用户和消费者的生产力。 此外，处理时间的减少可以使某些非常大的问题的考虑变得容易。拟议的活动建立在PI先前的工作和其他并行堆芯外等值面提取经验的基础上。 PI可以访问必要的计算机资源来完成工作，包括访问三个站点的集群计算机和其他三个站点的超级计算机。由于拟议的活动对整个科学界使用的可视化方法的影响，整个社会可能会受益于该项目所帮助的学科科学的新知识发现。此外，拟议的工作将有利于管理和理解越来越多的数据收集有关科学，工程和商业现象。 该项目的结果将通过公开文献、会议和网络上的出版物传播。通过PI与NASA、NIH和其他研究人员的合作，这项工作的结果很有可能在多个学科产生影响。该项目还将通过以下方式帮助本科生和研究生的培训和发展：（1）对项目支持的研究生进行PI指导，（2）在定期研究论坛上对结果进行演示，讨论和分析，以及（3）将研究结果整合到PI教授的研究生计算机图形学/可视化课程中。

Isosurface extraction and rendering is a useful and popular method for exploring volume datasets.While many studies of extraction techniques have been presented, few researchers have considered how to perform isosurface extraction from very large datasets in a way that utilizes parallel computation while also effectively managing memory access. For out-of-core datasets (i.e., those too large to be processed entirely in main memory), delays from access of secondary storage must be minimized if high performance is to be achieved.In this project, an investigation of new techniques for parallel, out-of-core isosurface extraction are conducted. The work seeks to exploit multiple types of parallelism, effectively organize memory access to minimize access penalties, and to effectively manage inter-process communication. A hallmark of the workis that it focuses on total system performance rather than attempting to only maximize intermediary measures of a single aspect of performance. One primary target platform for testing of the techniques is cluster computation environments comprised of commodity CPUs.Intellectual Merit. The proposed activity can benefit multiple disciplines. Many scientific and engineering enterprises generate and/or wish to use large datasets. Some finance and consumer applications also desire to effectively utilize large collections of business data. Discovery of trends, phenomena, and structures in those datasets can be aided by more efficient visualization. In particular, for large datasets too large to be processed in-core, the time to compute a visualization is likely to be very high due to the relatively slow access times of the secondary storage. Reduction of these times by resource (memory, CPU, and communication)-effective processing will increase productivity among scientific visualization users and consumers. In addition, reduction of processing times may make tractable the consideration of certain very large problems. The proposed activity builds upon prior work of the PI and of the other experiences with parallel out-of-core isosurface extraction. The PI has access to the necessary computer resources to complete the work, including access to cluster computers at three sites and to supercomputers at three other sites.Due to the impact of the proposed activity on visualization methods that are used across the scientific community, society at large is likely to benefit via new knowledge discovery by thediscipline science that this project aids. In addition, the proposed work will be beneficial in managing and understanding the increasing body of data being collected about scientific, engineering, and business phenomena. The project's results will be disseminated via publication inthe open literature, in conferences, and on the web. Through the PI's partnerships with NASA, NIH, and other researchers, the results of the work have a high probability of producing an impact in multiple disciplines. The project will also aid undergraduate and graduate student training and development via (1) PI-mentoring of the graduate student supported on the project, (2) presentation, discussion, and analysis of results at a regular research forum, and (3) integration of research findings in graduate computer graphics/visualization courses taught by the PI.