The SDAV Software Frameworks for Visualization and Analysis on Next-Generation Multi-Core and Many-Core Architectures

Christopher M. Sewell, J. Meredith, K. Moreland, T. Peterka, David E. DeMarle, Li-Ta Lo, J. Ahrens, Robert Maynard, Berk Geveci
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引用次数: 9

Abstract

This paper surveys the four software frameworks being developed as part of the visualization pillar of the SDAV (Scalable Data Management, Analysis, and Visualization) Institute, one of the SciDAC (Scientific Discovery through Advanced Computing) Institutes established by the ASCR (Advanced Scientific Computing Research) Program of the U.S. Department of Energy. These frameworks include EAVL (Extreme-scale Analysis and Visualization Library), DAX (Data Analysis at Extreme), DIY (Do It Yourself), and PISTON. The objective of these frameworks is to facilitate the adaptation of visualization and analysis algorithms to take advantage of the available parallelism in emerging multi-core and many-core hardware architectures, in anticipation of the need for such algorithms to be run in-situ with LCF (leadership-class facilities) simulation codes on supercomputers.
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面向下一代多核与多核架构的SDAV可视化与分析软件框架
本文调查了作为SDAV(可扩展数据管理、分析和可视化)研究所可视化支柱的一部分正在开发的四个软件框架,SDAV是由美国能源部ASCR(高级科学计算研究)计划建立的SciDAC(通过高级计算进行科学发现)研究所之一。这些框架包括EAVL(极端规模分析和可视化库)、DAX(极端数据分析)、DIY(自己动手)和活塞。这些框架的目标是促进可视化和分析算法的适应,以利用新兴的多核和多核硬件架构中的可用并行性,预计这些算法需要在超级计算机上与LCF(领导级设施)模拟代码一起原位运行。
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