EAVL: The Extreme-scale Analysis and Visualization Library
EAVL: The Extreme-scale Analysis and Visualization Library
复制标题
EAVL：超大规模分析和可视化库
DOI：
10.2312/egpgv/egpgv12/021-030
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
R. Sisneros
中科院分区：
文献类型：
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作者：
J. Meredith;Sean Ahern;D. Pugmire;R. Sisneros
对科学模拟代码生成的数据进行分析和可视化是从计算中实现科学的关键一步。然而，在当前的硬件和软件路径上，科学发现面临着沿着许多挑战。首先，只有先进的并行技术才能充分利用未来机器的空前规模。此外，随着计算的改进超过I/O的改进，更多的数据将被丢弃，I/O繁重的分析将受到影响。此外，有限的内存环境，特别是在可以避开一些I/O限制的现场分析的背景下，将需要算法和基础设施的效率。最后，具有复杂数据模型的高级仿真代码需要在分析工具中使用相称的数据模型。然而，为并行性和大数据设计的社区可视化和分析工具在这些领域中的许多方面都存在不足。在本文中，我们描述了EAVL，这是一个新的库，其基础设施和算法旨在满足当前和未来几代科学软件和硬件的这些关键需求。我们展示了EAVL的结果，展示了其强大的数据模型，高级并行性和效率的优势。
Analysis and visualization of the data generated by scientific simulation codes is a key step in enabling science from computation. However, a number of challenges lie along the current hardware and software paths to scientific discovery. First, only advanced parallelism techniques can take full advantage of the unprecedented scale of coming machines. In addition, as computational improvements outpace those of I/O, more data will be discarded and I/O-heavy analysis will suffer. Furthermore, the limited memory environment, particularly in the context of in situ analysis which can sidestep some I/O limitations, will require efficiency of both algorithms and infrastructure. Finally, advanced simulation codes with complex data models require commensurate data models in analysis tools. However, community visualization and analysis tools designed for parallelism and large data fall short in a number of these areas. In this paper, we describe EAVL, a new library with infrastructure and algorithms designed to address these critical needs for current and future generations of scientific software and hardware. We show results from EAVL demonstrating the strengths of its robust data model, advanced parallelism, and efficiency.