gLucifer: next generation visualization framework for high-performance computational geodynamics
gLucifer: next generation visualization framework for high-performance computational geodynamics
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DOI：
10.1007/s10069-008-0010-2
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发表时间：
2008-06
期刊：
影响因子：
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通讯作者：
D. Stegman;Louis Moresi;R. Turnbull;J. Giordani;P. Sunter;Alan Lo;S. Quenette
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文献类型：
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作者：
D. Stegman;Louis Moresi;R. Turnbull;J. Giordani;P. Sunter;Alan Lo;S. Quenette
高性能计算提供了前所未有的功能，可以在很短的时间内生成更高分辨率的4D模型。因此，需要新一代的可视化系统，能够保持与所产生的大量数据的奇偶性。在尝试将如此多的数据写入磁盘时，每个计算步骤都引入了显著的性能瓶颈，然而大多数现有的可视化软件包固有地依赖于从转储文件中阅读数据。现有的软件包在相当基础的水平上假设了后处理，并且不太适合绘制大量的特殊粒子。这就需要建立一个新的可视化系统，以满足大规模地球动力学建模的需要。我们已经开发了这样一个系统，gLucifer，使用一个软件框架的方法，使我们的努力在其他领域的研究有效地重用。gLucifer能够“动态”生成4D数据集的电影（同时运行并行科学应用程序），而不会产生性能瓶颈。通过消除后处理中可视化结果所涉及的大部分人工工作，gLucifer将科学家与数值实验重新联系起来。以前甚至很难管理的数据集可以在不写入磁盘的情况下被有效地探索和询问，并且因为这种方法完全基于跨与科学应用所利用的处理器一样多的处理器分布的存储器，所以可视化解决方案可扩展到真实的时间中呈现的TB级数据。
High-performance computing provides unprecedented capabilities to produce higher resolution 4-D models in a fraction of time. Thus, the need exists for a new generation of visualization systems able to maintain parity with the enormous volume of data generated. In attempting to write this much data to disk, each computational step introduces a significant performance bottleneck, yet most existing visualization software packages inherently rely on reading data in from a dump file. Available packages make this assumption of postprocessing at quite a fundamental level and are not very well suited for plotting very large numbers of specialized particles. This necessitates the creation of a new visualization system that meets the needs of large-scale geodynamic modeling. We have developed such a system, gLucifer, using a software framework approach that allows efficient reuse of our efforts in other areas of research. gLucifer is capable of producing movies of a 4-D data set “on the fly” (simultaneously with running the parallel scientific application) without creating a performance bottleneck. By eliminating most of the human efforts involved in visualizing results through postprocessing, gLucifer reconnects the scientist to the numerical experiment as it unfolds. Data sets that were previously very difficult to even manage may be efficiently explored and interrogated without writing to disk, and because this approach is based entirely on memory distributed across as many processors as are being utilized by the scientific application, the visualization solution is scalable into terabytes of data being rendered in real time.