Integrating data and compute-intensive workflows for uncertainty quantification in large-scale simulation: application to model-based hazard analysis
Integrating data and compute-intensive workflows for uncertainty quantification in large-scale simulation: application to model-based hazard analysis
复制标题
集成数据和计算密集型工作流程以实现大规模模拟中的不确定性量化：基于模型的危害分析的应用
DOI：
10.1080/00207160.2013.844337
复制
发表时间：
2014
影响因子：
1.8
通讯作者：
V. Chaudhary
中科院分区：
文献类型：
--
作者：
Shivaswamy Rohit;A. Patra;V. Chaudhary
不确定性量化中使用的模拟集成的复杂推理导致管理大量数据和执行CPU密集型计算的双重计算挑战。虽然使用代理、本地化和并行化的算法创新可以使问题变得可行，但仍然有非常大的数据和计算任务。当数据仓库和数据挖掘与计算成本高昂的任务交织在一起时，处理大型数据的问题变得更加复杂。我们在这里提出了一种解决这个问题的方法，通过使用一个精心编排的工作流程中适合每个任务的硬件组合。计算环境本质上是Netezza数据库和高性能集群的集成。它基于一个简单的想法，即分离数据密集型和计算密集型任务，并为它们分配正确的架构。我们在这里提出的布局的计算模型和新的计算方案，通过生成概率危险图。
Complex inference from simulation ensembles used in uncertainty quantification leads to twin computational challenges of managing large amount of data and performing CPU-intensive computing. While algorithmic innovations using surrogates, localization and parallelization can make the problem feasible, one still has very large data and compute tasks. The problem of dealing with large data gets compounded when data warehousing and data mining are intertwined with computationally expensive tasks. We present here an approach to solving this problem by using a mix of hardware suitable for each task in a carefully orchestrated workflow. The computing environment is essentially an integration of Netezza database and high-performance cluster. It is based on the simple idea of segregating the data-intensive and compute-intensive tasks and assigning the right architecture for them. We present here the layout of the computing model and the new computational scheme adopted to generate probabilistic hazard maps.
影响因子：
0.9
作者：
Goldstein, Michael;Rougier, Jonathan
通讯作者：
Rougier, Jonathan