High-Performance Computational and Information Technologies for Numerical Models and Data Processing

D. Akhmed-Zaki, M. Mansurova, Timur Imankulov, D. Lebedev, O. Turar, B. Daribayev, S. Aubakirov, A. Shomanov, K. Aidarov
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Abstract

This chapter discusses high-performance computational and information technologies for numerical models and data processing. In the first part of the chapter, the numerical model of the oil displacement problem was considered by injection of chemical reagents to increase oil recovery of reservoir. Moreover the fragmented algorithm was developed for solving this problem and the algorithm for high-performance visualization of calculated data. Analysis and comparison of parallel algorithms based on the fragmented approach and using MPI technologies are also presented.The algorithm for solving given problem on mobile platforms andanalysisofcomputationalresultsisgiventoo.Inthesecondpartofthechapter,theproblem ofunstructuredandsemi-structureddataprocessingwasconsidered.Itwasdecidedtoaddress the task of n-gram extraction which requires a lot of computing with large amount of textual data. In order to deal with such complexity, there was a need to adopt and implement parallelization patterns. The second part of the chapter also describes parallel implementation of the document clustering algorithm that used a heuristic genetic algorithm. Finally, a novel UPC implementation of MapReduce framework for semi-structured data processing was
数值模型和数据处理的高性能计算和信息技术
本章讨论了用于数值模型和数据处理的高性能计算和信息技术。本章第一部分考虑了通过注入化学试剂提高油藏采收率的驱油问题的数值模型。针对这一问题,提出了碎片化算法和计算数据的高性能可视化算法。对基于碎片化方法的并行算法和采用MPI技术的并行算法进行了分析和比较。给出了在移动平台上求解给定问题的算法和计算结果分析。ofunstructuredandsemi-structureddataprocessingwasconsidered Inthesecondpartofthechapter,问题。它决定解决n-gram提取的任务,这需要大量的计算和大量的文本数据。为了处理这种复杂性,需要采用并实现并行化模式。本章的第二部分还描述了使用启发式遗传算法的文档聚类算法的并行实现。最后,提出了一种基于MapReduce框架的半结构化数据处理UPC实现方法
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