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Increasing performance of parallel and distributed systems in high performance computing using weight based approach

机译:使用基于权重的方法在高性能计算中提高并行和分布式系统的性能

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High performance computing (HPC), large scale instruments and continuously increasing simulation tools are generating data at a huge rate that are difficult to be effectively managed and analyzed. Implementation of MapReduce model provides a way for processing huge volumes of data through the use of large number of commodity computers. MapReduce and Hadoop have been initially used for processing web data. But recently they have been used for processing more complex scientific applications. The proposed system helps to understand the impact of file system, network and programming modes on performance. The performance an application can obtain is largely work load dependent. Design of every MapReduce system has to include the Key factors like High speed, Quick Response, Accurate result. The proposed work is to improve the scheduling and management functionality of Parallel and Distributed Computing. The proposed technique Weight based Approach improves the performance by improving job scheduling strategy.
机译:高性能计算(HPC),大型仪器和不断增加的仿真工具正在以巨大的速度生成数据,这些数据难以有效地管理和分析。 MapReduce模型的实现提供了一种通过使用大量商用计算机来处理大量数据的方法。 MapReduce和Hadoop最初已用于处理Web数据。但是最近它们已被用于处理更复杂的科学应用。提出的系统有助于了解文件系统,网络和编程模式对性能的影响。应用程序可以获得的性能在很大程度上取决于工作负载。每个MapReduce系统的设计都必须包括诸如高速,快速响应,准确结果之类的关键因素。拟议的工作是改善并行和分布式计算的调度和管理功能。所提出的基于权重的技术通过改进作业调度策略来提高性能。

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