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Accelerating Iterative Big Data Computing Through MPI 被引量:5
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作者 梁帆 鲁小亿 《Journal of Computer Science & Technology》 SCIE EI CSCD 2015年第2期283-294,共12页
Current popular systems, Hadoop and Spark, cannot achieve satisfied performance because of the inefficient overlapping of computation and communication when running iterative big data applications. The pipeline of com... Current popular systems, Hadoop and Spark, cannot achieve satisfied performance because of the inefficient overlapping of computation and communication when running iterative big data applications. The pipeline of computing, data movement, and data management plays a key role for current distributed data computing systems. In this paper, we first analyze the overhead of shuffle operation in Hadoop and Spark when running PageRank workload, and then propose an event-driven pipeline and in-memory shuffle design with better overlapping of computation and communication as DataMPI- Iteration, an MPI-based library, for iterative big data computing. Our performance evaluation shows DataMPI-Iteration can achieve 9X-21X speedup over Apache Hadoop, and 2X-3X speedup over Apache Spark for PageRank and K-means. 展开更多
关键词 iterative computation DataMPI SPARK Hadoop MapReduce
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An adaptive switching scheme for iterative computing in the cloud 被引量:2
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作者 Yu ZHANG Xiaofei LIAO Hai JIN Li LIN Feng LU 《Frontiers of Computer Science》 SCIE EI CSCD 2014年第6期872-884,共13页
Delta-based accumulative iterative computation (DAIC) model is currently proposed to support iterative algorithms in a synchronous or an asynchronous way. However, both the synchronous DAIC model and the asynchronou... Delta-based accumulative iterative computation (DAIC) model is currently proposed to support iterative algorithms in a synchronous or an asynchronous way. However, both the synchronous DAIC model and the asynchronous DAIC model only satisfy some given conditions, respectively, and perform poorly under other conditions either for high synchronization cost or for many redundant activations. As a result, the whole performance of both DAIC models suffers from the serious network jitter and load jitter caused by multi- tenancy in the cloud. In this paper, we develop a system, namely Hyblter, to guarantee the performance of iterative algorithms under different conditions. Through an adaptive execution model selection scheme, it can efficiently switch between synchronous and asynchronous DAIC model in order to be adapted to different conditions, always getting the best performance in the cloud. Experimental results show that our approach can improve the performance of current solutions up to 39.0%. 展开更多
关键词 iterative algorithm computational skew communication skew CLOUD delta-based accumulative iterative computation
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THE IMPLICIT METHOD OF STREAMLINE ITERATION FOR COMPUTING TWO-DIMENSIONAL VISCOUS INCOMPRESSIBLE FLOW
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作者 Jiang Jin-liang, Shanghai Institute of Electric Power, Shanghai 200090, P.R.China 《Journal of Hydrodynamics》 SCIE EI CSCD 1991年第2期88-93,共6页
This paper presents the implicit method of streamline iteration on the bases of the method of streamline itera- tion for computing two-dimensional viscous incompressible steady flow in a channel with arbitrary shape. ... This paper presents the implicit method of streamline iteration on the bases of the method of streamline itera- tion for computing two-dimensional viscous incompressible steady flow in a channel with arbitrary shape. A new total pressure equation of viscous incompressible flow is introduced in this paper and the equation is numerically computed by the implicit method. It is shown from the computational results of examples that the implicit method of streamline iteration can speed up the convergence and decrease the computational time. 展开更多
关键词 Flow LINE THE IMPLICIT METHOD OF STREAMLINE ITERATION FOR computing TWO-DIMENSIONAL VISCOUS INCOMPRESSIBLE FLOW
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An effective framework for asynchronous incremental graph processing 被引量:5
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作者 Xinqiao LV Wei XIAO +3 位作者 Yu ZHANG Xiaofei LIAO Hai JIN Qiangsheng HUA 《Frontiers of Computer Science》 SCIE EI CSCD 2019年第3期539-551,共13页
Although many graph processing systems have been proposed, graphs in the real-world are often dynamic. It is important to keep the results of graph computation up-todate. Incremental computation is demonstrated to be ... Although many graph processing systems have been proposed, graphs in the real-world are often dynamic. It is important to keep the results of graph computation up-todate. Incremental computation is demonstrated to be an efficient solution to update calculated results. Recently, many incremental graph processing systems have been proposed to handle dynamic graphs in an asynchronous way and are able to achieve better performance than those processed in a synchronous way. However, these solutions still suffer from sub-optimal convergence speed due to their slow propagation of important vertex state (important to convergence speed) and poor locality. In order to solve these problems, we propose a novel graph processing framework. It introduces a dynamic partition method to gather the important vertices for high locality, and then uses a priority-based scheduling algorithm to assign them with a higher priority for an effective processing order. By such means, it is able to reduce the number of updates and increase the locality, thereby reducing the convergence time. Experimental results show that our method reduces the number of updates by 30%, and reduces the total execution time by 35%, compared with state-of-the-art systems. 展开更多
关键词 incremental computation graph processing iterative computation ASYNCHRONOUS CONVERGENCE
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