Cluster-M Paradigms for High-Order Heterogeneous Procedural Specification Computing

The paper uses parallel computation of grid and data grid as theoretical basis. The isomerism of each node and difference of communication rate under grid environment, and massive data query makes query operations of database difficult. For the new structural characteristic, the paper proposes a parallel JOIN algorithm based on massive data, it fully uses parallelism and reduces transmission volume to shorten response time. The paper includes implementation and optimization of JOIN algorithm, fault-tolerant mechanism, RDMM, partition and transmission of massive data.

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“…The partial result set is merged with the above partial result set, which is sent to sub-master. (8) Return to result set.…”

Section: B Parameter Definitionmentioning

confidence: 99%

Optimization algorithm of massive data query based on JOIN

Zheng

Sun

2014

2014 IEEE 5th International Conference on Software Engineering and Service Science

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“…The general problem of optimally mapping tasks to machines (resource management) in heterogeneous parallel and distributed computing environments has been shown in general to be NP-complete (e.g., [10,14,16]). Thus, the development of heuristic techniques to find a near-optimal solution for the mapping problem is an active area of research (e.g., [1,4,5,13,15,18,20,21,26]). …”

Section: Introductionmentioning

confidence: 99%

Dynamic resource management heuristics for minimizing makespan while maintaining an acceptable level of robustness in an uncertain environment

Mehta

Smith

Siegel

et al. 2006

12th International Conference on Parallel and Distributed Systems - (ICPADS'06)

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“…This is essential to match the type of each code block with a suitable machine type in the system. The Cluster-M parallel programming paradigm introduced in [3] meets most of the above requirements. Cluster-M models a parallel task as a problem specification, independent of the underlying architecture.…”

Section: Modeling Requirementsmentioning

confidence: 99%

“…Clusters in intermediate levels are merged, split, and/or their elements manipulated according to computation and communication requirements. Several essential Cluster-M constructs needed to formulate the Cluster-M specification of a task are discussed in[3].…”

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confidence: 99%

A Selection Theory and Methodology for Heterogeneous Supercomputing

Chen

Eshaghian²,

Khokhar

et al.

Proceedings. Workshop on Heterogeneous Processing,

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In this paper, a methodology for mapping algorithms onto heterogeneous suite of supercomputers is presented. A n approach for selecting an optimal suite of computers for solving problems with diverse computational requirements, called Heterogeneous Optimal Selection Theory (HOST), is presented. HOST is an extension t o Augmented Optimal Selection Theory in two ways: It incorporates heterogeneous parallelism embedded in the tasks, and it reflects the costs associated in using various fine grain mapping strategies at individual machine level. The proposed mapping methodology is based on the Cluster-M programming paradigm. For the mapping purpose, the input format, assumed in HOST, is modeled in terms of Hierarchical Cluster-M specification and representation. For a given problem, a hj'ierarchical Cluster-M specification is generated to indicate the execution of concurrent tasks at different stizges of the computation. This specification is then mapped onto the Hierarchical Cluster-M representation ofthe underlying heterogeneous suite of supercomputers. 15 1066-1220193 $3.00 0 1.993 IEEE

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Cluster-M Paradigms for High-Order Heterogeneous Procedural Specification Computing

Cited by 13 publications

References 4 publications

Optimization algorithm of massive data query based on JOIN

Optimization algorithm of massive data query based on JOIN

Dynamic resource management heuristics for minimizing makespan while maintaining an acceptable level of robustness in an uncertain environment

A Selection Theory and Methodology for Heterogeneous Supercomputing

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