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International Journal for Multiscale Computational Engineering

Erscheint 6 Ausgaben pro Jahr

ISSN Druckformat: 1543-1649

ISSN Online: 1940-4352

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.4 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.3 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 2.2 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00034 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.46 SJR: 0.333 SNIP: 0.606 CiteScore™:: 3.1 H-Index: 31

Indexed in

Efficient Parallel Execution of Event-Driven Electromagnetic Hybrid Models

Volumen 5, Ausgabe 1, 2007, pp. 27-38
DOI: 10.1615/IntJMultCompEng.v5.i1.40
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ABSTRAKT

New discrete-event formulations of physics simulation models are emerging that can outperform traditional time-stepped models, especially in simulations containing multiple timescales. Detailed simulation of the Earth's magnetosphere, for example, requires execution of submodels that operate at timescales that differ by orders of magnitude. In contrast to time-stepped simulation, which requires tightly coupled updates to almost the entire system state at regular time intervals, the new approaches that use discrete event simulation (DES) modeling help evolve the states of submodels on relatively independent timescales. However, in contrast to the relative ease of parallelization of time-stepped codes, the parallelization of DES-based models raises challenges with respect to their scalability and performance. One of the key challenges is to improve the computation granularity to offset synchronization and communication overheads within and across processors. Our previous work on parallelization was limited in scalability and run-time performance due to such challenges. Here, we report on optimizations we performed on DES-based plasma simulation models to improve parallel execution performance. The mapping of the model to simulation processes is optimized via aggregation techniques, and the parallel run-time engine is optimized for communication and memory efficiency. The net result is the capability to simulate hybrid particle-in-cell models with over two billion ion particles using 512 processors on supercomputing platforms.

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