Fast Generation of Large Task Network Mappings

机译：快速生成大型任务网络映射

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In the context of networks of massively parallel execution models, optimizing the locality if inter-process communication is a major performance issue. We propose two heuristics to solve a dataflow process network mapping problem, where a network of communicating tasks is placed into a set of processors with limited resource capacities, while minimizing the overall communication bandwidth between processors. Those approaches are designed to tackle instances of over one hundred thousand tasks in acceptable time.

机译：在大规模并行执行模型的网络环境中，如果进程间通信是主要的性能问题，则优化位置。我们提出了两种启发式方法来解决数据流过程网络映射问题，其中将通信任务的网络放置在资源容量有限的一组处理器中，同时使处理器之间的总体通信带宽最小。这些方法旨在在可接受的时间内处理超过十万个任务的实例。

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《IEEE International Parallel Distributed Processing Symposium》|2014年|1526-1530|共5页
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Berger Karl-Eduard; Galea Francois; Cun Bertrand Le; Sirdey Renaud;
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Context; Logic gates; Network topology; Optimization; Partitioning algorithms; Program processors; Topology; heuristics; manycore execution optimization; process network mapping;

机译：上下文;逻辑门;网络拓扑;优化;分区算法;程序处理器;拓扑;启发式;多核执行优化;过程网络映射;

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6. Mapping the functional anatomy of task preparation: Priming task‐appropriate brain networks [O] . Catherine Fassbender, John J. Foxe, Hugh Garavan 2006

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7. Fast-AT: Fast Automatic Thumbnail Generation using Deep Neural Networks [O] . Esmaeili, Seyed A., Singh, Bharat, Davis, Larry S. 2017

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Fast Generation of Large Task Network Mappings

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