Graph partitioning and scheduling for distributed dataflow computation

Larissa Laich
During the last years, the amount of data which can be represented and processed as graph structured data has massively increased. To process these large data sets, graph processing systems have been developed which distribute and partition a graph among multiple machines. Due to an increase in processing power and data collection, machine learning and especially neural networks have become very popular. Consequently, machine learning systems like TensorFlow have emerged. Machine learning models can be...
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