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To solve the problem of efficient storing and updating of model parameters in the learning process, the parameter server is concerned as a high-throughput distributed machine learning (ML) architecture with the emergence of big models with billions of parameters. Current parameter servers, such as the Parameter Server and the Petuum, do not address data management and lack high-level data abstraction...
The growing use of Big Data frameworks on large machines highlights the importance of performance issues and the value of High Performance Computing (HPC) technology. This paper looks carefully at three major frameworks Spark, Flink and Message Passing Interface (MPI) both in scaling across nodes and internally over the many cores inside modern nodes. We focus on the special challenges of the Java...
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