Google Distributed Computing.pdf下载

weixin_39821620 2020-09-09 06:00:27

Google distributed computing，distributed algorithm， distributed system
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Google distributed computing，distributed algorithm， distributed system

Machine Learning and Data Mining (MLDM) algorithms are becoming increasingly important in analyzing large volume of data generated by simulations, experiments and mobile devices. With increasing data volume, distributed memory systems (such as tightly connected supercomputers or cloud computing systems) are becoming important in designing in-memory and massively parallel MLDM algorithms. Yet, the majority of open source MLDM software is limited to sequential execution with a few supporting multi-core/manycore execution. In this paper, we extend recently proposed Google TensorFlow for execution on large scale clusters using Message Passing Interface (MPI). Our approach requires minimal changes to the TensorFlow runtime – making the proposed implementation generic and readily usable to increasingly large users of TensorFlow. We evaluate our implementation using an InﬁniBand cluster and several well known datasets. Our evaluation indicates the eﬃciency of our proposed implementation.

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