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首页> 外文期刊>Parallel and Distributed Systems, IEEE Transactions on >Maiter: An Asynchronous Graph Processing Framework for Delta-Based Accumulative Iterative Computation
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Maiter: An Asynchronous Graph Processing Framework for Delta-Based Accumulative Iterative Computation

机译：Maiter：用于基于增量的累积迭代计算的异步图处理框架

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摘要

Myriad of graph-based algorithms in machine learning and data mining require parsing relational data iteratively. These algorithms are implemented in a large-scale distributed environment to scale to massive data sets. To accelerate these large-scale graph-based iterative computations, we propose delta-based accumulative iterative computation (DAIC). Different from traditional iterative computations, which iteratively update the result based on the result from the previous iteration, DAIC updates the result by accumulating the “changes” between iterations. By DAIC, we can process only the “changes” to avoid the negligible updates. Furthermore, we can perform DAIC asynchronously to bypass the high-cost synchronous barriers in heterogeneous distributed environments. Based on the DAIC model, we design and implement an asynchronous graph processing framework, Maiter. We evaluate Maiter on local cluster as well as on Amazon EC2 Cloud. The results show that Maiter achieves as much as 60$times$ speedup over Hadoop and outperforms other state-of-the-art frameworks.
机译：机器学习和数据挖掘中无数的基于图的算法都需要迭代解析关系数据。这些算法在大规模分布式环境中实现，可以扩展到海量数据集。为了加速这些基于图形的大规模迭代计算，我们提出了基于增量的累积迭代计算（DAIC）。与传统的迭代计算不同，传统的迭代计算基于前一次迭代的结果迭代更新结果，而DAIC通过累积迭代之间的“变化”来更新结果。通过DAIC，我们只能处理“更改”以避免微不足道的更新。此外，我们可以异步执行DAIC，以绕过异构分布式环境中的高成本同步障碍。基于DAIC模型，我们设计并实现了异步图形处理框架Maiter。我们在本地集群以及Amazon EC2 Cloud上评估Maiter。结果表明，Maiter在Hadoop上实现了多达60 $ times $ 的加速，并且性能优于其他最新技术构架。

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