CodedReduce: A Fast and Robust Framework for Gradient Aggregation in Distributed Learning
CodedReduce: A Fast and Robust Framework for Gradient Aggregation in Distributed Learning
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DOI：
10.1109/tnet.2021.3109097
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发表时间：
2019-02
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通讯作者：
Amirhossein Reisizadeh;Saurav Prakash;Ramtin Pedarsani;A. Avestimehr
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作者：
Amirhossein Reisizadeh;Saurav Prakash;Ramtin Pedarsani;A. Avestimehr
我们专注于用于大规模分布式学习的常用同步梯度下降范式，为此，人们越来越兴趣发展有效且健壮的梯度聚合策略，这些策略克服了两个关键的系统瓶颈：交流带宽和散乱的延误。已经提出了环形呼叫（RAR）设计，以避免在任何特定节点的带宽瓶颈，允许每个工人仅与其交流另一方面，已在逻辑环中排列的邻居（GC）是通过允许精心设计的数据集的冗余分配给工人的，以减轻大师工作的拓扑。通信拓扑设计和数据集分配策略，命名为CodedReduce（CR），将RAR和GC的最佳作用结合在一起。带宽利用，并在节点上仔细设计了冗余数据集分配和编码策略，以使提出的梯度聚合方案尤其适用于散乱者，我们量化了拟议的CR方案的通信并行化增益和弹性，并证明其最佳性交流拓扑是一棵普通的树，我们表征了CR的预期运行时间，并显示了与基准方案相比。我们紧急评估了我们在亚马逊EC2上提议的CR设计的性能，并证明它的加速度分别达到了$ 27.2 \ times $和$ 7.0 \ times $ $，这是在基准测试的GC和RAR上的。
We focus on the commonly used synchronous Gradient Descent paradigm for large-scale distributed learning, for which there has been a growing interest to develop efficient and robust gradient aggregation strategies that overcome two key system bottlenecks: communication bandwidth and stragglers’ delays. In particular, Ring-AllReduce (RAR) design has been proposed to avoid bandwidth bottleneck at any particular node by allowing each worker to only communicate with its neighbors that are arranged in a logical ring. On the other hand, Gradient Coding (GC) has been recently proposed to mitigate stragglers in a master-worker topology by allowing carefully designed redundant allocation of the data set to the workers. We propose a joint communication topology design and data set allocation strategy, named CodedReduce (CR), that combines the best of both RAR and GC. That is, it parallelizes the communications over a tree topology leading to efficient bandwidth utilization, and carefully designs a redundant data set allocation and coding strategy at the nodes to make the proposed gradient aggregation scheme robust to stragglers. In particular, we quantify the communication parallelization gain and resiliency of the proposed CR scheme, and prove its optimality when the communication topology is a regular tree. Moreover, we characterize the expected run-time of CR and show order-wise speedups compared to the benchmark schemes. Finally, we empirically evaluate the performance of our proposed CR design over Amazon EC2 and demonstrate that it achieves speedups of up to $27.2\times $ and $7.0\times $ , respectively over the benchmarks GC and RAR.