Modern learning algorithms use gradient descent updates to train inferential
models that best explain data. Scaling these approaches to massive data sizes
requires proper distributed gradient descent schemes where distributed worker
nodes compute partial gradients based on their partial and local data sets, and
send the results to a master node where all the computations are aggregated
into a full gradient and the learning model is updated. However, a major
performance bottleneck that arises is that some of the worker nodes may run
slow. These nodes a.k.a. stragglers can significantly slow down computation as
the slowest node may dictate the overall computational time. We propose a
distributed computing scheme, called Batched Coupon's Collector (BCC) to
alleviate the effect of stragglers in gradient methods. We prove that our BCC
scheme is robust to a near optimal number of random stragglers. We also
empirically demonstrate that our proposed BCC scheme reduces the run-time by up
to 85.4% over Amazon EC2 clusters when compared with other straggler mitigation
strategies. We also generalize the proposed BCC scheme to minimize the
completion time when implementing gradient descent-based algorithms over
heterogeneous worker nodes.

本研究提出了一种名为批处理优惠券集合（BCC）的分布式计算方案，以减轻梯度法中流浪者的影响，该方案在随机弱者数量接近最优时具有鲁棒性，并证明了 BCC 方案的有效性，提高了 85.4％的运行时间。此外，该方案的泛化结果证明了其能够在异构工作节点上实现梯度下降算法以最小化完成时间。