The current big data era routinely requires the processing of large-scale data on massive distributed computing clusters. In these applications, data sets are often so large that they cannot be housed in the memory and/or the disk of any one computer. Thus, the data and the processing are typically distributed across multiple nodes. Distributed computation is thus a necessity rather than a luxury. The widespread use of such clusters presents several opportunities and advantages over traditional computing paradigms. However, it also presents newer challenges where coding-theoretic ideas have recently had a significant impact. Large-scale clusters (which can be heterogeneous in nature) suffer from the problem of stragglers, which are slow or failed worker nodes in the system. Thus, the overall speed of a computation is typically dominated by the slowest node in the absence of a sophisticated assignment of tasks to the worker nodes.

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基于编码理论的分布式矩阵计算：消除大规模数据处理中的瓶颈

当前的大数据时代通常需要在海量分布式计算集群上处理海量数据。在这些应用程序中，数据集通常非常大，以至于它们无法容纳在任何一台计算机的内存和/或磁盘中。因此，数据和处理通常分布在多个节点上。因此，分布式计算是必需品而不是奢侈品。与传统计算范式相比，此类集群的广泛使用提供了多种机会和优势。然而，它也带来了新的挑战，其中编码理论思想最近产生了重大影响。大规模集群（本质上可能是异构的）存在落后者的问题，即系统中的工作节点缓慢或出现故障。因此，