arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2109.12930cs.DC
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使用云的分布式计算

Distributed Computing With the Cloud

Yehuda Afek, Gal Giladi, Boaz Patt-Shamir

摘要:

我们研究无所不在的云存储对分布式计算的影响。我们指定了一个网络模型与规定的带宽连接标准的处理节点,此外,被动存储节点的链接。每个被动节点代表一个云存储系统,如Dropbox,Google Drive等。我们研究了这个模型中的一些任务,假设一个云节点连接到所有其他节点,这些节点任意相互连接。我们给出了协作写入云和从云读取的基本任务的实现,以及更高级的应用程序,如矩阵乘法和联邦学习。我们的研究结果表明,利用节点-云链接以及节点-节点链接可以大大加快计算速度,与处理器仅通过云或仅通过网络链接进行通信的情况相比。我们提供一般有向图的结果,并与处理节点之间的“胖”链接的图形。对于一般情况下,我们提供了最佳的算法,使用流技术上传和下载文件。我们使用这些原语来推导\emph{combining}的算法,其中每个处理器节点都有一个输入值,任务是在给定的关联运算符下计算组合值。在胖链接的情况下,我们假设处理器之间的链接是双向的,并具有高带宽,我们给任何交换组合算子(如矢量加法)的近最优算法。对于矩阵乘法(或其他非交换组合运算符)的任务,其中输入是有序的,我们在简单的“轮子”网络中给出了清晰的结果,其中处理节点排列在一个环中,并且都连接到一个单一的云节点。

英文摘要:

We investigate the effect of omnipresent cloud storage on distributed computing. We specify a network model with links of prescribed bandwidth that connect standard processing nodes, and, in addition, passive storage nodes. Each passive node represents a cloud storage system, such as Dropbox, Google Drive etc. We study a few tasks in this model, assuming a single cloud node connected to all other nodes, which are connected to each other arbitrarily. We give implementations for basic tasks of collaboratively writing to and reading from the cloud, and for more advanced applications such as matrix multiplication and federated learning. Our results show that utilizing node-cloud links as well as node-node links can considerably speed up computations, compared to the case where processors communicate either only through the cloud or only through the network links. We provide results for general directed graphs, and for graphs with ``fat'' links between processing nodes. For the general case, we provide optimal algorithms for uploading and downloading files using flow techniques. We use these primitives to derive algorithms for \emph{combining}, where every processor node has an input value and the task is to compute a combined value under some given associative operator. In the case of fat links, we assume that links between processors are bidirectional and have high bandwidth, and we give near-optimal algorithms for any commutative combining operator (such as vector addition). For the task of matrix multiplication (or other non-commutative combining operators), where the inputs are ordered, we present sharp results in the simple ``wheel'' network, where procesing nodes are arranged in a ring, and are all connected to a single cloud node.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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