分布式SpMM算法学术论文片段

4.1 Distributed SpMM Algorithm
We present a distributed algorithm for SpMM using an arrow matrix decomposition. In Section 6, we analyze the data movement and storage requirements of this algorithm in the α−β model. In particular, it improves bandwidth cost and storage requirements by a factor of Θ(√p) at a similar latency cost compared to a fully replicated 1.5D decomposition.
We present a distributed algorithm for SpMM using an arrow matrix decomposition. In Section 6, we analyze the data movement and storage requirements of this algorithm in the α−β model. This method notably improves bandwidth cost and storage efficiency by a factor of Θ(√p), while maintaining comparable latency costs to a fully replicated 1.5D decomposition.

Algorithm 1: Arrow Matrix Multiply

这是一张计算机领域学术论文的屏幕截图，展示了4.1章节「Distributed SpMM Algorithm」的内容。图中介绍了一种基于箭头矩阵分解的分布式稀疏矩阵乘法（SpMM）算法，提及将在第6章中在α−β模型下分析该算法的数据移动和存储需求，核心亮点是相比完全复制的1.5D分解，在延迟成本相近的情况下，能将带宽成本和存储需求优化Θ(√p)倍，并且重复了一段核心介绍内容，下方还有「Algorithm 1: Arrow Matrix Multiply」的算法标题，右下角带有知乎用户@xliudy的水印。

该图片是学术论文的屏幕截图，右下角的水印显示内容来自知乎用户@xliudy，推测是该用户在知乎平台分享的计算机科学领域关于分布式算法、稀疏矩阵乘法的学术研究论文片段，这类论文通常发表于计算机相关的学术会议或期刊。