分布式SpMM算法学术论文片段
文本内容
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
整体描述
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。
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