论文标题
性能分析和最佳节点感知通信，用于扩大共轭梯度方法
Performance Analysis and Optimal Node-Aware Communication for Enlarged Conjugate Gradient Methods
论文作者
论文摘要
Krylov方法是解决大型稀疏线性方程式系统的关键方法，但在分布式存储器上却遭受了较差的较差的骨骼。这是由于大量集体通信呼叫以及低计算工作量的高同步成本。扩大的Krylov方法通过减少收敛的总迭代来解决此问题，这是分裂初始残留物并导致块向量操作的工件。在本文中，我们介绍了扩大的Krylov方法，增大的共轭梯度（ECG）的性能研究，并指出了块向量对大规模平行性能的影响。最值得注意的是，由于稀疏矩阵矢量乘法内核中的密集消息，我们观察到点对点通信的开销增加。此外，我们提出了分析ECG的预期性能以及激励设计决策的模型。最重要的是，我们基于节点感知通信技术引入了一种新的点对点通信方法，该方法提高了该方法的大规模效率。
Krylov methods are a key way of solving large sparse linear systems of equations, but suffer from poor strong scalabilty on distributed memory machines. This is due to high synchronization costs from large numbers of collective communication calls alongside a low computational workload. Enlarged Krylov methods address this issue by decreasing the total iterations to convergence, an artifact of splitting the initial residual and resulting in operations on block vectors. In this paper, we present a performance study of an Enlarged Krylov Method, Enlarged Conjugate Gradients (ECG), noting the impact of block vectors on parallel performance at scale. Most notably, we observe the increased overhead of point-to-point communication as a result of denser messages in the sparse matrix-block vector multiplication kernel. Additionally, we present models to analyze expected performance of ECG, as well as, motivate design decisions. Most importantly, we introduce a new point-to-point communication approach based on node-aware communication techniques that increases efficiency of the method at scale.
