GPU集群上不同稀疏矩阵格式和互连的自调Krylov基计算

Langshi Chen, Serge G. Petition
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引用次数: 0

摘要

Krylov子空间方法在求解大规模稀疏线性问题中得到了广泛的应用。在GMRES等方法中,正交化过程将消耗大部分时间。由于基于现代多核架构的加速器为计算提供了巨大的动力,通信开销仍然是一个瓶颈,特别是在具有大量节点的集群中。筑波大学的HA-PACS/TCA是一个CPU-GPU混合集群,配备了不同的互连方式,用于gpu之间的通信。在HA-PACS/TCA上测试了一组具有不同稀疏矩阵和互连方式的Krylov基计算方法。结果表明,需要一种自动调优方案来处理各种类型的矩阵。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Toward Auto-tuned Krylov Basis Computation for Different Sparse Matrix Formats and Interconnects on GPU Clusters
Krylov subspace methods (KSMs) are widely used in solving large-scale sparse linear problems. The orthogonalization process in methods like GMRES would consume a majority of the time. Since modern manycore architecture based accelerators have provided great horsepowers for computations,communication overheads remain a bottleneck, especially in clusters with a great number of nodes. The HA-PACS/TCA of Tsukuba University is a CPU-GPU hybrid cluster equipped with different interconnects for communications among GPUs. We testa group of Krylov basis computation methods with different sparse matrices and interconnects on HA-PACS/TCA. Results show that an auto-tuning scheme is required to deal with various types of matrices.
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