We detail the performance optimizations made in rocHPL, AMD's open-source implementation of the High-Performance Linpack (HPL) benchmark targeting accelerated node architectures designed for exascale systems such as the Frontier supercomputer. The implementation leverages the high-throughput GPU accelerators on the node via highly optimized linear algebra libraries, as well as the entire CPU socket to perform latency-sensitive factorization phases. We detail novel performance improvements such as a multi-threaded approach to computing the panel factorization phase on the CPU, time-sharing of CPU cores between processes on the node, as well as several optimizations which hide MPI communication. We present some performance results of this implementation of the HPL benchmark on a single node of the Frontier early access cluster at Oak Ridge National Laboratory, as well as scaling to multiple nodes.

翻译：本文详细介绍了rocHPL（AMD开源的面向百亿亿次系统如Frontier超级计算机加速节点架构的高性能Linpack基准测试实现）的性能优化方法。该实现通过高度优化的线性代数库利用节点上的高吞吐量GPU加速器，同时借助整个CPU插槽执行延迟敏感的分解阶段。我们阐述了多项新颖的性能改进技术，包括在CPU上采用多线程方法计算面板分解阶段、节点内进程间CPU核心的分时复用，以及隐藏MPI通信的多项优化。文中展示了该HPL基准测试实现方法在橡树岭国家实验室Frontier早期接入集群单节点上的性能结果，以及向多节点扩展的测试数据。