The article by Flegar et al. titled “Adaptive Precision Block-Jacobi for High Performance Preconditioning in the Ginkgo Linear Algebra Software” presents a novel, practical implementation of an adaptive precision block-Jacobi preconditioner. Performance results using state-of-the-art GPU architectures for the block-Jacobi preconditioner generation and application demonstrate the practical usability of the method, compared to a traditional full-precision block-Jacobi preconditioner. A production-ready implementation is provided in the Ginkgo numerical linear algebra library. In this report, the Ginkgo library is reinstalled and performance results are generated to perform a comparison to the original results when using Ginkgo’s Conjugate Gradient solver with either the full or the adaptive precision block-Jacobi preconditioner for a suite of test problems on an NVIDIA GPU accelerator. After completing this process, the published results are deemed reproducible.

中文翻译：

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“Ginkgo 线性代数软件中高性能预处理的自适应精度 Block-Jacobi”的复制计算结果 (RCR) 报告

Flegar 等人的文章。题为“用于 Ginkgo 线性代数软件中高性能预处理的自适应精度块雅可比”提出了一种新颖的、实用的自适应精度块雅可比预处理器的实现。与传统的全精度块雅可比预条件器相比，使用最先进的 GPU 架构生成和应用块雅可比预条件器的性能结果证明了该方法的实际可用性。Ginkgo 数值线性代数库中提供了生产就绪的实现。在这份报告中，重新安装 Ginkgo 库并生成性能结果，以便在使用 Ginkgo 的共轭梯度求解器与完整或自适应精度块雅可比预条件器在 NVIDIA GPU 加速器上解决一组测试问题时与原始结果进行比较。完成此过程后，已发布的结果被认为是可重复的。