Collaborative Research: Tuning Libraries to Effectively Exploit the Memory Hierarchy

我们建议使用三种不同的优化方法来提高运行在多级内存层次结构上的库代码的性能。 我们将针对分块和预处理方法来增加稀疏和结构化矩阵中的引用局部性。 接下来，我们采用缓存意识的数据结构，以进一步提高内存层次结构的利用率。 最后，我们执行代码和数据布局转换，有效地将库重新映射到底层内存硬件。 我们最初的目标应用程序包括各种Krylov迭代求解器。 虽然我们计划生产这些库代码的优化版本，但我们也将追求其他类的库。 我们的优化将满足有效地将代码迁移到新硬件平台的需求，并利用新平台上提供的新架构功能。

We propose to improve the performance of library codes as run on a multi-level memory hierarchy using three different approaches to optimization. We will target blocking and preconditioning methods to increase the locality of reference in sparse and structured matrices. Next we employ cache-conscious data structures to further improve memory hierarchy utilization. Finally, we perform code and data layout transformations that effectively remap library to the underlying memory hardware. Our initial target applications include a variety of Krylov iterative solvers. While we plan to produce optimized versions of these library codes, we will also pursue additional classes of libraries. Our optimizations will address the need to effectively migrate code to new hardware platforms and exploit the novel architecture features provided on the new platform.