A hierarchical approach for autotuning linear algebra routines on heterogeneous platforms is presented. Hierarchy helps to alleviate the difficulties of tuning parallel routines for high-performance computing systems. This paper analyzes the application of the hierarchical approach at both the hardware and software levels, using the basic matrix multiplication and the Strassen multiplication as proof of concept on multicore+coprocessor nodes. In this way, the hierarchical approach allows partial delegation of the efficient exploitation of the computing units in the node to the underlying direct autotuned matrix multiplication used in the base case.

中文翻译：

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在异构集群中并行例程的自动调整方法中集成软件和硬件层次结构

提出了一种在异构平台上自动调整线性代数例程的分层方法。层次结构有助于减轻为高性能计算系统调整并行例程的困难。本文分析了分层方法在硬件和软件层面的应用，使用基本矩阵乘法和施特拉森乘法作为多核 + 协处理器节点上的概念证明。以这种方式，分层方法允许将节点中计算单元的有效利用部分委托给基本情况下使用的底层直接自动调整矩阵乘法。