The efficiency of matrix–vector multiplication is of considerable importance. No current approaches can optimize this sufficiently well under severe time constraints. All major existing methods are based on either manual-tuning or auto-tuning and can therefore be time-consuming. We introduce an alternative model-driven approach, which is used to map the implementation of matrix–vector multiplication to a target architecture and analytically obtain its parameters. The approach yields the performance that is competitive with optimized Basic Linear Algebra Subprograms (BLAS)-like dense linear algebra libraries without the need for manual-tuning or auto-tuning. Our method provides competitive performance across hardware architectures and can be utilized to obtain single-threaded and multi-threaded implementations on multicore processors. We expect that this approach allows the community to progress from valuable engineering solutions to techniques with a broader application.

中文翻译：

---

多核处理器上矩阵向量乘法的分析建模

矩阵向量乘法的效率非常重要。在严格的时间限制下，目前没有任何方法可以充分优化这一点。所有主要的现有方法都基于手动调整或自动调整，因此可能非常耗时。我们介绍了一种替代的模型驱动方法，用于将矩阵向量乘法的实现映射到目标架构并分析获取其参数。该方法产生的性能可与优化的基本线性代数子程序 (BLAS) 类似的密集线性代数库相媲美，而无需手动调整或自动调整。我们的方法提供了跨硬件架构的竞争性能，并可用于在多核处理器上获得单线程和多线程实现。