It is universally known that caching is critical to attain high- performance
implementations: In many situations, data locality (in space and time) plays a
bigger role than optimizing the (number of) arithmetic floating point
operations. In this paper, we show evidence that at least for linear algebra
algorithms, caching is also a crucial factor for accurate performance modeling
and performance prediction.Comment: Submitted to the Ninth International Workshop on Automatic
Performance Tuning (iWAPT2014
