A Study on the Influence of Caching: Sequences of Dense Linear Algebra Kernels
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.
Financial support from the Deutsche Forschungsgemeinschaft (DFG) through grant GSC 111 and the Deutsche Telekom Stiftung is gratefully acknowledged.
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