  Dealing with asymmetry in the architecture opens a plethora of questions from
the perspective of scheduling task-parallel applications, and there exist early
attempts to address this problem via ad-hoc strategies embedded into a runtime
framework. In this paper we take a different path, which consists in addressing
the complexity of the problem at the library level, via a few asymmetry-aware
fundamental kernels, hiding the architecture heterogeneity from the task
scheduler. For the specific domain of dense linear algebra, we show that this
is not only possible but delivers much higher performance than a naive approach
based on an asymmetry-oblivious scheduler. Furthermore, this solution also
outperforms an ad-hoc asymmetry-aware scheduler furnished with sophisticated
scheduling techniques.
