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Dealing with asymmetry in the architecture opens a plethora of questions fromthe perspective of scheduling task-parallel applications, and there exist earlyattempts to address this problem via ad-hoc strategies embedded into a runtimeframework. In this paper we take a different path, which consists in addressingthe complexity of the problem at the library level, via a few asymmetry-awarefundamental kernels, hiding the architecture heterogeneity from the taskscheduler. For the specific domain of dense linear algebra, we show that thisis not only possible but delivers much higher performance than a naive approachbased on an asymmetry-oblivious scheduler. Furthermore, this solution alsooutperforms an ad-hoc asymmetry-aware scheduler furnished with sophisticatedscheduling techniques.

Revisiting Conventional Task Schedulers to Exploit Asymmetry in ARM big.LITTLE Architectures for Dense Linear Algebra