  Dense linear algebra libraries, such as BLAS and LAPACK, provide a relevant
collection of numerical tools for many scientific and engineering applications.
While there exist high performance implementations of the BLAS (and LAPACK)
functionality for many current multi-threaded architectures,the adaption of
these libraries for asymmetric multicore processors (AMPs)is still pending. In
this paper we address this challenge by developing an asymmetry-aware
implementation of the BLAS, based on the BLIS framework, and tailored for AMPs
equipped with two types of cores: fast/power hungry versus slow/energy
efficient. For this purpose, we integrate coarse-grain and fine-grain
parallelization strategies into the library routines which, respectively,
dynamically distribute the workload between the two core types and statically
repartition this work among the cores of the same type.
  Our results on an ARM big.LITTLE processor embedded in the Exynos 5422 SoC,
using the asymmetry-aware version of the BLAS and a plain migration of the
legacy version of LAPACK, experimentally assess the benefits, limitations, and
potential of this approach.
